using scientific method in experiments

Scientific Method Experiments for Kids: Easy & Fun Setup

Krystal DeVille

December 25, 2022

The Scientific Method is a method and process used to conduct research. It is an easy way to provide a road map that kids can use to quench their hunger for curiosity.

They are constantly trying to figure out how the world works around them, and by providing them with the Scientific Method as an outline, they can navigate their way to success.

It can help students develop traditional cognitive skills, including logic, rationalization, and problem-solving through the application of the Scientific Method.

Table of Contents

What is the Scientific Method

It may seem boring to experiment with the Scientific Method, but it provides kids with the tools needed to solve any problem or question!

It doesn’t necessarily mean filling out a worksheet as done in school or, in many cases, even writing anything down. The Scientific Method for kids is a proven method and process of research. It works as a step-by-step guideline to teach people, including kids, how to experiment properly and come to an evidence-based solution.

It is how people study and learn things! If you think your child can study and learn things, then they can most certainly use the scientific method.

Steps of the Scientific Method

• Ask a Question (Channel your inner curiosity and pose a question)
• Make a Hypothesis (Create a prediction: What do they think will happen?)
• Research (Gather information and observe what it is you are really studying)
• Experiment (This step may need to be repeated, A LOT. Keep on trying!)
• Make Observations (Yup, more observations. Examine what is happening.)
• Draw Conclusions (What happened compared to what you thought would happen? What would happen if you change a variable? This could be the part where you revisit step 4 and try all over again)
• Share and Discuss Results (Share them with everyone! The more you collaborate, the better your study will be)

Scientific Method Experiments for Preschoolers

For preschoolers, we will use a more toned-down version of the scientific method to not inundate them with a long, scary list.

• Ask a Question
• Ask them what they think will happen ( Hypothesis )
• Test it out ( Experiment )
• What actually happened? ( Conclusions )

Scientific Experiments:

The Fastest Fizz

• Sink or Swim

Blow the Biggest Bubble

Materials Needed:

• Two identical glasses or jars (Any type of container for the water)
• Ice cubes to fill the glass up to halfway
• Water (Cold and hot tap water will do)
• Two Alka-Seltzer Tablets
• Thermometer (Optional: To measure the temperature of the water before dropping the Alka-Seltzer)

Scientific Method Steps :

• Ask a Question: How does the temperature of water affect the fizzing of Alka-Seltzer?
• Hypothesis : Will the Alka-Seltzer fizz faster in warm or cold water?
• Experiment:

Step 1: Fill one glass halfway with ice cubes

Step 2: Add cold water to that glass up to an inch from the rim

Step 3: Stir the ice cubes with the spoon to even out the temperature

Step 4: Remove the ice cubes with the spoon right before you will drop the Alka-Seltzer in the glasses

Step 5: Add hot tap water up to an inch from the rim in the second glass

Step 6: Drop the Alka-Seltzer into the glass with warm water and time how long it takes for the tablet to disappear

Step 7: Drop the Alka-Seltzer into the glass with cold water and time how long it takes for the tablet to disappear

• Conclusion: Which glass made the tablet disappear faster? Why might that be? What else dissolves in water?

If you like to find more activities you can do at home, please check out our article, Baking Soda and Vinegar Experiments .

Sink or Swim?

• Big bowl (Any large container that holds water)
• Recommended items: Twig

Scientific Method Steps:

• Ask a Question: Which types of materials will float or sink in the water?
• Hypothesis : Which items do they think will float or sink?

Step 1: Have them explore the backyard or a playground for items

Step 2: Encourage them to retrieve a variety of items including a leaf, a pebble, and/or twigs

Step 3: Place each item separately in the container of water

Step 4: Examine what happens!

• Conclusion: Which items floated and which didn’t? Why might that be!?

• At least 3 different varieties
• I suggest Bubblicious, Bubbaloo, Bazooka, or even a balloon.
• Ask a Question: What type of gum blows the biggest bubbles?
• Hypothesis : Which type of gum do they think will be bigger?

Step 1: Start with one brand of bubblegum

Step 2: Each person chews the gum for 5 minutes

Step 3: Begin blowing bubbles with your piece of gum

Step 4: Blow 5 bubbles with that brand of gum

Step 5: Measure each bubble with the ruler and write down the results

Step 6: Repeat steps 1-5 using a different brand of gum

• Conclusion: Which gum blew the biggest bubble?

Kids love Microscopes as they can explore the unseen world around them. Please take a look at our article for more information, Microscope Activities for Kids – Fun Experiments Kids Will Love!

Scientific Method Experiments for Elementary-Aged Kids

Elephant toothpaste.

• Empty Plastic Bottle
• A wide washable surface area (A large tub or tray would work well – preferable if you are in the kitchen, bathroom, or outdoors; it will get messy)
• 3% Hydrogen Peroxide (It could be a higher percentage for a bigger reaction)
• Measuring Spoons (At least 2)
• Measuring Cups (At least 2)
• Safety goggles
• Liquid food coloring (Optional)
• Question: What happens when you mix yeast with hydrogen peroxide? (This may need some background information) After the first experiment: engage their curiosity by leading them to questions like: How would the experiment change if you add more yeast?
• Hypothesis : What do they think will happen? What might happen if you add more or less yeast?
• Research: Look into the chemical breakdown of what Hydrogen Peroxide is (H₂O₂) and how that can be broken down. If mixed with a catalyst, such as yeast, then the breakdown reaction can occur very quickly!
• Experiment :

Step 1: Put on your safety goggles (It may be called elephant toothpaste but don’t put it in your mouth!)

Step 2: Measure ½ cup of hydrogen peroxide and carefully pour it into the bottle.

Step 3: Add food coloring at this point if you opt to use that.

Step 4: In a separate measuring cup, mix one tablespoon of yeast and three tablespoons of warm water.

Step 5: Mix the yeast and water for about 30 seconds.

Step 6: Pour the yeast mixture into the bottle of peroxide and watch what happens.

• Observations: What happened? How fast did it happen? How much of a reaction did you get?
• Conclusions : What happens when you add yeast to hydrogen peroxide? What would happen if you added more or less yeast?
• Share and Discuss Results! Feel free to try again using different variables!

Rubber Egg Experiment

• Household Vinegar
• Glass/Jar/Container
• Food coloring (Optional)
• Plate or tray
• Question: Can you bounce an egg?
• Hypothesis: What do they think will happen? Will an egg really be able to bounce? How high will it be able to bounce?
• Research: An eggshell is made of calcium carbonate and vinegar is an acid. When the two are combined, a chemical reaction occurs that once again mixes to form a carbon dioxide gas. The vinegar can cross with the selectively permeable membrane of the eggshell through osmosis. Then, the vinegar proceeds to thicken the outer layer of the remaining eggshell to make it “bouncy.”

Step 1: Carefully place an egg in the glass cup or jar.

Step 2: Pour white vinegar in the glass until the egg is completely submerged (At this point you can add the food coloring if you’d like).

Step 3: Leave the egg in the glass for at least 48 hours.

Step 4: After 48 hours, or when the egg is translucent, you can remove it from the glass and run it through water.

Step 5: Gently rub off the exterior of the egg until it is completely translucent (Or the white part is gone).

Step 6: Examine the egg and begin bouncing, lightly at first and then test how far it can go (Have a plate or tray handy as this part could get messy).

• Observations: What happened? You should also encourage them to observe the changes during the 48-hour transition process. What did it look like? What happened to the size of the egg? How did the egg feel? How high can it bounce without breaking apart?
• Conclusions : What happens when you cover an egg in vinegar? How did that happen? What other liquids will have a similar reaction?

For more experiments with only a few simple items, check out our article, Easy Experiments in Fluids .

Air Cannon Experiment

Materials Needed: (This should be done with an older elementary-aged child or make sure they are mature enough to do this)

• Plastic Bottle
• Rubber Band
• Pieces of paper/plastic objects to knock over. (Dominoes, paper towel roll, plastic cups).
• Stickers (Optional)
• Question: Can you control air? Why does air move? How powerful can air be?
• Hypothesis : What do they think will happen? How strong can you make air when it is controlled? What objects can be pushed over by air?
• Research: Air is a gas making it difficult to contain or hold its shape. Air pressure and density can be felt by temperature changes or changes in elevation. Even tiny air molecules have weight, and when billions of those molecules come together, they weigh down on other objects. Look into the changes of air pressures at different levels of elevation even into the atmosphere.

Step 1: Cut the bottom of the end of the plastic bottle.

Step 2: Attach the balloon to the open part that you cut off. (If you need to, you can cut the end of the balloon to make it fit).

Step 3: Attach a rubber band to the outside of the balloon to make sure it is securely attached.

Step 4: Decorate the bottle with stickers to make it look cool! (Optional step of course)

Step 5: Stretch the end of the balloon back and release.

Step 6: See how many controlled objects you can knock down. (Again, think dominoes, paper towel rolls, plastic cups, etc.).

• Observations: What happened? How does this happen? How many objects were successfully knocked down? Which objects stood strong? Why might that be?
• Conclusions : How can air be controlled? What other objects can you knock down? What is needed to make the air vortex stronger and more powerful?

While it’s always fun to build an air cannon, I understand this can be complex, for younger kids. You do have the option to buy one to play around with. You can find the current price of one right here .

Oil vs. Water

• A glass cup/jar
• Vegetable Oil
• Food Coloring
• Measuring Cup
• Toothpick (Optional)
• Question: What happens when you mix oil and water?
• Hypothesis : What do they think will happen? Will they mix? How will they mix? What will happen when you add in food coloring?
• Research: Researching density will make this experiment more valuable to your kids. Water molecules are polarized and more dense than oil. Oil is made up of non-polar molecules and is a lot less dense. Polar molecules only dissolve in polar solvents while non-polar molecules only dissolve in non-polar solvents making them not compatible, therefore, not allowing them to mix.

Step 1: Pour ½ cup of oil into a glass cup.

Step 2: Pour ½ cup of water into the glass cup.

Step 3: Allow it to settle and watch what happens.

Step 4: Add a drop or two of food coloring.

Step 5: Allow it to settle and watch what happens. (If the food coloring drop needs a little bit of a push, you can do so with the toothpick)

Step 6: Examine the glass and see what happens.

• Observations: What happened? Why didn’t they mix? What happened to the food coloring?
• Conclusions : What happens when oil and water are in the same container? Why does that happen? What happens if you add different food coloring to the container? Why doesn’t the food coloring mix with the oil? What does food coloring mix with? What else doesn’t oil mix with?

Magic Inflating Balloon

• Water bottle (Soda bottle will work just fine too)
• Baking Soda
• Funnel (Two preferably: If you have one, then make sure you wash it completely before using it again)
• Question: Can you fill a balloon without air?
• Hypothesis : What do they think will happen? Will a balloon fill with baking soda and vinegar?
• Research: You need carbon dioxide gas to fill a balloon. When baking soda (a base) and vinegar (an acid) are combined, they create carbon dioxide gas. Since the gas doesn’t have a shape, it will quickly expand, filling in all of the space that it can, resulting in an inflated balloon.

Step 1: Put ⅓ of a cup of vinegar into a water bottle.

Step 2: Attach the other funnel (or the washed funnel) to the open end of the balloon.

Step 3: Place two teaspoons of baking soda into the balloon then remove the funnel from the balloon.

Step 4: Attach the open end of the balloon to the top of the water bottle. (Try not to let the baking soda mix with the vinegar when attaching)

Step 5: When the balloon is securely attached, lift the top of it so the baking soda does drop into the water bottle.

Step 6: Watch the balloon magically inflate!

• Observations: What happened? How fast did it happen? How did that happen? How long until it deflates?
• Conclusions : What happens when you mix a base and an acid? Will another acid work just as well? Is the size of the balloon dependent on the amount of acid?

Is soda really that bad for your teeth?

• Baby teeth (This one may take a little while to get, depending on their age. Egg shells work similarly, but the curiosity may not fully be there for them. Just make sure you hard-boil them).
• Different types of soda (At least 3 – can include a sugary drink such as Gatorade too)
• Multiple glass jars (Enough for each separate
• Question: How bad is soda for your teeth?
• Hypothesis : What do they think will happen? How will the soda/drink affect the teeth?
• Research: Research the acidity of different drinks and the effect they have on tooth enamel. Erosion will be the biggest effect as it occurs when the acid and carbonation of the soda touches the outer layer of the tooth, or the tooth enamel.

Step 1: Pour each soda or drink into its own separate glass jar up to ¼ of the way full (Up to ½ if you are using an egg).

Step 2: Place a tooth in each of the glass jars.

Step 4: Take out the tooth every day and record the changes that you’ve observed.

Step 5: After five days, take them out for good and record your final observations.

• Observations: What happened? What do they feel like? What do they look like?
• Conclusions : How did different types of soda affect the teeth? What will happen if you brush that tooth? How much do you have to brush to clean it?

Magic Cloud

• Water Bottle (The bigger, the better for this experiment, get a 2L bottle at minimum)
• Rubbing Alcohol
• A tablespoon
• A cork or a rubber stopper (Clay will work well too)
• Ball pump or Bicycle tire pump
• Safety Goggles
• Question: What is a cloud made of? How does a cloud form?
• Hypothesis : What do they think will happen? Do they think they’ll be able to simulate a cloud? How can you create a cloud?
• Research: Look into the changes in the atmosphere and how clouds emerge. The pump simulates the mass junction of air molecules to create clouds. Take this as an opportunity to research different types of clouds too.

Step 1: Pour 1 tablespoon of rubbing alcohol into the bottle.

Step 2: Shake the bottle around to ensure the rubbing alcohol touches all parts of the bottle.

Step 3: Use the pushpin to poke a hole in the middle of the bottle cap. (You may have to move it around a bit to make sure it is big enough for the bicycle pump)

Step 4: Add the cork or rubber stopper to the inside of the bottle cap.

Step 5: Put on your safety goggles. Slide the needle of the pump into the hole and start pumping. (The more you pump, the bigger the cloud).

Step 6: Keep your safety goggles on. Hold the bottle firmly and open the cap to release the pressure. (Be aware that a loud noise will occur here, so be prepared).

• Observations: What happened? How much of a reaction did you get?
• Conclusions : What would happen if you were to close the cap again in the middle of releasing the cloud? What would happen if you added more rubbing alcohol? How big would the cloud be if pumped more air into it?

There are a few ways you can do this, and I go into more in detail in this article .

Wrapping Up

A lot of these experiments are great but do require a lot of preparation and guidance from the parents/adults. If you really want to engage their curiosity, put every question they have to the test.

Do double-stuffed Oreos actually have twice the amount of stuffing? Test it. Are footlong hoagies really 1 foot long? Measure it. Can you revive a dead marker? Try it. Why do volcanoes explode? Research it. There is no need to keep them wondering, and it’ll pique their curiosity for future experiences.

The Scientific Method gives them the appropriate avenue to test some of these ideas, even if it is just verbally referencing them. You might learn some pretty cool things on the way too. Have fun experimenting!

Do your kids like to get messy? You can find messy STEM projects in our article, Messy STEM Science Experiments for Kids!

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Scientific Method For Kids With Examples

Kids have questions about the world around them every day, and there is so much to learn through experimentation with simple materials. You can begin using the scientific method with elementary kids. Below we’ll share with you how and when to introduce the scientific method, the steps of the scientific method, and some easy scientific method experiments. There are so many great ways to enjoy science projects with kids!

What Is The Scientific Method?

The scientific method is a process or method of research. A problem is identified, information about the problem is gathered, a hypothesis or question is formulated from the information, and the hypothesis is put to the test with an experiment to prove or disprove its validity.

Sounds heavy… What in the world does that mean?!? It means you don’t need to try and solve the world’s biggest science questions! The scientific method is all about studying and learning things right around you.

As children develop practices that involve creating, gathering data evaluating, analyzing, and communicating, they can apply these critical thinking skills to any situation.

Note: The use of the best Science and Engineering Practices is also relevant to the topic of using the scientific method. Read more here and see if it fits your science planning needs.

Can Young Kids Use the Scientific Method?

Kids are great scientists at any age, and can use the scientific method in context to what they are learning. It can be adapted for any age!

The scientific method is a valuable tool for introducing kids to a logical way to solve scientific problems. Scientists use the scientific method to study, learn, and come up with an answer!

The scientific method is a process that helps double-check that answers are correct and the correct results are obtained through careful planning. Sometimes the guesses and questions change as you run your experiments.

Kids can use the scientific method too on questions that are relevant to them!

Let’s break the scientific method for kids down into six parts, and you can quickly see how each can be incorporated into your next science experiment.

What Are The Steps In The Scientific Method?

• Make initial observations.
• Come up with a question of interest that is based on the observations.
• Develop a hypothesis or prediction to go along with the question.
• Experiment and test.
• Gather and record results of tests and experiments and draw conclusions.
• Share and discuss results.

Whoa… Wait A  Minute! That sounds like a lot for a young kid!

You are correct. Depending on your kid’s abilities, following all the scientific method steps precisely will not go well. Someone will get frustrated, bored, and turned off by just how cool science can be. We do not want that to happen!

Using The Scientific Method For Preschool and Kindergarten

Use the scientific method steps as a guideline in the back of your mind. You can cover most of the steps by talking with your kids about…

• What do they think will happen?
• What is happening ?
• What happened compared to what they thought would happen ?

No writing is required! It’s also best to pick pretty straightforward ideas that aren’t overly involved or complicated to set up and test. Kids always have burning questions and “what ifs.”

See if you can tackle their next “what if” using the scientific method by listening carefully to their conversations. You can even have them keep a journal with their “what if” questions for your next science time.

Learn more about Science Activities For Preschoolers and Kindergarten Science Experiments .

Now on to how to apply the scientific method for elementary kiddos and beyond.

Scientific Method Steps In Action

Learn more about the steps of the scientific method below, which are great for science at home with your kids or in the classroom! We have also included some simple scientific method experiments for you to enjoy.

Ice Science Experiments are perfect for this! Try these 3 today !

STEP 1: Make Observations

Tons of everyday activities would make for cool science experiments using the scientific method. Listen to what your kids talk about and see happening. My son noticed that ice melted pretty fast in his water.

Observation is simply noticing what’s happening through our senses or with tools like a magnifying glass. Observation is used to collect and record data, enabling scientists to construct and test hypotheses and theories.

Learn more about observations in science.

STEP 2: Come Up With A Question 

Your kids’ observations should lead to some sort of question. For my son and his ice observations, he came up with questions. Does ice melt faster in different liquids? His curiosity about what happens to the ice in liquids is a simple science experiment perfect for using the scientific method.

Next! Do some research and come up with ideas!

STEP 3: Develop A Prediction or Hypothesis

You have made your observations, you have your question, and now you need to make a prediction about what you think will happen.

A prediction is a guess at what might happen in an experiment based on observation or other information.

A hypothesis is not simply a guess! It’s a statement of what you believe will happen based on the information you have gathered.

My son hypothesizes that ice will melt faster in juice than in water.

STEP 4: Conduct An Experiment

We made a prediction that ice will melt faster in juice than it will in water, and now we have to test our hypothesis. We set up an experiment with a glass of juice, a glass of water, and an ice cube for each.

For the best experiments, only one thing should change! All the things that can be changed in a science experiment are called variables. There are three types of variables; independent, dependent, and controlled.

The independent variable is the one that is changed in the experiment and will affect the dependent variable. Here we will use different types of liquids to melt our ice cube in.

The dependent variable is the factor that is observed or measured in the experiment. This will be the melting of the ice cubes. Set up a stopwatch or set a time limit to observe the changes!

The controlled variable stays constant in the experiment. The liquids should be roughly the same temperature (as close as possible) for our ice melting experiment and measured to the same amount. So we left them out to come to room temperature. They could also be tested right out of the fridge!

You can find simple science experiments here with dependent and controlled variables.

STEP 5: Record Results and Draw Conclusions

Make sure to record what is happening as well as the results—note changes at specific time intervals or after one set time interval.

For example…

• Record when each ice cube is completely melted.
• Add drawings if you wish of the setup up and the end results.
• Was your prediction accurate? If it was inaccurate, record why.
• Write out a final conclusion to your experiment.

STEP 6: Communicate Your Results

This is the opportunity to talk about your hypothesis, experiment, results, and conclusion!

ALTERNATIVE IDEAS: Switch out an ice cube for a lollipop or change the liquids using vinegar and cooking oil.

Now you have gone through the steps of the scientific method, read on for more fun scientific method experiments to try!

Free printable scientific method worksheets!

Fun Scientific Method Experiments

Sink or float experiment.

A Sink or Float experiment is great for practicing the steps of the scientific method with younger kids.

Grab this FREE printable sink or float experiment

Here are a few of our favorite scientific method experiments, which are great for elementary-age kids . Of course, you can find tons more awesome and doable science projects for kids here!

Magic Milk Experiment

Start with demonstrating this delightful magic milk experiment. Then get kids to apply the steps of the scientific method by coming up with a question to investigate. What happens when you change the type of milk used?

What Dissolves In Water

Investigate  what solids dissolve in water  and what do not. Here’s a super fun science experiment for kids that’s very easy to set up! Learn about solutions, solutes, and solvents through experimenting with water and common kitchen ingredients.

Apple Browning Experiment

Investigate how to keep apples from turning brown with this apple oxidation experiment . What can you add to cut apples to stop or slow the oxidation process?

Freezing Water Experiment

Will it freeze? What happens to the freezing point of water when you add salt?

Viscosity Experiment

Learn about the viscosity of fluids with a simple  viscosity experiment . Grab some marbles and add them to different household liquids to find out which one will fall to the bottom first. 

Seed Germination Experiment

Set up a simple seed germination experiment .

Catapult Experiment

Make a simple popsicle stick catapult and use one of our experiment ideas to investigate from rubber band tension to changes in launch angle and more. How far can you fling your objects? Take measurements and find out.

Floating Orange

Investigate whether an orange floats or sinks in water, and what happens if you use different types of oranges. Learn about buoyancy and density with a simple ingredient from the kitchen, an orange.

Bread Mold Experiment

Grow mold on bread for science, and investigate how factors such as moisture, temperature, and air affect mold growth. 

Eggshell Strength Experiment

Test how strong an egg is with this eggshell strength experiment . Grab some eggs, and find out how much weight an egg can support.

Free Printable Science Fair Starter Guide

Are you looking to plan a science fair project, make a science fair board, or want an easy guide to set up science experiments?

Learn more about prepping for a science fair and grab this free printable science fair project pack here!

If you want a variety of science fair experiments with instructions, make sure to pick up a copy of our Science Project Pack in the shop.

Bonus STEM Projects For Kids

STEM activities include science, technology, engineering, and mathematics. As well as our kids science experiments, we have lots of fun STEM activities for you to try. Check out these STEM ideas below…

• Building Activities
• Engineering Projects For Kids
• What Is Engineering For Kids?
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• STEM Worksheets
• Top 10 STEM Challenges For Kids

Printable Science Projects Pack

If you’re looking to grab all of our printable science projects in one convenient place plus exclusive worksheets and bonuses like a STEAM Project pack, our Science Project Pack is what you need! Over 300+ Pages!

• 90+ classic science activities  with journal pages, supply lists, set up and process, and science information.  NEW! Activity-specific observation pages!
• Best science practices posters  and our original science method process folders for extra alternatives!
• Be a Collector activities pack  introduces kids to the world of making collections through the eyes of a scientist. What will they collect first?
• Know the Words Science vocabulary pack  includes flashcards, crosswords, and word searches that illuminate keywords in the experiments!
• My science journal writing prompts  explore what it means to be a scientist!!
• Bonus STEAM Project Pack:  Art meets science with doable projects!
• Bonus Quick Grab Packs for Biology, Earth Science, Chemistry, and Physics

19 Comments

A great post and sure to help extend children’s thinking! I would like to download the 6 steps but the blue download button doesn’t seem to be working for me.

Thank you! All fixed. You should be able to download now!

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The 6 Scientific Method Steps and How to Use Them

General Education

When you’re faced with a scientific problem, solving it can seem like an impossible prospect. There are so many possible explanations for everything we see and experience—how can you possibly make sense of them all? Science has a simple answer: the scientific method.

The scientific method is a method of asking and answering questions about the world. These guiding principles give scientists a model to work through when trying to understand the world, but where did that model come from, and how does it work?

In this article, we’ll define the scientific method, discuss its long history, and cover each of the scientific method steps in detail.

What Is the Scientific Method?

At its most basic, the scientific method is a procedure for conducting scientific experiments. It’s a set model that scientists in a variety of fields can follow, going from initial observation to conclusion in a loose but concrete format.

The number of steps varies, but the process begins with an observation, progresses through an experiment, and concludes with analysis and sharing data. One of the most important pieces to the scientific method is skepticism —the goal is to find truth, not to confirm a particular thought. That requires reevaluation and repeated experimentation, as well as examining your thinking through rigorous study.

There are in fact multiple scientific methods, as the basic structure can be easily modified.  The one we typically learn about in school is the basic method, based in logic and problem solving, typically used in “hard” science fields like biology, chemistry, and physics. It may vary in other fields, such as psychology, but the basic premise of making observations, testing, and continuing to improve a theory from the results remain the same.

The History of the Scientific Method

The scientific method as we know it today is based on thousands of years of scientific study. Its development goes all the way back to ancient Mesopotamia, Greece, and India.

The Ancient World

In ancient Greece, Aristotle devised an inductive-deductive process , which weighs broad generalizations from data against conclusions reached by narrowing down possibilities from a general statement. However, he favored deductive reasoning, as it identifies causes, which he saw as more important.

Aristotle wrote a great deal about logic and many of his ideas about reasoning echo those found in the modern scientific method, such as ignoring circular evidence and limiting the number of middle terms between the beginning of an experiment and the end. Though his model isn’t the one that we use today, the reliance on logic and thorough testing are still key parts of science today.

The Middle Ages

The next big step toward the development of the modern scientific method came in the Middle Ages, particularly in the Islamic world. Ibn al-Haytham, a physicist from what we now know as Iraq, developed a method of testing, observing, and deducing for his research on vision. al-Haytham was critical of Aristotle’s lack of inductive reasoning, which played an important role in his own research.

Other scientists, including Abū Rayhān al-Bīrūnī, Ibn Sina, and Robert Grosseteste also developed models of scientific reasoning to test their own theories. Though they frequently disagreed with one another and Aristotle, those disagreements and refinements of their methods led to the scientific method we have today.

Following those major developments, particularly Grosseteste’s work, Roger Bacon developed his own cycle of observation (seeing that something occurs), hypothesis (making a guess about why that thing occurs), experimentation (testing that the thing occurs), and verification (an outside person ensuring that the result of the experiment is consistent).

After joining the Franciscan Order, Bacon was granted a special commission to write about science; typically, Friars were not allowed to write books or pamphlets. With this commission, Bacon outlined important tenets of the scientific method, including causes of error, methods of knowledge, and the differences between speculative and experimental science. He also used his own principles to investigate the causes of a rainbow, demonstrating the method’s effectiveness.

Scientific Revolution

Throughout the Renaissance, more great thinkers became involved in devising a thorough, rigorous method of scientific study. Francis Bacon brought inductive reasoning further into the method, whereas Descartes argued that the laws of the universe meant that deductive reasoning was sufficient. Galileo’s research was also inductive reasoning-heavy, as he believed that researchers could not account for every possible variable; therefore, repetition was necessary to eliminate faulty hypotheses and experiments.

All of this led to the birth of the Scientific Revolution , which took place during the sixteenth and seventeenth centuries. In 1660, a group of philosophers and physicians joined together to work on scientific advancement. After approval from England’s crown , the group became known as the Royal Society, which helped create a thriving scientific community and an early academic journal to help introduce rigorous study and peer review.

Previous generations of scientists had touched on the importance of induction and deduction, but Sir Isaac Newton proposed that both were equally important. This contribution helped establish the importance of multiple kinds of reasoning, leading to more rigorous study.

As science began to splinter into separate areas of study, it became necessary to define different methods for different fields. Karl Popper was a leader in this area—he established that science could be subject to error, sometimes intentionally. This was particularly tricky for “soft” sciences like psychology and social sciences, which require different methods. Popper’s theories furthered the divide between sciences like psychology and “hard” sciences like chemistry or physics.

Paul Feyerabend argued that Popper’s methods were too restrictive for certain fields, and followed a less restrictive method hinged on “anything goes,” as great scientists had made discoveries without the Scientific Method. Feyerabend suggested that throughout history scientists had adapted their methods as necessary, and that sometimes it would be necessary to break the rules. This approach suited social and behavioral scientists particularly well, leading to a more diverse range of models for scientists in multiple fields to use.

The Scientific Method Steps

Though different fields may have variations on the model, the basic scientific method is as follows:

#1: Make Observations 

Notice something, such as the air temperature during the winter, what happens when ice cream melts, or how your plants behave when you forget to water them.

#2: Ask a Question

Turn your observation into a question. Why is the temperature lower during the winter? Why does my ice cream melt? Why does my toast always fall butter-side down?

This step can also include doing some research. You may be able to find answers to these questions already, but you can still test them!

#3: Make a Hypothesis

A hypothesis is an educated guess of the answer to your question. Why does your toast always fall butter-side down? Maybe it’s because the butter makes that side of the bread heavier.

A good hypothesis leads to a prediction that you can test, phrased as an if/then statement. In this case, we can pick something like, “If toast is buttered, then it will hit the ground butter-first.”

#4: Experiment

Your experiment is designed to test whether your predication about what will happen is true. A good experiment will test one variable at a time —for example, we’re trying to test whether butter weighs down one side of toast, making it more likely to hit the ground first.

The unbuttered toast is our control variable. If we determine the chance that a slice of unbuttered toast, marked with a dot, will hit the ground on a particular side, we can compare those results to our buttered toast to see if there’s a correlation between the presence of butter and which way the toast falls.

If we decided not to toast the bread, that would be introducing a new question—whether or not toasting the bread has any impact on how it falls. Since that’s not part of our test, we’ll stick with determining whether the presence of butter has any impact on which side hits the ground first.

#5: Analyze Data

After our experiment, we discover that both buttered toast and unbuttered toast have a 50/50 chance of hitting the ground on the buttered or marked side when dropped from a consistent height, straight down. It looks like our hypothesis was incorrect—it’s not the butter that makes the toast hit the ground in a particular way, so it must be something else.

Since we didn’t get the desired result, it’s back to the drawing board. Our hypothesis wasn’t correct, so we’ll need to start fresh. Now that you think about it, your toast seems to hit the ground butter-first when it slides off your plate, not when you drop it from a consistent height. That can be the basis for your new experiment.

#6: Communicate Your Results

Good science needs verification. Your experiment should be replicable by other people, so you can put together a report about how you ran your experiment to see if other peoples’ findings are consistent with yours.

This may be useful for class or a science fair. Professional scientists may publish their findings in scientific journals, where other scientists can read and attempt their own versions of the same experiments. Being part of a scientific community helps your experiments be stronger because other people can see if there are flaws in your approach—such as if you tested with different kinds of bread, or sometimes used peanut butter instead of butter—that can lead you closer to a good answer.

A Scientific Method Example: Falling Toast

We’ve run through a quick recap of the scientific method steps, but let’s look a little deeper by trying again to figure out why toast so often falls butter side down.

#1: Make Observations

At the end of our last experiment, where we learned that butter doesn’t actually make toast more likely to hit the ground on that side, we remembered that the times when our toast hits the ground butter side first are usually when it’s falling off a plate.

The easiest question we can ask is, “Why is that?”

We can actually search this online and find a pretty detailed answer as to why this is true. But we’re budding scientists—we want to see it in action and verify it for ourselves! After all, good science should be replicable, and we have all the tools we need to test out what’s really going on.

Why do we think that buttered toast hits the ground butter-first? We know it’s not because it’s heavier, so we can strike that out. Maybe it’s because of the shape of our plate?

That’s something we can test. We’ll phrase our hypothesis as, “If my toast slides off my plate, then it will fall butter-side down.”

Just seeing that toast falls off a plate butter-side down isn’t enough for us. We want to know why, so we’re going to take things a step further—we’ll set up a slow-motion camera to capture what happens as the toast slides off the plate.

We’ll run the test ten times, each time tilting the same plate until the toast slides off. We’ll make note of each time the butter side lands first and see what’s happening on the video so we can see what’s going on.

When we review the footage, we’ll likely notice that the bread starts to flip when it slides off the edge, changing how it falls in a way that didn’t happen when we dropped it ourselves.

That answers our question, but it’s not the complete picture —how do other plates affect how often toast hits the ground butter-first? What if the toast is already butter-side down when it falls? These are things we can test in further experiments with new hypotheses!

Now that we have results, we can share them with others who can verify our results. As mentioned above, being part of the scientific community can lead to better results. If your results were wildly different from the established thinking about buttered toast, that might be cause for reevaluation. If they’re the same, they might lead others to make new discoveries about buttered toast. At the very least, you have a cool experiment you can share with your friends!

Key Scientific Method Tips

Though science can be complex, the benefit of the scientific method is that it gives you an easy-to-follow means of thinking about why and how things happen. To use it effectively, keep these things in mind!

Don’t Worry About Proving Your Hypothesis

One of the important things to remember about the scientific method is that it’s not necessarily meant to prove your hypothesis right. It’s great if you do manage to guess the reason for something right the first time, but the ultimate goal of an experiment is to find the true reason for your observation to occur, not to prove your hypothesis right.

Good science sometimes means that you’re wrong. That’s not a bad thing—a well-designed experiment with an unanticipated result can be just as revealing, if not more, than an experiment that confirms your hypothesis.

Be Prepared to Try Again

If the data from your experiment doesn’t match your hypothesis, that’s not a bad thing. You’ve eliminated one possible explanation, which brings you one step closer to discovering the truth.

The scientific method isn’t something you’re meant to do exactly once to prove a point. It’s meant to be repeated and adapted to bring you closer to a solution. Even if you can demonstrate truth in your hypothesis, a good scientist will run an experiment again to be sure that the results are replicable. You can even tweak a successful hypothesis to test another factor, such as if we redid our buttered toast experiment to find out whether different kinds of plates affect whether or not the toast falls butter-first. The more we test our hypothesis, the stronger it becomes!

What’s Next?

Want to learn more about the scientific method? These important high school science classes will no doubt cover it in a variety of different contexts.

Test your ability to follow the scientific method using these at-home science experiments for kids !

Need some proof that science is fun? Try making slime

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The Scientific Method by Science Made Simple

Understanding and using the scientific method.

The Scientific Method is a process used to design and perform experiments. It's important to minimize experimental errors and bias, and increase confidence in the accuracy of your results.

In the previous sections, we talked about how to pick a good topic and specific question to investigate. Now we will discuss how to carry out your investigation.

Steps of the Scientific Method

• Observation/Research
• Experimentation

Now that you have settled on the question you want to ask, it's time to use the Scientific Method to design an experiment to answer that question.

If your experiment isn't designed well, you may not get the correct answer. You may not even get any definitive answer at all!

The Scientific Method is a logical and rational order of steps by which scientists come to conclusions about the world around them. The Scientific Method helps to organize thoughts and procedures so that scientists can be confident in the answers they find.

OBSERVATION is first step, so that you know how you want to go about your research.

HYPOTHESIS is the answer you think you'll find.

PREDICTION is your specific belief about the scientific idea: If my hypothesis is true, then I predict we will discover this.

EXPERIMENT is the tool that you invent to answer the question, and

CONCLUSION is the answer that the experiment gives.

Don't worry, it isn't that complicated. Let's take a closer look at each one of these steps. Then you can understand the tools scientists use for their science experiments, and use them for your own.

OBSERVATION

This step could also be called "research." It is the first stage in understanding the problem.

After you decide on topic, and narrow it down to a specific question, you will need to research everything that you can find about it. You can collect information from your own experiences, books, the internet, or even smaller "unofficial" experiments.

Let's continue the example of a science fair idea about tomatoes in the garden. You like to garden, and notice that some tomatoes are bigger than others and wonder why.

Because of this personal experience and an interest in the problem, you decide to learn more about what makes plants grow.

For this stage of the Scientific Method, it's important to use as many sources as you can find. The more information you have on your science fair topic, the better the design of your experiment is going to be, and the better your science fair project is going to be overall.

Also try to get information from your teachers or librarians, or professionals who know something about your science fair project. They can help to guide you to a solid experimental setup.

The next stage of the Scientific Method is known as the "hypothesis." This word basically means "a possible solution to a problem, based on knowledge and research."

The hypothesis is a simple statement that defines what you think the outcome of your experiment will be.

All of the first stage of the Scientific Method -- the observation, or research stage -- is designed to help you express a problem in a single question ("Does the amount of sunlight in a garden affect tomato size?") and propose an answer to the question based on what you know. The experiment that you will design is done to test the hypothesis.

Using the example of the tomato experiment, here is an example of a hypothesis:

TOPIC: "Does the amount of sunlight a tomato plant receives affect the size of the tomatoes?"

HYPOTHESIS: "I believe that the more sunlight a tomato plant receives, the larger the tomatoes will grow.

This hypothesis is based on:

(1) Tomato plants need sunshine to make food through photosynthesis, and logically, more sun means more food, and;

(2) Through informal, exploratory observations of plants in a garden, those with more sunlight appear to grow bigger.

The hypothesis is your general statement of how you think the scientific phenomenon in question works.

Your prediction lets you get specific -- how will you demonstrate that your hypothesis is true? The experiment that you will design is done to test the prediction.

An important thing to remember during this stage of the scientific method is that once you develop a hypothesis and a prediction, you shouldn't change it, even if the results of your experiment show that you were wrong.

An incorrect prediction does NOT mean that you "failed." It just means that the experiment brought some new facts to light that maybe you hadn't thought about before.

Continuing our tomato plant example, a good prediction would be: Increasing the amount of sunlight tomato plants in my experiment receive will cause an increase in their size compared to identical plants that received the same care but less light.

This is the part of the scientific method that tests your hypothesis. An experiment is a tool that you design to find out if your ideas about your topic are right or wrong.

It is absolutely necessary to design a science fair experiment that will accurately test your hypothesis. The experiment is the most important part of the scientific method. It's the logical process that lets scientists learn about the world.

On the next page, we'll discuss the ways that you can go about designing a science fair experiment idea.

The final step in the scientific method is the conclusion. This is a summary of the experiment's results, and how those results match up to your hypothesis.

You have two options for your conclusions: based on your results, either:

(1) YOU CAN REJECT the hypothesis, or

(2) YOU CAN NOT REJECT the hypothesis.

This is an important point!

You can not PROVE the hypothesis with a single experiment, because there is a chance that you made an error somewhere along the way.

What you can say is that your results SUPPORT the original hypothesis.

If your original hypothesis didn't match up with the final results of your experiment, don't change the hypothesis.

Instead, try to explain what might have been wrong with your original hypothesis. What information were you missing when you made your prediction? What are the possible reasons the hypothesis and experimental results didn't match up?

Remember, a science fair experiment isn't a failure simply because does not agree with your hypothesis. No one will take points off if your prediction wasn't accurate. Many important scientific discoveries were made as a result of experiments gone wrong!

A science fair experiment is only a failure if its design is flawed. A flawed experiment is one that (1) doesn't keep its variables under control, and (2) doesn't sufficiently answer the question that you asked of it.

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Six Steps of the Scientific Method

Learn What Makes Each Stage Important

ThoughtCo. / Hugo Lin 

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• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
• B.A., Physics and Mathematics, Hastings College

The scientific method is a systematic way of learning about the world around us. The key difference between the scientific method and other ways of acquiring knowledge is that, when using the scientific method, we make hypotheses and then test them with an experiment.

Anyone can use the scientific method to acquire knowledge by asking questions and then working to find the answers to those questions. Below are the six steps involved in the scientific method and variables you may encounter when working with this method.

The Six Steps

The number of steps in the scientific method can vary from one description to another (which mainly happens when data and analysis are separated into separate steps), however, below is a fairly standard list of the six steps you'll likely be expected to know for any science class:

• Purpose/Question Ask a question.
• Research Conduct background research. Write down your sources so you can cite your references. In the modern era, you might conduct much of your research online. As you read articles and papers online, ensure you scroll to the bottom of the text to check the author's references. Even if you can't access the full text of a published article, you can usually view the abstract to see the summary of other experiments . Interview experts on a topic. The more you know about a subject, the easier it'll be to conduct your investigation.
• Hypothesis Propose a hypothesis . This is a sort of educated guess about what you expect your research to reveal. A hypothesis is a statement used to predict the outcome of an experiment. Usually, a hypothesis is written in terms of cause and effect. Alternatively, it may describe the relationship between two phenomena. The null hypothesis or the no-difference hypothesis is one type of hypothesis that's easy to test because it assumes changing a variable will not affect the outcome. In reality, you probably expect a change, but rejecting a hypothesis may be more useful than accepting one.
• Experiment Design and experiment to test your hypothesis. An experiment has an independent and dependent variable. You change or control the independent variable and record the effect it has on the dependent variable . It's important to change only one variable for an experiment rather than try to combine the effects of variables in an experiment. For example, if you want to test the effects of light intensity and fertilizer concentration on the growth rate of a plant, you're looking at two separate experiments.
• Data/Analysis Record observations and analyze the meaning of the data. Often, you'll prepare a table or graph of the data. Don't throw out data points you think are bad or that don't support your predictions. Some of the most incredible discoveries in science were made because the data looked wrong! Once you have the data, you may need to perform a mathematical analysis to support or refute your hypothesis.
• Conclusion Conclude whether to accept or reject your hypothesis. There's no right or wrong outcome to an experiment, so either result is fine. Accepting a hypothesis doesn't necessarily mean it's correct! Sometimes repeating an experiment may give a different result. In other cases, a hypothesis may predict an outcome, yet you might draw an incorrect conclusion. Communicate your results. You can compile your results into a lab report or formally submit them as a paper . Whether you accept or reject the hypothesis, you likely learned something about the subject and may wish to revise the original hypothesis or form a new one for a future experiment.

When Are There Seven Steps?

Some teach the scientific method with seven steps instead of six. In the seven-step model, the first step is to make observations. Even if you don't make observations formally, you should think about prior experiences with a subject to ask a question or solve a problem.

Formal observations are a type of brainstorming that can help you find an idea and form a hypothesis. Observe your subject and record everything about it. Include colors, timing, sounds, temperatures, changes, behavior, and anything that strikes you as interesting or significant.

When you design an experiment, you're controlling and measuring variables. There are three types of variables:

• Controlled Variables:  You can have as many  controlled variables  as you like. These are parts of the experiment that you try to keep constant throughout an experiment so they won't interfere with your test. Writing down controlled variables is a good idea because it helps make your experiment  reproducible , which is important in science! If you have trouble duplicating results from one experiment to another, there may be a controlled variable you missed.
• Independent Variable:  This is the variable you control.
• Dependent Variable:  This is the variable you measure. It's called the dependent variable because it  depends  on the independent variable.
• Null Hypothesis Examples
• Scientific Method Flow Chart
• Random Error vs. Systematic Error
• What Is an Experimental Constant?
• Scientific Variable
• What Is a Hypothesis? (Science)
• What Are the Elements of a Good Hypothesis?
• What Are Examples of a Hypothesis?
• What Is a Testable Hypothesis?
• Scientific Hypothesis Examples
• Scientific Method Vocabulary Terms
• Understanding Simple vs Controlled Experiments
• The Role of a Controlled Variable in an Experiment
• What Is the Difference Between a Control Variable and Control Group?
• What Is a Controlled Experiment?
• DRY MIX Experiment Variables Acronym

Exploring the Scientific Method

Students in science classes often start off the first chapter with that familiar step-by-step flow chart showing how scientists develop a hypothesis, test a hypothesis, gather data, and then draw conclusions. These chapters often include an experiment where students follow directions and answer a question.

Unfortunately, these experiments often don't do justice to the creative process of designing a method to test a hypothesis, because the method is provided for them. The development of an authentic way to test a hypothesis requires students use logic and communication, as well as flexibility with changing a design that doesn't seem to work. Inquiry labs, or open-ended labs, allow students to explore science, answer a question, or test a hypothesis without the traditional recipe tied to traditional labs. Inquiry labs are a great way to start a chapter on the scientific method, or start any science class.

Teachers are sometimes nervous to conduct inquiry labs due to equipment availability or safety concerns. However, inquiry labs can be cheap and fun for the students. The following list gives some ideas for beginning inquiry with a list of equipment you could use, but like the labs themselves, no other instructions are given. Students must figure out how to answer the question using logic and data gathering. Each lab starts with a simple question....

1. Oreo Cookie Challenge

Do Double-Stuff Oreos actually have double the stuffing or regular Oreos ?

Equipment: Scale, Beaker, Rulers, *Oreos and Double Stuff Oreos

*you may need to set some guidelines about eating the experiment items

2. Are Bounty paper towels more absorbant than generic paper towels?

Equipment: Beaker, Graduated Cylinder, Scale, Rules, Water, Two types of towels

Dissecting trays or pans might help for catching water.

3. How does surface area of a candy affect how quickly it dissolves in water?

Equipment: smarties or sweet tarts (or any sugary dissolvable candy that can be cut into smaller portions), water, ruler, scale, *scalpel or blade, timer

*requires safety discussion beforehand

4. Many gum brands claim that they have the longest lasting flavor. Design and conduct an experiment to determine what type of gum has the longest lasting flavor.

clock, a variety of different types of gum

5. Which type of polish remover works best, acetone or acetone free?

fingernail polish, polish remover (2 types), dishes, lids, or other surfaces to paint on, variety is good so that students must consider the surface when conducting the experiment.

Remember that the answer to the question is not as important as the process, and in fact, some might not have an exact correct answer. Students should document their process, instructors should write guidelines on the board and have students turn in their lab report with data gathered and an answer to the question. It is important to circulate around the room and offer suggestions and criticisms for the student designed experiments.

Sample Guidelines On a single paper for your group write: 1) Experiment Question 2) Your hypothesis (include reasoning) 3) Your experimental design * 4) Data collected 5) Your Conclusions

*You may want to withhold experimental equipment until after students have presented a valid design, this forces students to really think about how they will test rather than just grabbing stuff and jumping right in.

Now that students have a feel for consumer testing, ask them to evaluate claims they see on commercials, possibly design and conduct an experiment to test those claims.

Related Resources

Variables with Simpsons  – read stories involving characters from the Simpsons and determine the independent and dependent variables

Independent Variables  – read a short sentence of science experiment and determine the variables

Investigation – Heat Storage and Loss  – Use a jar and different types of insulation to explore how heat is lost and which materials are better insulators ( Key, TpT )

Plop Plop Fiz Fiz  – measure the rate of dissolving in alka-seltzer tablets in both hot and cold water (a basic experiment for introducing the scientific method)

Science and the scientific method: Definitions and examples

Here's a look at the foundation of doing science — the scientific method.

The scientific method

Hypothesis, theory and law, a brief history of science, additional resources, bibliography.

Science is a systematic and logical approach to discovering how things in the universe work. It is also the body of knowledge accumulated through the discoveries about all the things in the universe. 

The word "science" is derived from the Latin word "scientia," which means knowledge based on demonstrable and reproducible data, according to the Merriam-Webster dictionary . True to this definition, science aims for measurable results through testing and analysis, a process known as the scientific method. Science is based on fact, not opinion or preferences. The process of science is designed to challenge ideas through research. One important aspect of the scientific process is that it focuses only on the natural world, according to the University of California, Berkeley . Anything that is considered supernatural, or beyond physical reality, does not fit into the definition of science.

When conducting research, scientists use the scientific method to collect measurable, empirical evidence in an experiment related to a hypothesis (often in the form of an if/then statement) that is designed to support or contradict a scientific theory .

"As a field biologist, my favorite part of the scientific method is being in the field collecting the data," Jaime Tanner, a professor of biology at Marlboro College, told Live Science. "But what really makes that fun is knowing that you are trying to answer an interesting question. So the first step in identifying questions and generating possible answers (hypotheses) is also very important and is a creative process. Then once you collect the data you analyze it to see if your hypothesis is supported or not."

The steps of the scientific method go something like this, according to Highline College :

• Make an observation or observations.
• Form a hypothesis — a tentative description of what's been observed, and make predictions based on that hypothesis.
• Test the hypothesis and predictions in an experiment that can be reproduced.
• Analyze the data and draw conclusions; accept or reject the hypothesis or modify the hypothesis if necessary.
• Reproduce the experiment until there are no discrepancies between observations and theory. "Replication of methods and results is my favorite step in the scientific method," Moshe Pritsker, a former post-doctoral researcher at Harvard Medical School and CEO of JoVE, told Live Science. "The reproducibility of published experiments is the foundation of science. No reproducibility — no science."

Some key underpinnings to the scientific method:

• The hypothesis must be testable and falsifiable, according to North Carolina State University . Falsifiable means that there must be a possible negative answer to the hypothesis.
• Research must involve deductive reasoning and inductive reasoning . Deductive reasoning is the process of using true premises to reach a logical true conclusion while inductive reasoning uses observations to infer an explanation for those observations.
• An experiment should include a dependent variable (which does not change) and an independent variable (which does change), according to the University of California, Santa Barbara .
• An experiment should include an experimental group and a control group. The control group is what the experimental group is compared against, according to Britannica .

The process of generating and testing a hypothesis forms the backbone of the scientific method. When an idea has been confirmed over many experiments, it can be called a scientific theory. While a theory provides an explanation for a phenomenon, a scientific law provides a description of a phenomenon, according to The University of Waikato . One example would be the law of conservation of energy, which is the first law of thermodynamics that says that energy can neither be created nor destroyed. 

A law describes an observed phenomenon, but it doesn't explain why the phenomenon exists or what causes it. "In science, laws are a starting place," said Peter Coppinger, an associate professor of biology and biomedical engineering at the Rose-Hulman Institute of Technology. "From there, scientists can then ask the questions, 'Why and how?'"

Laws are generally considered to be without exception, though some laws have been modified over time after further testing found discrepancies. For instance, Newton's laws of motion describe everything we've observed in the macroscopic world, but they break down at the subatomic level.

This does not mean theories are not meaningful. For a hypothesis to become a theory, scientists must conduct rigorous testing, typically across multiple disciplines by separate groups of scientists. Saying something is "just a theory" confuses the scientific definition of "theory" with the layperson's definition. To most people a theory is a hunch. In science, a theory is the framework for observations and facts, Tanner told Live Science.

The earliest evidence of science can be found as far back as records exist. Early tablets contain numerals and information about the solar system , which were derived by using careful observation, prediction and testing of those predictions. Science became decidedly more "scientific" over time, however.

1200s: Robert Grosseteste developed the framework for the proper methods of modern scientific experimentation, according to the Stanford Encyclopedia of Philosophy. His works included the principle that an inquiry must be based on measurable evidence that is confirmed through testing.

1400s: Leonardo da Vinci began his notebooks in pursuit of evidence that the human body is microcosmic. The artist, scientist and mathematician also gathered information about optics and hydrodynamics.

1500s: Nicolaus Copernicus advanced the understanding of the solar system with his discovery of heliocentrism. This is a model in which Earth and the other planets revolve around the sun, which is the center of the solar system.

1600s: Johannes Kepler built upon those observations with his laws of planetary motion. Galileo Galilei improved on a new invention, the telescope, and used it to study the sun and planets. The 1600s also saw advancements in the study of physics as Isaac Newton developed his laws of motion.

1700s: Benjamin Franklin discovered that lightning is electrical. He also contributed to the study of oceanography and meteorology. The understanding of chemistry also evolved during this century as Antoine Lavoisier, dubbed the father of modern chemistry , developed the law of conservation of mass.

1800s: Milestones included Alessandro Volta's discoveries regarding electrochemical series, which led to the invention of the battery. John Dalton also introduced atomic theory, which stated that all matter is composed of atoms that combine to form molecules. The basis of modern study of genetics advanced as Gregor Mendel unveiled his laws of inheritance. Later in the century, Wilhelm Conrad Röntgen discovered X-rays , while George Ohm's law provided the basis for understanding how to harness electrical charges.

1900s: The discoveries of Albert Einstein , who is best known for his theory of relativity, dominated the beginning of the 20th century. Einstein's theory of relativity is actually two separate theories. His special theory of relativity, which he outlined in a 1905 paper, " The Electrodynamics of Moving Bodies ," concluded that time must change according to the speed of a moving object relative to the frame of reference of an observer. His second theory of general relativity, which he published as " The Foundation of the General Theory of Relativity ," advanced the idea that matter causes space to curve.

In 1952, Jonas Salk developed the polio vaccine , which reduced the incidence of polio in the United States by nearly 90%, according to Britannica . The following year, James D. Watson and Francis Crick discovered the structure of DNA , which is a double helix formed by base pairs attached to a sugar-phosphate backbone, according to the National Human Genome Research Institute .

2000s: The 21st century saw the first draft of the human genome completed, leading to a greater understanding of DNA. This advanced the study of genetics, its role in human biology and its use as a predictor of diseases and other disorders, according to the National Human Genome Research Institute .

• This video from City University of New York delves into the basics of what defines science.
• Learn about what makes science science in this book excerpt from Washington State University .
• This resource from the University of Michigan — Flint explains how to design your own scientific study.

Merriam-Webster Dictionary, Scientia. 2022. https://www.merriam-webster.com/dictionary/scientia

University of California, Berkeley, "Understanding Science: An Overview." 2022. ​​ https://undsci.berkeley.edu/article/0_0_0/intro_01  

Highline College, "Scientific method." July 12, 2015. https://people.highline.edu/iglozman/classes/astronotes/scimeth.htm  

North Carolina State University, "Science Scripts." https://projects.ncsu.edu/project/bio183de/Black/science/science_scripts.html  

University of California, Santa Barbara. "What is an Independent variable?" October 31,2017. http://scienceline.ucsb.edu/getkey.php?key=6045  

Encyclopedia Britannica, "Control group." May 14, 2020. https://www.britannica.com/science/control-group  

The University of Waikato, "Scientific Hypothesis, Theories and Laws." https://sci.waikato.ac.nz/evolution/Theories.shtml  

Stanford Encyclopedia of Philosophy, Robert Grosseteste. May 3, 2019. https://plato.stanford.edu/entries/grosseteste/  

Encyclopedia Britannica, "Jonas Salk." October 21, 2021. https://www.britannica.com/ biography /Jonas-Salk

National Human Genome Research Institute, "​Phosphate Backbone." https://www.genome.gov/genetics-glossary/Phosphate-Backbone  

National Human Genome Research Institute, "What is the Human Genome Project?" https://www.genome.gov/human-genome-project/What  

‌ Live Science contributor Ashley Hamer updated this article on Jan. 16, 2022.

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3 Science Experiments Using the Scientific Method

July 1, 2018

If you’ve ever conducted a scientific experiment with your child, you’ve probably noticed that most kid-friendly experiments rely heavily upon observation. For most of your child’s life, he or she has been asked to observe a reaction or result of an experiment to learn about the underlying scientific principal. 

What Exactly is Scientific Method? 

You’ve probably heard of the scientific method, and used it yourself when you were in school. The scientific method is used by scientists to ensure that the results of their experiments are reliable and valid. When kids use the scientific method, they learn more and think critically, asking questions and making predictions about their experiments. 

Scientists start with a question they want to answer, which serves as a goal and sets a purpose for the experiment. This is the most important part! Every experiment should start with a big question that guides the research being conducted. Next, participants form a hypothesis , or a prediction based on prior knowledge. After gathering materials needed for the experiment, the procedure is conducted and scientists make observations and record data and results. Finally, a conclusion is reached and published. 

Your child can emulate this process at home by simply modifying each experiment to include a big question, and a hypothesis that will drive their experiment and process. Let’s explore ideas for 3rd grade science experiments using the scientific method! Learn more how to incorporate science into your child's routine . 

  Experiment 1. Which Liquids Melt the Fastest? 

Simple scientific method experiments should be easy and fun and include everyday supplies you can find in your own home! This experiment will help your child understand how various household liquids melt at different rates.

For detailed instructions to complete this experiment, and for even more information on using the scientific method with your child, check out the full video guided by Kids Academy teacher!

Materials Needed: 

• Different liquids, like milk, water, iced tea, and orange juice
• Ice cube trays

Experiement 2. How Does Water Travel from Roots to Leaves? 

Has your child ever wondered why a plant’s leaves are supple and moist? When we water plants, how does the moisture travel from the roots to the leaves? This experiment teaches kids about the xylem tubes that transport water through plants through the process of capillary action! 

• 3 clear, glass jars
• 3 different colors of food coloring
• 3 celery stalks

Step 1: Help your child form a big question before getting started. 

Step 2: Encourage your child to make predictions based on their prior knowledge. For instance, plants are alive, just like people, and may have cells or structures that transport the water from roots to tip.

Step 3: Gather the above materials.

Step 4: Cut the bottom off the celery stalks, about one inch from the base.

Step 5: Fill each jar about half way with water. Add a few drops of food coloring to each, ensuring that each jar is a different color. 

Step 6: Place a stalk of celery in each jar and let them sit for about 20 minutes to an hour. 

Step 7: Observe the results! Rip apart the stalks to see how the coloring travels through each stalk. Notice how the color reaches the leaves at the very tip of the stalk! 

Step 8: Record results and help your child draw a conclusion. 

Explain to your child that xylem tubes are structures in plants that carry water from the roots up through the tip of the plant. This process is called capillary action, and it works a lot like a straw sucking the water up through the plant! 

Play & Learn Science

  Experiment 3. Does it Dissolve? 

Do all substances dissolve in water? Kids explore the varying levels of solubility of common household substances in this fun-filled experiment! 

• 4 clear, glass jars filled with plain tap water
• Talcum or baby powder
• Granulated sugar

Step 1: Help your child form a big question before starting the experiment. 

Step 2: Make a hypothesis for each substance. Perhaps the salt will dissolve because your child has watched you dissolve salt or sugar in water when cooking. Maybe the baby powder will not dissolve because of its powdery texture. Help your child write down his or her predictions. 

Step 3: Scoop a teaspoon of each substance in the jars, only adding one substance per jar. Stir it up! 

Step 4: Observe whether or not each substance dissolves and record the findings! 

Your child will likely note that that sugar and salt dissolve, while the flour will partially dissolve, and the baby powder will remain intact. The grainy crystals of the sugar and salt are easily dissolved in water, but the dry, powdery substances are likely to clump up or remain at the bottom of the jar.  

As you can see, the scientific method is easy to work into your child’s scientific experiments. Not only does it increase your child’s scientific learning and critical thinking skills, but it sparks curiosity and motivates kids as they learn to ask questions and prove their ideas! Get started today with the above ideas, and bring the scientific method home to your child during your next exciting science experiment! 

  Play & Learn Science

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Teaching the Scientific Method with 7 Easy Science Experiments

I’m sure you have seen long, drawn-out lesson plans for teaching the scientific method in the elementary grades.

It doesn’t really have to be that difficult to teach kids how to work through the steps.

What if I told you there was an easy way to introduce the scientific method to your students and help reinforce science-related skills?

Well, there is! Maybe I should say I have seven easy ways to teach and reinforce the scientific method to students.

I actually came up with 7 Easy Science Experiments that do just that. (TIP: Use the code THANKYOU for 10% off today!)

They are easy, fun, and engaging.

Easy Experiments for Teaching the Scientific Method

I know you are wondering what these experiments entail. And you probably want to know just how easy they really are. Trust me. I like easy. They are easy.

The 7 easy science experiments include:

·        Rainbow Milk Experiment

·        Tornado in a Bottle Experiment

·        Fingerprint Science Experiment (Read more about this one HERE )

·        Marshmallow Toothpick Tower Experiment

·        Coffee Filter Pigment Experiment

·        Flubber Slime Experiment

·        Clean and Dirty Penny Experiment

  So, what’s in each book?

Each experiment can be used as a stand-alone project and includes:

·        Directions for each experiment

·        Full digital version

·        Student flipbooks (fill in the blank, observation notes, experiment notes)

What is the digital version?

The digital version is the exact same lesson including the student worksheets, but it’s all done online.

Students get the same workbook pages on Google slides and can fill out the whole thing online and then click “present” to share their saved information with a teacher or parent.

This digital version can be incorporated into a virtual learning environment in a lot of ways.

You can make your own videos and upload them.

Maybe you don’t want to make a video. There are tons of them on YouTube that can be used when teaching the scientific method.

It’s versatile and super easy to adapt to your teaching style and teaching environment.

It can all be easily added to Google Drive, so you have the option of creating a full virtual experience for students.

Ways to Incorporate the 7 Easy Experiments Lessons into the Classroom

There are a lot of ways to use these lessons for teaching the scientific method.

Use it for an end-of-week, fun day activity on Fridays.

It makes a great reward lesson once students have accomplished a class goal.

It can be used for extension or enrichment activities.

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Conclusion:

This bundle seriously has everything you need to teach and reinforce the scientific method. It has booklets, worksheets, directions, tab books, and a full digital version of the activities too.

Do you have any tried-and-true tricks for teaching the scientific method? Share your ideas with us!

P.S. Two ways to grab a copy of the 7 Easy Science Experiments

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You may also like, a letter to teachers during covid-19, easy parent gift from students [inexpensive], fun math review for 2nd graders, using forensics in your science experiments, handwriting made fun.

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Scientific Method Steps in Psychology Research

Steps, Uses, and Key Terms

Verywell / Theresa Chiechi

How do researchers investigate psychological phenomena? They utilize a process known as the scientific method to study different aspects of how people think and behave.

When conducting research, the scientific method steps to follow are:

• Observe what you want to investigate
• Ask a research question and make predictions
• Test the hypothesis and collect data
• Examine the results and draw conclusions
• Report and share the results 

This process not only allows scientists to investigate and understand different psychological phenomena but also provides researchers and others a way to share and discuss the results of their studies.

Generally, there are five main steps in the scientific method, although some may break down this process into six or seven steps. An additional step in the process can also include developing new research questions based on your findings.

What Is the Scientific Method?

What is the scientific method and how is it used in psychology?

The scientific method consists of five steps. It is essentially a step-by-step process that researchers can follow to determine if there is some type of relationship between two or more variables.

By knowing the steps of the scientific method, you can better understand the process researchers go through to arrive at conclusions about human behavior.

Scientific Method Steps

While research studies can vary, these are the basic steps that psychologists and scientists use when investigating human behavior.

The following are the scientific method steps:

Step 1. Make an Observation

Before a researcher can begin, they must choose a topic to study. Once an area of interest has been chosen, the researchers must then conduct a thorough review of the existing literature on the subject. This review will provide valuable information about what has already been learned about the topic and what questions remain to be answered.

A literature review might involve looking at a considerable amount of written material from both books and academic journals dating back decades.

The relevant information collected by the researcher will be presented in the introduction section of the final published study results. This background material will also help the researcher with the first major step in conducting a psychology study: formulating a hypothesis.

Step 2. Ask a Question

Once a researcher has observed something and gained some background information on the topic, the next step is to ask a question. The researcher will form a hypothesis, which is an educated guess about the relationship between two or more variables

For example, a researcher might ask a question about the relationship between sleep and academic performance: Do students who get more sleep perform better on tests at school?

In order to formulate a good hypothesis, it is important to think about different questions you might have about a particular topic.

You should also consider how you could investigate the causes. Falsifiability is an important part of any valid hypothesis. In other words, if a hypothesis was false, there needs to be a way for scientists to demonstrate that it is false.

Step 3. Test Your Hypothesis and Collect Data

Once you have a solid hypothesis, the next step of the scientific method is to put this hunch to the test by collecting data. The exact methods used to investigate a hypothesis depend on exactly what is being studied. There are two basic forms of research that a psychologist might utilize: descriptive research or experimental research.

Descriptive research is typically used when it would be difficult or even impossible to manipulate the variables in question. Examples of descriptive research include case studies, naturalistic observation , and correlation studies. Phone surveys that are often used by marketers are one example of descriptive research.

Correlational studies are quite common in psychology research. While they do not allow researchers to determine cause-and-effect, they do make it possible to spot relationships between different variables and to measure the strength of those relationships. 

Experimental research is used to explore cause-and-effect relationships between two or more variables. This type of research involves systematically manipulating an independent variable and then measuring the effect that it has on a defined dependent variable .

One of the major advantages of this method is that it allows researchers to actually determine if changes in one variable actually cause changes in another.

While psychology experiments are often quite complex, a simple experiment is fairly basic but does allow researchers to determine cause-and-effect relationships between variables. Most simple experiments use a control group (those who do not receive the treatment) and an experimental group (those who do receive the treatment).

Step 4. Examine the Results and Draw Conclusions

Once a researcher has designed the study and collected the data, it is time to examine this information and draw conclusions about what has been found.  Using statistics , researchers can summarize the data, analyze the results, and draw conclusions based on this evidence.

So how does a researcher decide what the results of a study mean? Not only can statistical analysis support (or refute) the researcher’s hypothesis; it can also be used to determine if the findings are statistically significant.

When results are said to be statistically significant, it means that it is unlikely that these results are due to chance.

Based on these observations, researchers must then determine what the results mean. In some cases, an experiment will support a hypothesis, but in other cases, it will fail to support the hypothesis.

So what happens if the results of a psychology experiment do not support the researcher's hypothesis? Does this mean that the study was worthless?

Just because the findings fail to support the hypothesis does not mean that the research is not useful or informative. In fact, such research plays an important role in helping scientists develop new questions and hypotheses to explore in the future.

After conclusions have been drawn, the next step is to share the results with the rest of the scientific community. This is an important part of the process because it contributes to the overall knowledge base and can help other scientists find new research avenues to explore.

Step 5. Report the Results

The final step in a psychology study is to report the findings. This is often done by writing up a description of the study and publishing the article in an academic or professional journal. The results of psychological studies can be seen in peer-reviewed journals such as  Psychological Bulletin , the  Journal of Social Psychology ,  Developmental Psychology , and many others.

The structure of a journal article follows a specified format that has been outlined by the  American Psychological Association (APA) . In these articles, researchers:

• Provide a brief history and background on previous research
• Present their hypothesis
• Identify who participated in the study and how they were selected
• Provide operational definitions for each variable
• Describe the measures and procedures that were used to collect data
• Explain how the information collected was analyzed
• Discuss what the results mean

Why is such a detailed record of a psychological study so important? By clearly explaining the steps and procedures used throughout the study, other researchers can then replicate the results. The editorial process employed by academic and professional journals ensures that each article that is submitted undergoes a thorough peer review, which helps ensure that the study is scientifically sound.

Once published, the study becomes another piece of the existing puzzle of our knowledge base on that topic.

Before you begin exploring the scientific method steps, here's a review of some key terms and definitions that you should be familiar with:

• Falsifiable : The variables can be measured so that if a hypothesis is false, it can be proven false
• Hypothesis : An educated guess about the possible relationship between two or more variables
• Variable : A factor or element that can change in observable and measurable ways
• Operational definition : A full description of exactly how variables are defined, how they will be manipulated, and how they will be measured

Uses for the Scientific Method

The  goals of psychological studies  are to describe, explain, predict and perhaps influence mental processes or behaviors. In order to do this, psychologists utilize the scientific method to conduct psychological research. The scientific method is a set of principles and procedures that are used by researchers to develop questions, collect data, and reach conclusions.

Goals of Scientific Research in Psychology

Researchers seek not only to describe behaviors and explain why these behaviors occur; they also strive to create research that can be used to predict and even change human behavior.

Psychologists and other social scientists regularly propose explanations for human behavior. On a more informal level, people make judgments about the intentions, motivations , and actions of others on a daily basis.

While the everyday judgments we make about human behavior are subjective and anecdotal, researchers use the scientific method to study psychology in an objective and systematic way. The results of these studies are often reported in popular media, which leads many to wonder just how or why researchers arrived at the conclusions they did.

Examples of the Scientific Method

Now that you're familiar with the scientific method steps, it's useful to see how each step could work with a real-life example.

Say, for instance, that researchers set out to discover what the relationship is between psychotherapy and anxiety .

• Step 1. Make an observation : The researchers choose to focus their study on adults ages 25 to 40 with generalized anxiety disorder.
• Step 2. Ask a question : The question they want to answer in their study is: Do weekly psychotherapy sessions reduce symptoms in adults ages 25 to 40 with generalized anxiety disorder?
• Step 3. Test your hypothesis : Researchers collect data on participants' anxiety symptoms . They work with therapists to create a consistent program that all participants undergo. Group 1 may attend therapy once per week, whereas group 2 does not attend therapy.
• Step 4. Examine the results : Participants record their symptoms and any changes over a period of three months. After this period, people in group 1 report significant improvements in their anxiety symptoms, whereas those in group 2 report no significant changes.
• Step 5. Report the results : Researchers write a report that includes their hypothesis, information on participants, variables, procedure, and conclusions drawn from the study. In this case, they say that "Weekly therapy sessions are shown to reduce anxiety symptoms in adults ages 25 to 40."

Of course, there are many details that go into planning and executing a study such as this. But this general outline gives you an idea of how an idea is formulated and tested, and how researchers arrive at results using the scientific method.

Erol A. How to conduct scientific research ? Noro Psikiyatr Ars . 2017;54(2):97-98. doi:10.5152/npa.2017.0120102

University of Minnesota. Psychologists use the scientific method to guide their research .

Shaughnessy, JJ, Zechmeister, EB, & Zechmeister, JS. Research Methods In Psychology . New York: McGraw Hill Education; 2015.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Scientific Method

Reviewed by: BD Editors

The scientific method is a series of processes that people can use to gather knowledge about the world around them, improve that knowledge, and attempt to explain why and/or how things occur. This method involves making observations, forming questions, making hypotheses, doing an experiment, analyzing the data, and forming a conclusion. Every scientific experiment performed is an example of the scientific method in action, but it is also used by non-scientists in everyday situations.

Scientific Method Overview

The scientific method is a process of trying to get as close as possible to the  objective truth . However, part of the process is to constantly refine your conclusions, ask new questions, and continue the search for the rules of the universe. Through the scientific method, scientists are trying to uncover how the world works and discover the laws that make it function in that way. You can use the scientific method to find answers for almost any question, though the scientific method can yield conflicting evidence based on the method of experimentation. In other words, the scientific method is a very useful way to figure things out – though it must be used with caution and care!

Scientific Method Steps

The exact steps of the scientific method vary from source to source , but the general procedure is the same: acquiring knowledge through observation and testing.

Making an Observation

The first step of the scientific method is to make an observation about the world around you. Before hypotheses can be made or experiments can be done, one must first notice and think about some sort of phenomena occurring. The scientific method is used when one does not know why or how something is occurring and wants to uncover the answer. But, before you can form a question you must notice something puzzling in the first place.

Asking a Question

Next, one must ask a question based on their observations. Here are some examples of good questions:

• Why is this thing occurring?
• How is this thing occurring?
• Why or how does it happen this way?

Sometimes this step is listed first in the scientific method, with making an observation (and researching the phenomena in question) listed as second. In reality, both making observations and asking questions tend to happen around the same time.

One can see a confusing occurrence and immediately think, “why is it occurring?” When observations are being made and questions are being formed, it is important to do research to see if others have already answered the question or uncovered information that may help you shape your question. For example, if you find an answer to why something is occurring, you may want to go a step further and figure out how it occurs.

Forming a Hypothesis

A hypothesis is an educated guess to explain the phenomena occurring based on prior observations. It answers the question posed in the previous step. Hypotheses can be specific or more general depending on the question being asked, but all hypotheses must be testable by gathering evidence that can be measured. If a hypothesis is not testable, then it is impossible to perform an experiment to determine whether the hypothesis is supported by evidence.

Performing an Experiment

After forming a hypothesis, an experiment must be set up and performed to test the hypothesis. An experiment must have an independent variable (something that is manipulated by the person doing the experiment), and a dependent variable (the thing being measured which may be affected by the independent variable). All other variables must be controlled so that they do not affect the outcome. During an experiment, data is collected. Data is a set of values; it may be quantitative (e.g. measured in numbers) or qualitative (a description or generalization of the results).

For example, if you were to test the effect of sunlight on plant growth, the amount of light would be the independent variable (the thing you manipulate) and the height of the plants would be the dependent variable (the thing affected by the independent variable). Other factors such as air temperature, amount of water in the soil, and species of plant would have to be kept the same between all of the plants used in the experiment so that you could truly collect data on whether sunlight affects plant growth. The data that you would collect would be quantitative – since you would measure the height of the plant in numbers.

Analyzing Data

After performing an experiment and collecting data, one must analyze the data. Research experiments are usually analyzed with statistical software in order to determine relationships among the data. In the case of a simpler experiment, one could simply look at the data and see how they correlate with the change in the independent variable.

Forming a Conclusion

The last step of the scientific method is to form a conclusion. If the data support the hypothesis, then the hypothesis may be the explanation for the phenomena. However, multiple trials must be done to confirm the results, and it is also important to make sure that the sample size—the number of observations made—is big enough so that the data is not skewed by just a few observations.

If the data do not support the hypothesis, then more observations must be made, a new hypothesis is formed, and the scientific method is used all over again. When a conclusion is drawn, the research can be presented to others to inform them of the findings and receive input about the validity of the conclusion drawn from the research.

Scientific Method Examples

There are very many examples of the use of the scientific method throughout history because it is the basis for all scientific experiments. Scientists have been conducting experiments using the scientific method for hundreds of years.

One such example is Francesco Redi’s experiment on spontaneous generation. In the 17 th Century, when Redi lived, people commonly believed that living things could spontaneously arise from organic material. For example, people believed that maggots were created from meat that was left out to sit. Redi had an alternate hypothesis: that maggots were actually part of the fly life cycle!

He conducted an experiment by leaving four jars of meat out: some uncovered, some covered with muslin, and some sealed completely. Flies got into the uncovered jars and maggots appeared a short time later. The jars that were covered had maggots on the outer surface of the muslin, but not inside the jars. Sealed jars had absolutely no maggots whatsoever.

Redi was able to conclude that maggots did not spontaneously arise in meat. He further confirmed the results by collecting captured maggots and growing them into adult flies. This may seem like common sense today, but back then, people did not know as much about the world, and it is through experiments like these that people uncovered what is now common knowledge.

Scientists use the scientific method in their research, but it is also used by people who aren’t scientists in everyday life. Even if you were not consciously aware of it, you have used the scientific method many times when solving problems around you.

For example, say you are at home and a lightbulb goes out. Noticing that the lightbulb is out is an observation. You would then naturally question, “Why is the lightbulb out?” and come up with possible guesses, or hypotheses. For example, you may hypothesize that the bulb has burned out. Then you would perform a very small experiment in order to test your hypothesis; namely, you would replace the bulb and analyze the data (“Did the light come back on?”).

If the light turned back on, you would conclude that the lightbulb had, in fact, burned out. But if the light still did not work, you would come up with other hypotheses (“The socket doesn’t work”, “Part of the lamp is broken,” “The fuse went out”, etc.) and test those.

1. Which step of the scientific method comes immediately after making observations and asking a question?

2. A scientist is performing an experiment to determine if the amount of light that rodents are exposed to affects their sleep cycle. She places some rodents in a room with 12 hours of light and 12 hours of darkness, some in a room with 24-hour light, and some in 24-hour darkness. What is the independent variable in this experiment?

3. What is the last step of the scientific method?

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How to Conduct Experiments Using the Scientific Method

Introduction: How to Conduct Experiments Using the Scientific Method

Experiments are performed all around us everyday. Whether they're done to find out if a cancer curing medication works or to find out how fast water evaporates at certain temperatures, experiments are constantly performed. However, what separates a simple experiment from a professionally done experiment is the use of the Scientific Method. The Scientific Method is a series of organized steps to which an experiment is done. The Scientific Method helps you plan, predict, research, conclude and maybe even publish your findings. The Scientific Method will make your experiment more organized, easy to interpret and learn from. In this Instructable, I will help guide you through each step using a sample experiment. You will also learn the significance of each step as I break the Scientific Method down.

The steps to the Scientific Method are:

1) Pose a Testable Question.

2) Conduct Background Research.

3) State your Hypothesis .

4) Design Experiment .

5) Perform your Experiment .

6) Collect Data .

7) Draw Conclusions .

8) Publish Findings (optional).

Step 1: Understand the Sample Experiment

Our sample experiment is going to be the rate of sugar cubes dissolving in water at different temperatures. Basically, I will drop sugar cubes into cups of water with different temperatures and time how long it takes the sugar cubes to "disappear" (dissolve).

Step 2: Pose a Testable Question

The Testable Question is the question that the experiment is based on. Every experiment is performed because someone questions or is curious about something. So, all the T estable Question really does, is pose that burning question.

In the sample experiment, our Testable Question is:

Does water temperature affect the rate at which sugar cubes dissolve?

Step 3: Research the Topic

Researching your topic is very important. It helps you predict an outcome (Hypothesis) and helps you to better understand the subject.

Your research should include, information about prior experiments done that are the same or similar to yours, information about things you are using in your experiment (chemicals, tools, etc.), definitions of words that you don't know that are relevant to your experiment, etc.

Your research doesn't need to be organized in any particular fashion. Some ways to organize your information are bullet points, charts and graphs (t-charts, spreadsheets, bar graphs, line graphs, etc.), list of words and their respective definitions, etc.

Step 4: State a Hypothesis

The Hypothesis is a prediction, based on prior research, on the outcome of the experiment. Think of the Hypothesis as an educated estimate.

Your Hypothesis will predict your opinion on the outcome of the experiment. If research points one way, and you predict that your experiment will go another way, that's totally fine. That's the point of doing the experiment. To see if your Hypothesis is right or wrong.

A Hypothesis is usually stated using a 'if and then' statement. Your sentence will sound something like, If I drink water, then I will feel hydrated.

In the sample experiment, the Hypothesis can be:

If you increase water temperature, then the rate at which a sugar cube dissolves is increased.

Remember, the hypothesis can be any prediction of the outcome of the experiment you are conducting. So, again, this doesn't have to be your hypothesis.

Step 5: Design Your Experiment

There are five main things to cover in the design step. Those five things are:

1) Make a list of parts, materials and tools needed for your experiment.

2) Declare your control.

3) Declare your independent variable.

4) Declare your dependent variable.

5) Describe how you will perform your experiment.

Make a List of Parts, Materials and Tools Needed for your Experiment

For the sample experiment I will need:

• Two clear plastic cups filled with half a cup of water
• A thermometer
• Two sugar cubes
• Distilled water
• A stopwatch
• A measuring cup
• Two microwaveable bowls

Declare your Control Variable

The control variable is the normal scenario.

For the sample experiment, the control variable is:

• A cup of water that is room temperature (seventy two degrees Fahrenheit).

Declare your Independent Variable The independent variable is the one variable you change that makes the scenario different than normal conditions (control).

For the sample experiment, the independent variable is:

• Increasing the water temperature to about ninety five degrees Fahrenheit. This is the independent variable because the control, or normal scenario is about seventy two degrees Fahrenheit.

Please note that you can only change one variable per experiment. If more than one variable is made different than the control, your experiment is invalid and the information could be considered wrong.

Declare your Dependent Variable

The dependent variable is the way you will measure the results of the experiment.

For the sample experiment, the dependent variable is:

• How long it takes for the sugar cube to completely "disappear" (dissolve).

Describe how the Experiment will be Performed

Your description should be written so that if anyone were to read it, that person would be able to conduct the experiment just the way you did it.

For the sample experiment, the description should go like this:

• In this experiment, I filled two cups with half a cup of distilled water. One cup was measured at approximately seventy five degrees Fahrenheit and the other cup was measured at ninety five degrees Fahrenheit. I dropped a sugar cube in the first cup and started the stopwatch exactly at the same time when the sugar cube touched the water. I repeated the process one more time with the second cup. After the sugar cube completely disappeared, I stopped the stopwatch and recorded my results. I repeated the process one more time with the second cup.

Step 6: Perform the Experiment

All you have to do in this step is perform the experiment exactly as you described in the description in the last step.

Step 7: Collect Data

When you finish timing the first cup, write down your results. Repeat that with the second cup.

Your data collection at this point, doesn't need to be fancy. All this step does is ensure that you know what the data is so you could make it fancy and presentable in the next step with graphs and charts.

For the sample experiment, the data was:

• In the first cup (seventy five degrees Fahrenheit), the sugar cube dissolved in
• In the second cup (ninety five degrees Fahrenheit), the sugar cube dissolved in twenty four minutes and thirty seconds.

Step 8: Conclusions

When you finish collecting your Data, you should now conclude with an analysis of your experiment.

Your analysis should include:

1) Charts and graphs displaying results

2) A sentence/paragraph that states if you accept or reject your hypothesis

3) A summary recapping your experiment (optional)

Charts and Graphs

For the sample experiment, I would recommend using a bar graph.

Rejecting/Accepting Hypothesis

For the sample experiment, your paragraph should go like this:

• In my experiment, my hypothesis was rejected because the sugar cube dissolved into seventy five degree Fahrenheit water dissolved in less time than the ninety five degree Fahrenheit water.

Step 9: Publishing Findings (optional)

If your experiment was groundbreaking, really interesting or anything along those lines, you might want to consider publishing in a science magazine or journal.

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Scientific Method

Instructor prep, student protocol.

The scientific method is a framework of techniques and questions that scientists use to investigate phenomena with the aim of making scientific discoveries simple and reproducible. It's been loosely observed by experimenters going as far back as the 4th century BC, but the first properly formalized scientific method was coined during the European Renaissance. Here individuals at the forefront of science like Francis Bacon, Galileo, and Isaac Newton started putting into routine practice the rules that we use to carry out experiments today.

Typically, the first step of the scientific method is to formulate a question, usually after observation of a phenomenon. For example, say you have been raising caterpillars and have noticed that some take longer than others to get to pupation. And you wonder, do the caterpillars develop at different rates depending on the temperature?

This is where the second part of the scientific method comes in, the hypothesis. A hypothesis is an uncertain explanation as to why we observe what we observe, and there are two main types. The first is the experimental or alternative hypothesis, and it implies that there will be a relationship between the variables being investigated, the temperature and caterpillar development, in this case. So, our experimental hypothesis could be that the caterpillars will take longer to go from egg to pupation if they're raised at colder temperatures. Crucially, a good hypothesis will be testable. For our caterpillars, we can change the temperature, and record the time it takes for them to go from egg to pupa, and falsifiable. So, if it takes around the same time for the caterpillars to develop no matter what the temperature, then we can accept that the hypothesis was likely false. The second type of hypothesis is the null hypothesis. This typically speculates that there won't be any observed significant change or difference during the experiment. In our caterpillar example, we would state that the caterpillars will develop at the same rate in each temperature condition.

Once we have our hypotheses, the third step of the scientific method covers experimentation and data collection. In a typical experiment, there will be two types of variables. The independent variable is something directly manipulated by the experimenter. So, with our caterpillars, we are altering the independent variable when we change the temperature. The dependent variable, also known as the response variable, should be affected by the state of the independent variable. So, when we expose our caterpillars to different temperatures, then the response, the dependent variable, is the rate at which they develop.

There are also two main types of data that could be collected to support or falsify the hypotheses. The first is qualitative data, which typically refers to descriptive observations made with the senses, seeing, touching, hearing, smelling, or even tasting. In our experiment, we might record that the caterpillars seem to move around and eat a lot in the normal temperature condition, compared to the cooler one. In contrast to qualitative data, quantitative data can be measured and written down as numbers. So, when we count the number of hours it takes the caterpillar from hatching to finally pupating, this gives us a definite figure. Where possible, it's almost important to have a control condition in any experiment where we manipulate the independent variables. In our caterpillar experiment, we can grow the caterpillars at a set standard room temperature of 21 degrees as a control, because this demonstrates what happens when the caterpillars develop under normal conditions in comparison to experimental settings.

In observational experiments, a control may not be needed or even possible. For example, imagine our caterpillars are now grown up butterflies, feeding on nectar in a flower garden. In our experimental hypothesis, we suggest that they prefer to feed from the big pink flowers, while our null hypothesis suggests they have no preference and will visit the flowers at random. In this case, simply observing and recording the number of times the butterflies visit each flower type will provide enough data to confirm or reject our hypotheses without needing manipulation of any variables or the need for a control.

Once the data have been collected, the next step is to figure out what it all means. Scientists will compare the predictions of their two hypotheses to figure out if they can reject the null hypothesis. This can be done by comparing the values of the dependent variable in the control versus the experimental conditions. If they are not equal, the null hypothesis can be rejected. If the data collected supports a hypothesis, like the caterpillars did take significantly more hours to go from egg to pupa when kept at the cooler climate, then this gives the experimental hypothesis more credibility, but critically it does not indicate that the hypothesis is definitely true, because future experiments may reveal new information.

The final part of the scientific method is where we draw conclusions, and discuss what our findings might mean. Here, scientists might refer to other experiments or other literature to put their findings into context, and come up with explanations of why the results showed what they did. For example, the conclusion could be that the caterpillars like to grow at temperatures closest to their natural habitat. This may, in turn, spiral new questions, like do other species pupate at different rates at different temperatures, too? This may inspire new experiments, which we can test using, you guessed it, the scientific method.

The scientific method is used to solve problems and explain phenomena. The development of the scientific method coincided with changes in philosophy underpinning scientific discovery, radically transforming the views of society about nature. During the European Renaissance, individuals such as Francis Bacon, Galileo, and Isaac Newton formalized the concept of the scientific method and put it into practice. Although the scientific method has been revised since its early conceptions, much of the framework and philosophy remains in practice today.

Step 1: The Observation and Question

Prior to investigation, a scientist must define the question to be addressed. This crucial first step in the scientific process involves observing some natural phenomena of interest. This observation should then lead to a number of questions about the phenomena. This stage frequently requires background research necessary to understand the subject matter and past work on similar ideas. Reviewing and evaluating previous research allows scientists to refine their questions to more accurately address gaps in scientific knowledge. Defining a research question and understanding relevant prior research will influence how the scientific method is applied, making it an important first step in the research process.

An everyday example: You are trying to get to school or work and your car won’t start. The thought process that most people go through in that situation clearly mirrors the official scientific method (after you are finished getting upset). First, you make an observation: my car won’t start! The question that follows: why isn’t it working?

Step 2: The Hypothesis

The next step is making a hypothesis, based on prior knowledge. A hypothesis is an “uncertain explanation” or an unproven conjecture that seeks to explain some phenomenon based on knowledge obtained while executing subsequent experiments or observations. Generally, scientists develop multiple hypotheses to address their questions and test them systematically.

All hypotheses must meet certain criteria for the scientific process to work. First, a hypothesis must be testable and falsifiable. This aspect of the hypothesis is critical and of much greater importance than the hypothesis being correct. A testable hypothesis is one that generates testable predictions, addressed through observations or experiments. A falsifiable hypothesis is one that, through observation of conflicting outcomes, can be proven wrong. This allows investigators to gain more confidence over time, not by accumulating evidence showing that a hypothesis is correct, but rather by showing that situations that could establish its falsity do not occur.

Hypotheses come in two forms: null hypotheses and alternative hypotheses. The null hypothesis is tested against the alternative hypothesis and reflects that there will be no observed change in the experiment. The alternative hypothesis is generally the one described in the previous two paragraphs, also referred to as the experimental hypothesis. The alternative hypothesis is the predicted outcome of the experiment. If the null hypothesis is rejected, then this builds evidence for the alternative hypothesis.

An everyday example: Maybe it is freezing outside and therefore it is fairly likely that your car battery is dead. Maybe you know you were low on gas the night before and therefore it is likely that the tank is empty.

Step 3: Experimentation and Data Collection

Either way, the next step is to make more observations or to conduct experiments leading to conclusions. Following the formulation of hypotheses, scientists plan and conduct experiments to test their hypotheses. These experiments provide data that will either support or falsify the hypothesis. Data can be collected from quantitative or qualitative observations. Qualitative information refers to observations that can be made simply using one's senses, be that through sight, sound, taste, smell, or touch. In contrast, quantitative observations are ones in which precise measurements of some type are used to investigate one's hypothesis.

An experiment is a procedure designed to determine whether observations of the real world agree with or refute the derived predictions in the hypothesis. If evidence from an experiment supports a hypothesis, that gives the hypothesis more credibility. This does not indicate that the hypothesis is true, as future experiments may reveal new information about the original hypothesis. Experimental design is another critical step in the scientific method and can have a great effect on the results and conclusions one draws from an experiment. Careful thought and time should be devoted to experimental design and minimizing possible errors. The experiment should be designed so that every variable or factor that could influence the outcome of the experiment be under control of the researcher. Two types of variables are used to describe the conditions in an experiment: the independent and the dependent, or response, variable. The independent variable is directly manipulated or controlled by the scientist and is generally what one predicts will affect the dependent variable. The dependent, or response, variable thus depends on the value of the independent variable. Experiments are generally designed so that one specific factor is manipulated in the experiment in order to illuminate cause and effect relationships.

An everyday example: Does the car still have all of its parts? Is this the right key? What does the gas gauge say? Does a jump start help?

Another important aspect in experimental design is the role of the control treatment, which represents a non-manipulated treatment condition. The control treatment is kept in the same conditions as the experimental treatment, but the experimental manipulation is not applied to the control. For example, if a researcher were testing the effects of soil salinity on plant growth, the soil in the control treatment would have no added salt. The control provides a baseline of “normal” conditions with which to compare the experimental treatments.

Experimental design should also incorporate replicates of each treatment. Repeatability of experimental results is an important part of the scientific method that ensures the validity and accuracy of data. It is quite difficult to control all aspects of an experiment so there is inherent variation in results that cannot be controlled for even under the most carefully designed and controlled experiments. Having replicates enables an investigator to estimate this inherent variation in results. Precise recording and measurement of data is also of great importance for ensuring the accuracy of results and the conclusions one draws from the results.

Step 4: Results and Data Analysis

The next step in the scientific method involves determining what the results from the experiment mean. Scientists compare the predictions of their null hypothesis to that of their alternative hypothesis to determine if they are able to reject the null hypothesis. Rejecting the null hypothesis means that there is a significant probability that values of the dependent variable in the control versus experimental treatments are not equal to each other. If significant differences exist, then one can reject the null hypothesis and accept the alternative hypothesis. Conversely, the investigator may fail to reject the null hypothesis, meaning the treatment has no effect on the results. Before scientists can make any claims about their null hypothesis from their experimental data or observations, statistical tests are required to ensure the validity of the data and further interpretation of the data. Statistical tests allow researchers to determine if there are genuine differences between the control and experimental treatments. From there, they can create figures and tables to illustrate their findings.

Step 5: Conclusions

The last portion of the scientific method involves providing explanations of the results and the conclusions that can be logically drawn from the results. Generally, this step of the scientific process also requires one to revisit scientific literature and compare their results with other experiments or observations on related topics. This allows researchers to put their experiment in a more general context and elaborate on the significance of particular results. Additionally, it allows them to explain how their work fits into a larger context in their discipline.

The scientific process does not stop here! The scientific process works through time as knowledge on topics in science accumulate and drive our understanding of particular mechanisms or processes explaining natural phenomena. If we fail to reject our null hypothesis, then it becomes necessary to revisit the initial stages of the scientific method and try to reformulate our questions and understand why an anticipated result was not attained.

Application of the Scientific Method

The only difference between the use of this method in every-day life and in the laboratory is that scientists carefully document their work, from observation to hypothesis to experiment, and finally conclusions and peer review. In addition, unlike problem solving outside the lab, the scientific method in the lab includes controlled conditions and variables.

Let’s investigate the scientific method using an example from the lab. It is known that plant growth is affected by microbes, such as bacteria and fungi, living in their soil. It is possible to figure out what microbes have which effects by potting plants in completely sterile soil, then adding in microbes one at a time, or in different combinations and measuring the growth of the plant. Now let’s fit this into the terms used to describe the scientific method:

Observation and Question : There are microbes in the soil…do these affect plant growth?

HYPOTHESES:

Experimental: One particular microbe of interest will cause the plants to grow more slowly.

Null: The presence or absence of microbes will have no effect on plant growth

Experiment : set up groups of plants in 1) sterile soil, 2) soil with the microbe added in, and 3) natural soil. Measure the growth of the plants over time, using a ruler.

Conclusion : if the plants in group 2 grow more slowly than the other two, the hypothesis is supported. This needs to be backed up with statistical analysis from many plants to be considered significant. An experiment like this is not legitimate with just one plant per group.

Group 1 is a control which shows the plants can grow in the sterile soil. Group 3 is a control that shows the plants can grow under normal conditions. Group 2 is the experimental group. It would be possible to add different amounts of the microbe, or different microbes, to introduce more variables. The main point is that the researcher has something to which to compare the experimental group- the control group. If the experiment included only group 2 and the researcher determined that the plants “looked sick,” that would be a matter of opinion. The only way to make that observation scientific is to have healthy plants to measure. The type or amount of microbe used is the independent variable , because the researcher has control over it. The size of the plant at the end of the experiment is the dependent or response variable because it is the result.

Ultimately, work like this is published in scientific journals so that other researchers can read about the methods used and conclusions drawn. Publications like this are subject to peer-review, which means that an article won’t be published in a journal until other researchers have checked it out and agree it is well-done. As a community of scientists, general concepts are developed based on observed patterns in the experiments that individual scientists conduct. This results in the development of a scientific theory . This term means that there is a consensus among researchers that a particular concept or process exists. It is important to note that the word theory does not mean the same thing as hypothesis. Once scientists label a concept with this term, it is considered to be true, considering all currently available data. Of course, if a large body of experimentation demonstrates information to the contrary, theories can be modified.

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72 Easy Science Experiments Using Materials You Already Have On Hand

Because science doesn’t have to be complicated.

If there is one thing that is guaranteed to get your students excited, it’s a good science experiment! While some experiments require expensive lab equipment or dangerous chemicals, there are plenty of cool projects you can do with regular household items. We’ve rounded up a big collection of easy science experiments that anybody can try, and kids are going to love them!

Easy Chemistry Science Experiments

Easy physics science experiments, easy biology and environmental science experiments, easy engineering experiments and stem challenges.

1. Taste the Rainbow

Teach your students about diffusion while creating a beautiful and tasty rainbow! Tip: Have extra Skittles on hand so your class can eat a few!

Learn more: Skittles Diffusion

2. Crystallize sweet treats

Crystal science experiments teach kids about supersaturated solutions. This one is easy to do at home, and the results are absolutely delicious!

Learn more: Candy Crystals

3. Make a volcano erupt

This classic experiment demonstrates a chemical reaction between baking soda (sodium bicarbonate) and vinegar (acetic acid), which produces carbon dioxide gas, water, and sodium acetate.

Learn more: Best Volcano Experiments

4. Make elephant toothpaste

This fun project uses yeast and a hydrogen peroxide solution to create overflowing “elephant toothpaste.” Tip: Add an extra fun layer by having kids create toothpaste wrappers for plastic bottles.

5. Blow the biggest bubbles you can

Add a few simple ingredients to dish soap solution to create the largest bubbles you’ve ever seen! Kids learn about surface tension as they engineer these bubble-blowing wands.

Learn more: Giant Soap Bubbles

6. Demonstrate the “magic” leakproof bag

All you need is a zip-top plastic bag, sharp pencils, and water to blow your kids’ minds. Once they’re suitably impressed, teach them how the “trick” works by explaining the chemistry of polymers.

Learn more: Leakproof Bag

7. Use apple slices to learn about oxidation

Have students make predictions about what will happen to apple slices when immersed in different liquids, then put those predictions to the test. Have them record their observations.

Learn more: Apple Oxidation

8. Float a marker man

Their eyes will pop out of their heads when you “levitate” a stick figure right off the table! This experiment works due to the insolubility of dry-erase marker ink in water, combined with the lighter density of the ink.

Learn more: Floating Marker Man

9. Discover density with hot and cold water

There are a lot of easy science experiments you can do with density. This one is extremely simple, involving only hot and cold water and food coloring, but the visuals make it appealing and fun.

Learn more: Layered Water

10. Layer more liquids

This density demo is a little more complicated, but the effects are spectacular. Slowly layer liquids like honey, dish soap, water, and rubbing alcohol in a glass. Kids will be amazed when the liquids float one on top of the other like magic (except it is really science).

Learn more: Layered Liquids

11. Grow a carbon sugar snake

Easy science experiments can still have impressive results! This eye-popping chemical reaction demonstration only requires simple supplies like sugar, baking soda, and sand.

Learn more: Carbon Sugar Snake

12. Mix up some slime

Tell kids you’re going to make slime at home, and watch their eyes light up! There are a variety of ways to make slime, so try a few different recipes to find the one you like best.

13. Make homemade bouncy balls

These homemade bouncy balls are easy to make since all you need is glue, food coloring, borax powder, cornstarch, and warm water. You’ll want to store them inside a container like a plastic egg because they will flatten out over time.

Learn more: Make Your Own Bouncy Balls

14. Create eggshell chalk

Eggshells contain calcium, the same material that makes chalk. Grind them up and mix them with flour, water, and food coloring to make your very own sidewalk chalk.

Learn more: Eggshell Chalk

15. Make naked eggs

This is so cool! Use vinegar to dissolve the calcium carbonate in an eggshell to discover the membrane underneath that holds the egg together. Then, use the “naked” egg for another easy science experiment that demonstrates osmosis .

Learn more: Naked Egg Experiment

16. Turn milk into plastic

This sounds a lot more complicated than it is, but don’t be afraid to give it a try. Use simple kitchen supplies to create plastic polymers from plain old milk. Sculpt them into cool shapes when you’re done!

17. Test pH using cabbage

Teach kids about acids and bases without needing pH test strips! Simply boil some red cabbage and use the resulting water to test various substances—acids turn red and bases turn green.

Learn more: Cabbage pH

18. Clean some old coins

Use common household items to make old oxidized coins clean and shiny again in this simple chemistry experiment. Ask kids to predict (hypothesize) which will work best, then expand the learning by doing some research to explain the results.

Learn more: Cleaning Coins

19. Pull an egg into a bottle

This classic easy science experiment never fails to delight. Use the power of air pressure to suck a hard-boiled egg into a jar, no hands required.

Learn more: Egg in a Bottle

20. Blow up a balloon (without blowing)

Chances are good you probably did easy science experiments like this when you were in school. The baking soda and vinegar balloon experiment demonstrates the reactions between acids and bases when you fill a bottle with vinegar and a balloon with baking soda.

21 Assemble a DIY lava lamp

This 1970s trend is back—as an easy science experiment! This activity combines acid-base reactions with density for a totally groovy result.

22. Explore how sugary drinks affect teeth

The calcium content of eggshells makes them a great stand-in for teeth. Use eggs to explore how soda and juice can stain teeth and wear down the enamel. Expand your learning by trying different toothpaste-and-toothbrush combinations to see how effective they are.

Learn more: Sugar and Teeth Experiment

23. Mummify a hot dog

If your kids are fascinated by the Egyptians, they’ll love learning to mummify a hot dog! No need for canopic jars , just grab some baking soda and get started.

24. Extinguish flames with carbon dioxide

This is a fiery twist on acid-base experiments. Light a candle and talk about what fire needs in order to survive. Then, create an acid-base reaction and “pour” the carbon dioxide to extinguish the flame. The CO2 gas acts like a liquid, suffocating the fire.

25. Send secret messages with invisible ink

Turn your kids into secret agents! Write messages with a paintbrush dipped in lemon juice, then hold the paper over a heat source and watch the invisible become visible as oxidation goes to work.

Learn more: Invisible Ink

26. Create dancing popcorn

This is a fun version of the classic baking soda and vinegar experiment, perfect for the younger crowd. The bubbly mixture causes popcorn to dance around in the water.

27. Shoot a soda geyser sky-high

You’ve always wondered if this really works, so it’s time to find out for yourself! Kids will marvel at the chemical reaction that sends diet soda shooting high in the air when Mentos are added.

Learn more: Soda Explosion

28. Send a teabag flying

Hot air rises, and this experiment can prove it! You’ll want to supervise kids with fire, of course. For more safety, try this one outside.

Learn more: Flying Tea Bags

29. Create magic milk

This fun and easy science experiment demonstrates principles related to surface tension, molecular interactions, and fluid dynamics.

Learn more: Magic Milk Experiment

30. Watch the water rise

Learn about Charles’s Law with this simple experiment. As the candle burns, using up oxygen and heating the air in the glass, the water rises as if by magic.

Learn more: Rising Water

31. Learn about capillary action

Kids will be amazed as they watch the colored water move from glass to glass, and you’ll love the easy and inexpensive setup. Gather some water, paper towels, and food coloring to teach the scientific magic of capillary action.

Learn more: Capillary Action

32. Give a balloon a beard

Equally educational and fun, this experiment will teach kids about static electricity using everyday materials. Kids will undoubtedly get a kick out of creating beards on their balloon person!

Learn more: Static Electricity

33. Find your way with a DIY compass

Here’s an old classic that never fails to impress. Magnetize a needle, float it on the water’s surface, and it will always point north.

Learn more: DIY Compass

34. Crush a can using air pressure

Sure, it’s easy to crush a soda can with your bare hands, but what if you could do it without touching it at all? That’s the power of air pressure!

35. Tell time using the sun

While people use clocks or even phones to tell time today, there was a time when a sundial was the best means to do that. Kids will certainly get a kick out of creating their own sundials using everyday materials like cardboard and pencils.

Learn more: Make Your Own Sundial

36. Launch a balloon rocket

Grab balloons, string, straws, and tape, and launch rockets to learn about the laws of motion.

37. Make sparks with steel wool

All you need is steel wool and a 9-volt battery to perform this science demo that’s bound to make their eyes light up! Kids learn about chain reactions, chemical changes, and more.

Learn more: Steel Wool Electricity

38. Levitate a Ping-Pong ball

Kids will get a kick out of this experiment, which is really all about Bernoulli’s principle. You only need plastic bottles, bendy straws, and Ping-Pong balls to make the science magic happen.

39. Whip up a tornado in a bottle

There are plenty of versions of this classic experiment out there, but we love this one because it sparkles! Kids learn about a vortex and what it takes to create one.

Learn more: Tornado in a Bottle

40. Monitor air pressure with a DIY barometer

This simple but effective DIY science project teaches kids about air pressure and meteorology. They’ll have fun tracking and predicting the weather with their very own barometer.

Learn more: DIY Barometer

41. Peer through an ice magnifying glass

Students will certainly get a thrill out of seeing how an everyday object like a piece of ice can be used as a magnifying glass. Be sure to use purified or distilled water since tap water will have impurities in it that will cause distortion.

Learn more: Ice Magnifying Glass

42. String up some sticky ice

Can you lift an ice cube using just a piece of string? This quick experiment teaches you how. Use a little salt to melt the ice and then refreeze the ice with the string attached.

Learn more: Sticky Ice

43. “Flip” a drawing with water

Light refraction causes some really cool effects, and there are multiple easy science experiments you can do with it. This one uses refraction to “flip” a drawing; you can also try the famous “disappearing penny” trick .

Learn more: Light Refraction With Water

44. Color some flowers

We love how simple this project is to re-create since all you’ll need are some white carnations, food coloring, glasses, and water. The end result is just so beautiful!

45. Use glitter to fight germs

Everyone knows that glitter is just like germs—it gets everywhere and is so hard to get rid of! Use that to your advantage and show kids how soap fights glitter and germs.

Learn more: Glitter Germs

46. Re-create the water cycle in a bag

You can do so many easy science experiments with a simple zip-top bag. Fill one partway with water and set it on a sunny windowsill to see how the water evaporates up and eventually “rains” down.

Learn more: Water Cycle

47. Learn about plant transpiration

Your backyard is a terrific place for easy science experiments. Grab a plastic bag and rubber band to learn how plants get rid of excess water they don’t need, a process known as transpiration.

Learn more: Plant Transpiration

48. Clean up an oil spill

Before conducting this experiment, teach your students about engineers who solve environmental problems like oil spills. Then, have your students use provided materials to clean the oil spill from their oceans.

Learn more: Oil Spill

49. Construct a pair of model lungs

Kids get a better understanding of the respiratory system when they build model lungs using a plastic water bottle and some balloons. You can modify the experiment to demonstrate the effects of smoking too.

Learn more: Model Lungs

50. Experiment with limestone rocks

Kids  love to collect rocks, and there are plenty of easy science experiments you can do with them. In this one, pour vinegar over a rock to see if it bubbles. If it does, you’ve found limestone!

Learn more: Limestone Experiments

51. Turn a bottle into a rain gauge

All you need is a plastic bottle, a ruler, and a permanent marker to make your own rain gauge. Monitor your measurements and see how they stack up against meteorology reports in your area.

Learn more: DIY Rain Gauge

52. Build up towel mountains

This clever demonstration helps kids understand how some landforms are created. Use layers of towels to represent rock layers and boxes for continents. Then pu-u-u-sh and see what happens!

Learn more: Towel Mountains

53. Take a play dough core sample

Learn about the layers of the earth by building them out of Play-Doh, then take a core sample with a straw. ( Love Play-Doh? Get more learning ideas here. )

Learn more: Play Dough Core Sampling

54. Project the stars on your ceiling

Use the video lesson in the link below to learn why stars are only visible at night. Then create a DIY star projector to explore the concept hands-on.

Learn more: DIY Star Projector

55. Make it rain

Use shaving cream and food coloring to simulate clouds and rain. This is an easy science experiment little ones will beg to do over and over.

Learn more: Shaving Cream Rain

56. Blow up your fingerprint

This is such a cool (and easy!) way to look at fingerprint patterns. Inflate a balloon a bit, use some ink to put a fingerprint on it, then blow it up big to see your fingerprint in detail.

57. Snack on a DNA model

Twizzlers, gumdrops, and a few toothpicks are all you need to make this super-fun (and yummy!) DNA model.

Learn more: Edible DNA Model

58. Dissect a flower

Take a nature walk and find a flower or two. Then bring them home and take them apart to discover all the different parts of flowers.

59. Craft smartphone speakers

No Bluetooth speaker? No problem! Put together your own from paper cups and toilet paper tubes.

Learn more: Smartphone Speakers

60. Race a balloon-powered car

Kids will be amazed when they learn they can put together this awesome racer using cardboard and bottle-cap wheels. The balloon-powered “engine” is so much fun too.

Learn more: Balloon-Powered Car

61. Build a Ferris wheel

You’ve probably ridden on a Ferris wheel, but can you build one? Stock up on wood craft sticks and find out! Play around with different designs to see which one works best.

Learn more: Craft Stick Ferris Wheel

62. Design a phone stand

There are lots of ways to craft a DIY phone stand, which makes this a perfect creative-thinking STEM challenge.

63. Conduct an egg drop

Put all their engineering skills to the test with an egg drop! Challenge kids to build a container from stuff they find around the house that will protect an egg from a long fall (this is especially fun to do from upper-story windows).

Learn more: Egg Drop Challenge Ideas

64. Engineer a drinking-straw roller coaster

STEM challenges are always a hit with kids. We love this one, which only requires basic supplies like drinking straws.

Learn more: Straw Roller Coaster

65. Build a solar oven

Explore the power of the sun when you build your own solar ovens and use them to cook some yummy treats. This experiment takes a little more time and effort, but the results are always impressive. The link below has complete instructions.

Learn more: Solar Oven

66. Build a Da Vinci bridge

There are plenty of bridge-building experiments out there, but this one is unique. It’s inspired by Leonardo da Vinci’s 500-year-old self-supporting wooden bridge. Learn how to build it at the link, and expand your learning by exploring more about Da Vinci himself.

Learn more: Da Vinci Bridge

67. Step through an index card

This is one easy science experiment that never fails to astonish. With carefully placed scissor cuts on an index card, you can make a loop large enough to fit a (small) human body through! Kids will be wowed as they learn about surface area.

68. Stand on a pile of paper cups

Combine physics and engineering and challenge kids to create a paper cup structure that can support their weight. This is a cool project for aspiring architects.

Learn more: Paper Cup Stack

69. Test out parachutes

Gather a variety of materials (try tissues, handkerchiefs, plastic bags, etc.) and see which ones make the best parachutes. You can also find out how they’re affected by windy days or find out which ones work in the rain.

Learn more: Parachute Drop

70. Recycle newspapers into an engineering challenge

It’s amazing how a stack of newspapers can spark such creative engineering. Challenge kids to build a tower, support a book, or even build a chair using only newspaper and tape!

Learn more: Newspaper STEM Challenge

71. Use rubber bands to sound out acoustics

Explore the ways that sound waves are affected by what’s around them using a simple rubber band “guitar.” (Kids absolutely love playing with these!)

Learn more: Rubber Band Guitar

72. Assemble a better umbrella

Challenge students to engineer the best possible umbrella from various household supplies. Encourage them to plan, draw blueprints, and test their creations using the scientific method.

Learn more: Umbrella STEM Challenge

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Assessing Scientific Inquiry: A Systematic Literature Review of Tasks, Tools and Techniques

• Theoretical Studies
• Open access
• Published: 04 September 2024

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• De Van Vo   ORCID: orcid.org/0000-0002-8515-0221 1 &
• Geraldine Mooney Simmie   ORCID: orcid.org/0000-0002-5026-4261 1  

While national curricula in science education highlight the importance of inquiry-based learning, assessing students’ capabilities in scientific inquiry remains a subject of debate. Our study explored the construction, developmental trends and validation techniques in relation to assessing scientific inquiry using a systematic literature review from 2000 to 2024. We used PRISMA guidelines in combination with bibliometric and Epistemic Network Analyses. Sixty-three studies were selected, across all education sectors and with a majority of studies in secondary education. Results showed that assessing scientific inquiry has been considered around the world, with a growing number (37.0%) involving global researcher networks focusing on novel modelling approaches and simulation performance in digital-based environments. Although there was modest variation between the frameworks, studies were mainly concerned with cognitive processes and psychological characteristics and were reified from wider ethical, affective, intersectional and socio-cultural considerations. Four core categories (formulating questions/hypotheses, designing experiments, analysing data, and drawing conclusions) were most often used with nine specific components (formulate questions formulate prediction/hypotheses, set experiment, vary independent variable, measure dependent variable, control confounding variables, describe data, interpret data, reach reasonable conclusion). There was evidence of transitioning from traditional to online modes, facilitated by interactive simulations, but the independent tests and performance assessments, in both multiple-choice and open-ended formats remained the most frequently used approach with a greater emphasis on context than heretofore. The findings will be especially useful for science teachers, researchers and policy decision makers with an active interest in assessing capabilities in scientific inquiry.

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Introduction

In contemporary times as more information and knowledge are created in a shorter timeline, the need for scientific literacy and inquiry-based capabilities beyond nature of science is increasing, especially in relation to the pressing needs of the wider world (Erduran, 2014 ). This is a growing concern, in relation to the future survival of humanity and sustainability of the planet for the reconceptualization of science education for epistemic justice and the foregrounding of intersectionality (Wallace et al., 2022 ). At the same time, policymakers and employers demand 21st century skills and inquiry-oriented approaches that include creativity, critical thinking, collaboration, communication and digital competencies (Binkley et al., 2012 ; Chu et al., 2017 ; Voogt & Roblin, 2012 ). Rather than teaching extensive content knowledge, there is a policy imperative to teach skills, dispositions, literacies and inquiry-oriented competencies. Mastery of capabilities, such as inquiry-oriented learning has therefore become a core outcome of national science education curricula globally (Baur et al., 2022 ).

Inquiry orientations are continuously emphasized in science education by the Organisation for Economic Cooperation and Development (OECD) operating in more than forty countries globally (OECD, 2015 , 2017 ) in the US (National Research Council [NRC], 2000 ), in Europe (European Commission and Directorate-General for Research and Innovation, 2015 ), and in nation states, such as in Ireland with the National Council for Curriculum and Assessment (NCCA, 2015 ).

The policy imperative for inquiry-oriented activities in science classrooms prompts a growing interest in assessing students’ scientific inquiry capabilities. While scientific inquiry is a well-established research area in science education (Fukuda et al., 2022 ), assessing students’ scientific inquiry capabilities is a growing topic of research, innovation and consideration.

There is a growing demand for innovative assessments that aim to either enhance or replace traditional summative methods. These assessments should focus on creating customized, student-centered formative tasks, tools, and techniques that capture both the final products and the processes used to achieve them (Hattie & Timperley, 2007 ). Many researchers argue that traditional models, originally designed to measure content knowledge, are no longer adequate for assessing competencies. Griffin et al. ( 2012 ) argued that traditional methods lack the ability to measure the higher-order skills, dispositions, and knowledge requirements of collaborative learning. Instead, it is asserted that modes of formative assessment can provide teachers and students with diagnostic information in order to continually adapt instruction and to foster a pedagogical cycle of learning (Kruit et al., 2018 ; Voogt & Roblin, 2012 ).

In this study, we systematically examined the construction, developmental trends and validation tasks, tools and techniques used in assessing students’ scientific inquiry capabilities in educational settings. We combined a systematic literature review from 2000 to 2024, using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines with Bibliometric (Diodato & Gellatly, 2013 ) and Epistemic Network Analyses (ENA) (Shaffer et al., 2016 ). Our aim was to illuminate current global trends, possibilities and challenges in relation to the assessment of scientific inquiry and to suggest potential spaces for future research. Our study was guided by the following three research questions:

RQ1: To what extent is research on assessment of scientific inquiry in educational contexts found in the international literature?

RQ2: What are the predominant components, tasks, tools, and techniques used to assess scientific inquiry?

RQ3: What are the trends and developments in the assessment of scientific inquiry?

We structured the paper as follows. First, we briefly interrogate current conceptualisations of inquiry-based learning and scientific inquiry as an important background to the study. Second, we justify our selected methodology, the use of a systematic literature review with bibliometric and ENA analyses. Third, we present the results from each research question in turn. Finally, we discuss the changing shape of this research domain and the implications for the future of science education.

Conceptualizations of Scientific Inquiry

Here we first explore the construct of inquiry-based learning in science education before considering something of the global policy imperatives underway in this regard.

Inquiry-based Approach in Science Education

In science education, two visions of scientific literacy are discussed: Vision I emphasizes scientific content and propositional knowledge, while Vision II focuses on engaging students with real-world applications of science knowledge (Roberts, 2007 ; Roberts & Bybee, 2014 ). Achieving the scientific literacy mentioned in Vision II literacy is a key challenge for 21st-century science education, shifting towards enabling individuals to apply scientific concepts in everyday life rather than solely producing ‘mini-scientists’ (Roberts & Bybee, 2014 ). Balancing these visions is crucial to meeting diverse student needs and enhancing understanding science-in-context in today’s highly scientific world (Roberts & Bybee, 2014 ). Scientific inquiry is considered fundamental to scientific literacy, encompassing practices and epistemology, with a growing focus on the meaning, application and contexts of real world inquiry (Schwartz et al., 2023 ).

An inquiry-orientation therefore provides a pedagogical approach in which students learn by actively using scientific methods to reason and generate explanations in relation to design, data and evidence (Anderson, 2002 ; Stender et al., 2018 ). Neumann et al. ( 2011 ) considered the Nature of Science and Scientific Inquiry as separate domains for inquiry-orientations including for analysing data, identifying and controlling variables, and forming logical cause-and‐effect relationships. Wenning ( 2007 ) proposed a detailed rubric for developing proficiency in scientific inquiry, that included identifying a problem to be investigated, doing background research, using induction, formulating a hypothesis, incorporating logic and evidence, using deduction, generating a prediction, designing experimental procedures to test the prediction, conducting a scientific experiment, observing or simulating a test or model, collecting data, organizing, and analysing data accurately and precisely, applying statistical methods to support conclusions and communicating results. Moreover, Turner et al. ( 2018 ) grouped sixteen of the activities into three components of inquiry for secondary school students in science and math classrooms, namely working with hypotheses (i.e., generation of hypotheses/predictions, designing procedures), communication in inquiry (i.e., interpreting outcomes, asking questions), hands-on inquiry (i.e., recording data, visualising data).

Pedaste et al. ( 2015 ) conceptualised an inquiry-based learning framework of four phases based on their review of thirty-two studies: orientation , conceptualization , investigation , and conclusion . The orientation phase stimulates interest and curiosity, involves background research and results in the writing of a problem statement or topic by the teacher and/or students. Conceptualization involves formulating theory-based questions as predictions or hypotheses. The investigation phase turns curiosity into action through exploration, experimentation, data gathering and interpretation. In the conclusion phase, learners address their original research questions and consider whether these questions are answered, supported or refuted.

The studies showed that the inquiry-orientation enhanced comprehension (Marshall et al., 2017 ), fostered an appreciation of the nature of scientific knowledge (Dogan et al., 2024 ), improved students’ achievement in both scientific practices and conceptual knowledge (Marshall et al., 2017 ). Inquiry-based approach was found to positively impact student engagement and motivation while the hands-on experimental skills made learning science more enjoyable (Ramnarain, 2014 ). Inquiry activities make learning visible and help to integrate scientific reasoning skills for the social construction of knowledge (Stender et al., 2018 ).

Global Policy Imperatives in Relation to Scientific Inquiry

The US National Science Education Standards presented by the National Research Council (NRC, 1996 ) defined inquiry is “a multifaceted activity that involves making observations; posing questions; examining books and other sources of information to see what is already known; planning investigations; reviewing what is already known in light of experimental evidence; using tools to gather, analyze, and interpret data; proposing answers, explanations, and predictions; and communicating the results” (p. 23). Scientific inquiry encompasses the various methods scientists use to investigate the natural world and formulate explanations grounded in evidence from their research. It also involves students’ activities where they gain knowledge and understanding of scientific concepts and learn about the processes which scientists use to explore the natural world.

Later NRC standards (2000, 2006) elaborated such proficiency as identifying a scientific question, designing and conducting an investigation, using appropriate tools to collect and analyse data, and developing evidence-based explanations. The US framework for K-12 science education (NRC, 2012 ) focused on a few core ideas and concepts, integrating them with the practices needed for scientific inquiry and engineering design. The emphasis appeared to have shifted from “inquiry” to “scientific practices” as a basis of the framework (Rönnebeck et al., 2016 ). It listed eight components of scientific and engineering practices, including asking questions, developing and using models, planning and carrying out investigations, analyzing and interpreting data, using mathematics and computational thinking, constructing explanations, engaging in argument from evidence, obtaining, evaluating, and communicating information (NRC, 2012 ). The eight practices intentionally intersect and connect with others rather than stand-alone (NRC, 2012 ; Rönnebeck et al., 2016 ).

The Twenty First Century Science program (2006) in England emphasized a broad qualitative understanding of significant “whole explanations” and placed a strong focus on Ideas about Science . It also prioritized developing the understanding and skills needed to critically evaluate scientific information encountered in everyday life. This initiative focuses on a foundational course aimed at fostering scientific literacy among all students. It emphasized equipping students with the knowledge and skills needed to critically evaluate scientific information encountered in daily life​. This connects science to real-world contexts and applications, and the big ideas of science rather than isolated facts​ (Millar, 2006 ).

The 2015 Programme for International Student Assessment (PISA) specified a number of essential science inquiry competencies in three key areas: explaining phenomena scientifically, interpreting data and evidence scientifically, and evaluating and designing scientific inquiry (OECD, 2017 ). The explaining phenomena dimension involves students being able to identify, provide, and assess explanations for a variety of natural and technological phenomena. The interpreting dimension means that students can describe and evaluate scientific investigations and suggest methods for scientifically addressing questions. The designing dimension refers to students who can analyse and assess claims and arguments presented in various forms and draw accurate scientific conclusions (OECD, 2017 ).

In the 21st-century vision for science education in Europe, involving citizens as active participants in inquiry-oriented learning was essential (European Commission and Directorate-General for Research and Innovation, 2015 ). The scientific inquiry involves students identifying research problems and finding solutions that apply science to everyday life. Inquiry-based science education engages students in problem-based learning, hands-on experiments, self-regulated learning, and collaborative discussion, fostering a deep understanding of science and awareness of the practical applications of scientific concepts.

In summary, global policy imperatives focus on enhancing the cognitive processes and psychological characteristics of scientific inquiry and its application in real-world contexts. This approach consistently emphasizes inquiry as fundamental to teaching and learning science, although the focus has varied over time between Vision I and Vision II in relation to scientific literacy and science education.

Methodology

For the systematic literature review, we used the PRISMA methodology (Moher et al., 2009 ) in order to assemble an evidence base of relevant studies. This was further supported by Bibliometric analysis (Diodato & Gellatly, 2013 ) and ENA analysis (Shaffer et al., 2016 ). Bibliometric analysis is a quantitative method used to evaluate various aspects of academic publications within a specified field of study. It involves the application of mathematical and statistical tools to analyse patterns, and impact within a defined body of literature. It is a powerful tool for analysing the knowledge framework and structure in a specific research area (Diodato & Gellatly, 2013 ). Meanwhile, ENA is an analytical method to describe individual (group) framework patterns through quantitative analysis of data by examining the structure of the co-occurrence or connections in coded data (Shaffer et al., 2016 ). ENA can be used to compare units of analysis in terms of their plotted point positions, individual networks, mean plotted point positions, and mean networks, which average the connection weights across individual networks. This approach has been applied in several fields, including educational research (Ruis & Lee, 2021 ).

A comprehensive examination of extant literature was undertaken using the PRISMA-framework stages, with a specific focus on empirical research. The criterion for article selection was predicated on the utilization of a testing instrument for assessment of scientific inquiry. The inclusion criteria were threefold. Firstly, empirical studies that assessed the information retrieval abilities of students - qualitative, quantitative, or mixed methods - were considered. Secondly, the selected studies were required to incorporate scientific inquiry assessment tasks for K-12 science education. Thirdly, the chosen articles were limited to those originally published in the English language and within a timeline from 2000 to 2024 (09/06/2024).

We conducted a systematic search for academic papers in electronic databases as presented in Fig. 1 , employing specific search terms in the title, keywords, and abstract sections: (“inquiry” OR “scientific inquiry” OR “science inquiry” OR “investigation skill”) AND (“assessment” OR “testing” OR “measurement” OR “computer-based assessment”) AND NOT (“review”). The review used two scientific databases: Scopus and Web of Science (WoS). The results in Scopus and WoS suggested 2228 and 1532 references respectively through the first search strategy. After merging the two datasets based on articles’ DOIs indices, as well as following the removal of duplicate entries, we reached 589 articles. We continued to check the titles and abstracts of the remaining articles for pre-selection purposes based on our predefined inclusion criteria. The process led to the identification of 263 papers for further consideration. In this stage, the authors further discussed and agreed on the inclusion criteria, content relevance, methodological quality, and methodological relevance for the selection of papers. We also facilitated discussions among raters to build consensus on ambiguous cases. Finally, we ended up with sixty-three articles selected for our dataset. Then, the data were manually entered one by one, coded and documented for final selection.

Flowchart of the inclusion and exclusion process following PRISMA guidelines

To server our research questions, we collected information from the selected articles as a dataset for thematic analysis in the PRISMA framework. This information included: (1) year of publication, (2) age groups of the participants (categorized into four age groups: 5–10 years, 11–15 ages and 16–18 ages, (3) study context, (4) components of scientific inquiry, (5) instruments/tests, and (6) technique/validation approaches. (Readers can access full raw data at https://osf.io/5bt82 ).

For bibliometric analysis, the data of the selected articles was exported from the Scopus platform. It involved common bibliographical information such authors, title, year, DOI, affiliation, abstract, keyword and reference. We used bibliometric analysis via R software version 4.2.3 (R Core Team, 2023 ) with shiny (Chang et al., 2023 ) and bibliometrix package (Aria & Cuccurullo, 2017 ).

To facilitate for ENA analysis, we coded the data regarding components of scientific inquiry, based on existing frameworks (Table 1 ). The analyses were employed via ENA Web Tool (Marquart et al., 2018 ).

The results are presented here in relation to the key research questions. First, we present surface characteristics that provide a general overview of empirical studies on assessing scientific inquiry worldwide. Then, we explore the components, constructs, and techniques most often used in these assessments across the empirical studies with specific illustrative examples highlighted. Finally, we review the results to identify trends and developments in the assessment of scientific inquiry over time.

Studies on Measuring Scientific Inquiry in School Contexts Worldwide

The 63 selected articles comprised a total of 189 authors, with only four single-author articles (Kind, 2013 ; Mutlu, 2020 ; Sarıoğlu, 2023 ; Teig, 2024 ). Bibliometric analysis showed 3194 references cited, while international co-author index and co-author per article was 17.46% and 3.62, respectively. There were 21 papers published from 2000 to 2012. This number more than doubled to 42 articles from 2013 to 2024. The articles were published in 29 journals, with the core source recognized for the International Journal of Science Education ( IJSE) (11 articles) and the Journal of Research in Science Teaching (JRST) (10 articles), followed by the International Journal of Science and Mathematics Education ( IJSME) (7 articles), and the Research in Science and Technological Education (RSTE) (3 articles). Figure 2 depicts the cumulative articles of the core sources’ production during the period from 2000 to 2024. The graph shows the major journals contributing to this field of study (IJSE, JRST and IJSME), and the noticeable growth curve in the last decade.

The cumulative occurrence of articles in key journals published over time

The findings showed that the 63 articles have a global reach, with study contexts spanning 19 different countries and territories. Notably, a high proportion of studies (23 articles, 36.5%) come from the United States, followed by Taiwan (9 articles, 14.3%), Turkey (5 articles, 7.9%), and Germany (4 articles, 6.3%), while Israel and China each contributed 3 studies (4.8%). The distribution indicates that assessing scientific inquiry is a relatively attractive area of research in science education at a global level.

Regarding affiliation contribution, Fig. 3 shows that five universities emerge as the significant contributors to this collection of publications. Among these institutions, two are located in the US: The University of California (UC) and the Caltech Precollege Science Initiative (CAPSI). UC has remained consistently active in the field since 2002, while CAPSI’s involvement has stagnated since 2005. Humboldt University in Berlin (HU-Berlin) began contributing in 2012. Meanwhile, the National Taiwan Normal University (NTNU) has been actively contributing since 2013, with a sharp increase in activity. Beijing Normal University (BNU) entered the research landscape later, but has shown a steady increase in contributions recently. It is noted that the contributions refer to the frequency distribution of affiliations of all co-authors for each paper (Aria & Cuccurullo, 2017 ).

Top 5 of the research institution contribution over time

With respect to collaboration network in the research field, Fig. 4 represents collaborative patterns among researchers in selected articles, covering author and country levels. Based on the studies selected, the analysis identified 11 distinct research networks, illustrated in Fig. 4 a, that present as networks with a significant number of researchers. For instance, in the networks, we can find research groups such as the ones led by Wu, Linn, and Gobert. Furthermore, Fig. 4 b shows that the United States play a pivotal role in leading out international collaborations within the field of scientific inquiry assessment.

Collaboration networks of researchers identified in the articles selected

The cumulative participant count involved in all the studies totalled 50,470 individuals, encompassing educational levels from primary to high schools. Participant categorization was contingent upon respective age group, with a predominant focus on students at age range of 11–15 years. Notably, more than half of the studies (36 studies, accounting for 57.1%) were centred on participants in this age range. Following closely, another significant portion, comprising 23 studies (36.5%), targeted students in the 16-18-year students. It was noted that there are seven studies assessing students, covering two age range groups.

Task, Tests and Techniques of Assessing Scientific Inquiry

Components (facets) for assessing scientific inquiry.

In empirical studies selected, various assessment frameworks were introduced to evaluate scientific inquiry, each incorporating a diverse range of specific components. Zachos et al. ( 2000 ) considered scientific inquiry as multi-aspects of competence related to human cognitive characteristics. They employed hands-on performance assessment tasks, Floating and Sinking and the Period of Oscillation of a Pendulum, to assess students’ inquiry abilities within specific components: linking theory with evidence, formulating hypotheses, maintaining records, using appropriate or innovative laboratory materials, identifying cause-and-effect relationships, controlling experiments, and applying parsimony in drawing conclusions.

Cuevas and colleages ( 2005 ) developed contextual problem tasks to assess inquiry in five components: questioning, planning, implementing, concluding, and reporting. Their assessment task described a story about a child named Marie, who was trying to determine if the size of a container’s opening would influence the rate at which water evaporated. Students were asked to formulate a question reflecting the problem Marie was trying to solve, develop a hypothesis, design an investigation, list the materials needed, describe how to record results, and explain how to draw a conclusion. The framework were referred in a study by Turkan and Liu ( 2012 ) and later utilized in a study by Yang et al. ( 2016 ), where science inquiry was defined as comprising seven aspects of identifying a research question, formulating a hypothesis, designing an experimental procedure, planning necessary equipment and materials, collecting data and evidence, drawing evidence-based conclusions, and constructing conceptual understanding.

Other studies described inquiry as process skills (Kipnis & Hofstein, 2008 ), science process skills (Feyzíoglu, 2012 ; Temiz et al., 2006 ) and scientific process skills (Tosun, 2019 ). For example, Temiz et al. ( 2006 ) developed an instrument aimed to measure the development of 12 science process skills: formulating hypotheses, observing, manipulating materials, measuring, identifying and controlling variables, recording the data, demonstrating the ability to use numbers in space and time relationships, classifying, using the data to create models, predicting, interpreting data, and inferring information or solutions to problems. Meanwhile, an inquiry process framework of Kipnis and Hofstein ( 2008 ) included identifying problems, formulating hypotheses, designing an experiment, gathering and analysing data, and drawing conclusions about scientific problems and phenomena.

Furthermore, based on the previous studies (Gobert et al., 2013 ; Liu et al., 2008 ; Pine et al., 2006 ; Quellmalz et al., 2012 ; Zachos et al., 2000 ), Kuo et al., ( 2015 ) defined an inquiry proficiency framework to integrate cognitive skills with scientific knowledge during student participation in activities akin to scientific discovery. The framework emphasized four fundamental abilities as core components including questioning (e.g., asking and identifying questions), experimenting (e.g., identifying variables and planning experimental procedures), analysing (e.g., identifying relevant data and transforming data), and explaining (e.g., making a claim and using evidence). Their scenario-based tasks were created within a web-based application, covering four content areas (Physics, Chemistry, Biology, and Earth Science) across four inquiry abilities (Wu et al., 2015 ). Chi et al. ( 2019 ) defined scientific inquiry as the ability to integrate science knowledge and skills to identify scientific questions design and conduct investigation, analyse and interpret information and generate evidence-based explanations. A hands-on performance assessment instrument for measuring student scientific inquiry competences in the science lab was developed based on this framework (see a sample task in Fig. 5 a).

PISA 2015 developed the framework to assess 15-year-old students’ scientific inquiry competency of explaining phenomena, designing inquiry, interpreting data (OECD, 2017 ). Some empirical studies (e.g., Intasoi et al., 2020 ; Lin & Shie, 2024 ) developed assessment framework based on the framework to assess scientific inquiry competence of students. For example, Lin and Shie ( 2024 ) developed a PISA-type test to assess Grade 9 students’ scientific competence and knowledge related to curriculum and daily-life contexts (e.g., trolley motion, camping, household electricity, driving speed, etc.).

In the line, Arnold et al. ( 2018 ) referred to scientific inquiry as the competence to emphasize the cognitive aspects of the ability to use problem-solving procedures. Scientific competence was defined as the ability to understand, conduct, and critically evaluate scientific experiments on causal relationships, addressing problems and phenomena in the natural world. Three key sub-competences of experimentation were identified: generating hypotheses, designing experiments, and analysing data. Each sub-competence included five specific components. For instance, the sub-competence of generating hypotheses covered the ability to define the investigative problem, identify the relationship between dependent and independent variables to generate testable hypotheses or predictions and justify them, as well as propose different independent variables or alternative predictions. Zheng et al. ( 2022 ) categorized inquiry into eight components, highlighting information processing and reflective evaluation, echoed in study by Mutlu ( 2020 ).

In other approaches, Nowak et al. ( 2013 ) developed a model for assessing students’ inquiry ability, which had two dimensions: scientific reasoning (including question and hypothesis, plan and performance, and analysis and reflection) and inquiry methods (comprising modelling, experimenting, observing, comparing, and arranging). Together, these dimensions form a 9-cell matrix. Based on the theoretical structure, they developed a test instrument to assess students’ scientific inquiry (see sample item in Fig. 5 b). Meanwhile, Pedaste and colleages ( 2021 ) developed a science inquiry test for primary students based on the four-stage inquiry-based learning framework by Pedaste et al. ( 2015 ). The test encompassed the essential skills aligned with the four stages of the inquiry-based learning framework. These included analytical skills, which are primarily required in the Orientation, Conceptualization, and Investigation phases; planning skills, mainly needed in the Investigation phase; and interpretation skills, primarily needed in the Conclusion and Discussion phases.

Samples of the item/task for assessing scientific inquiry

A virtual experimentation environment developed by McElhaney and Linn ( 2011 ) simulated the experimentation activities of Airbags. These activities illustrated the interaction between the airbag and the driver during a head-on collision, using the steering wheel as a point of reference. Referred the existing studies (e.g., Kind, 2013 ; Liu et al., 2008 ; Pine et al., 2006 ), a simulation-based test developed by Wu et al. ( 2014 ) focused on two types of abilities: experimental and explaining. Experimental ability involved three sub-abilities: identifying and choosing variables, planning an experiment and selecting appropriate measurements, while explaining ability covered three sub-abilities: making a claim, using evidence, and evaluating alternative explanations. They designed four simulation tasks, namely Camera, Viscosity, Buoyancy and Flypaper. For example, the Flypaper task simulated a farm context in which students investigated which colour of flypaper could catch the most fruit flies. They were asked to propose hypotheses related to the decrease in flies according to the given chart, conduct appropriate experiments to measure the effect of the flypaper colour, investigate which colour of flypaper is best for catching fruit flies, and decide on alternative explanations based on the data evidence.

In the vein, Sui et al. ( 2024 ) designed an animation-based web application allow students conduct a scientific inquiry on atmospheric chemistry with animation experiments to understand the climate change and atmospheric chemistry. The scientific inquiry was defined with three core abilities: data analytic, control of variables and scientific reasoning. The digital game-based inquiry, BioScientist (Bónus et al., 2024 ) involved series of tasks, which focused on inquiry skills focusing on design of experiment, identification and control of variables, interpretation of data, and conclusion. For instance, a simulation provided some relevant variables, students need to manipulate the first one and then second variables to generate the data set. Based on the data-based evidence, they selected the answer and draw reasonable conclusions.

In summary, what becomes clear is that the mainstream framing of the construct of scientific inquiry was categorised as lists of specific components of competence. The frameworks for assessing scientific inquiry in technology-rich environments share many similarities with those used in traditional settings. In this view, it may summarise scientific competence into four main sub-competencies and their respective components (facets) based on the existing frameworks, as shown in Table 1 .

The Frequent Usage of the Components in Assessing Scientific Inquiry

In this section, we employed ENA to quantitatively visualize the usage frequency of yed ENA to quantitatively visualize the usage frequency of individual components and their co-usage with others in the selected empirical studies. Figure 6  illustrates the frequency of usage (represented by the size of the nodes) and the degree of co-usage of the components (represented by the width of the lines) across the reviewed studies.

In general, it appears that the nine facets were most often used to assess scientific inquiry, including formulate prediction or hypotheses (FP), formulate questions (FQ), set experiment (DS), vary independent variable (DV), measure dependent variable (DM), control confounding variables (DC), describe data (AD), interpret data (AI), and reach reasonable conclusion (CR). Other components were frequently used in inquiry tasks, including identify independent variable (FI), Identify dependent variable (FD), using appropriate method (AU) and evaluate methods (CE).

The pattern of components of scientific inquiry competence in selected studies simulated in the ENA model

Foundation Frameworks for Scientific Inquiry Assessment

To explore foundational frameworks for scientific inquiry assessment, we employed the Bibliometric analyses via the co-citation networks prevalent in the studies selected. The findings as depicted in Fig. 7 showed that US science education standards (NRC, 1996 ) stood out as the most frequently cited, followed by NRC texts A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas” (2012) and “Inquiry and the National Science Education Standards: A Guide for Teaching and Learning” (NRC, 2000 ). Other texts were often cited such as: “The development of scientific thinking skills in elementary and middle school” (Zimmerman, 2007 ) and “Next Generation Science Standards: For States, By States” (2013). It is clear that the 1996 NRC standards were prominently featured in the discussion, while the 2012 framework was referred to more frequently than the actual standards, particularly in terms of citations in the reviewed studies.

The co-citation networks found in the studies reviewed

Constructs, Formats and Techniques Approaches in Assessing Scientific Inquiry

Generally, three types of tests emerged within the realm of scientific inquiry assessment: hands-on performance assessment, a battery of independent tests (paper battery), and digital-based battery tests (online battery) and simulation performance assessment. The analysis revealed that paper battery (41.1%) and on-line battery tests (39.7%) were the most widely applied construct, followed by and simulation performance assessment (37.0%). Hands-on performance (17.6%) still continues to hold its place in the field. The findings also suggest that, regardless of the mode of assessment, multiple-choice (71.4%) and open-ended (69.8%) formats are consistently prevalent. Notably, several studies (44.5%) used a combination of multiple-choice and open-ended formats.

Assessment of Scientific Inquiry in Traditional Environment

Performance assessments represent a groundwork approach to measuring students’ capabilities in scientific investigation, conceptualization, and problem-solving within authentic contexts. Researchers explored various dimensions of hands-on performance assessments, designing tasks that authentically mirror the scientific process. For example, Zachos et al. ( 2000 ) developed performance tasks mirroring scientific inquiry processes, assessing concepts, data collection, and conclusion drawing. Pine et al. ( 2006 ) emphasized inquiry skills like planning and data interpretation. Emden and Sumfleth ( 2016 ) assessed students’ ability in generating ideas, planning experiments, and drawing conclusions through hands-on inquiry tasks. They used video analysis in combined with paper-pencil free response reports to measure performance.

Traditional assessments tend to rely on standardized tests, featuring multiple-choice items aligned with policy-led standards. These tests, often administered in a paper-and-pencil format, measure students’ proficiency levels in comparison with peers. Without the need for advanced technology, they covered a wide range of content and question types, including multiple-choice, short answer, and essays (Fig. 8 ). The majority of studies employed such a battery of independent tests to assess one or more components of scientific inquiry (e.g., Arnold et al., 2018 ; Kaberman & Dori, 2009 ; Kazeni et al., 2018 ; Lin & Shie, 2024 ; Nowak et al., 2013 ; Schwichow et al., 2016 ; Vo et al., 2023 ; Van Vo & Csapó, 2021 ). There were a significant positive correlations between the paper-and-pencil tests and performance assessment tasks (e.g., Kruit et al., 2018 ). Table 2 presents an excerpt from the summarised table of the studies selected (See more Supplemental material at https://osf.io/5bt82 ).

Samples of item/task assessing scientific inquiry in paper-based modality. a A sample item in requiring interpretation [source from Kruit et al. ( 2018 )]. b A sample of a task for assessing inquiry [source from Temiz et al. ( 2006 )]

Assessment of Scientific Inquiry in Digital Based Environments

From 2012 onwards, studies started to increasingly use advanced technologies in digital-based environments in their assessment of scientific inquiry. Studies (e.g., Gobert et al., 2013 ; Kuo et al., 2015 ; Quellmalz et al., 2012 ; Sui et al., 2024 ) started to use innovative tools and methodologies to construct assessment platforms that more accurately captured the nuanced complexities of scientific inquiry. These include dynamic simulations with web-based applications like (Quellmalz et al., 2012 , 2013 ), Inquiry Intelligent Tutoring System (Inq-ITS) (Gobert et al., 2013 ), 3D-game simulation (Hickey et al., 2009 ; Ketelhut et al., 2013 ), PISA 2015 (e.g., OECD, 2017 ; Teig et al., 2020 ) (see Fig. 9 ) and scenario-based tasks integrating multimedia elements (Kuo et al., 2015 ). For example, Inq-ITS is an online intelligent tutoring and assessment platform designed for physics, life science, and earth science. It aims to automatically evaluate scientific inquiry skills in real-time through interactive microworld simulations.

Simulation-based tools like Simulation-based assessment of scientific inquiry abilities (Wu et al., 2014 ; Wu & Wu, 2020 ) can effectively assess abilities in explaining and other relevant components. Immersive virtual settings and automated content scoring engines offered efficient evaluation methods (Baker et al., 2016 ; Liu et al., 2016 ; Scalise & Clarke-Midura, 2018 ; Sui et al., 2024 ) and were potential for formative assessment (Hickey et al., 2009 ). The digital game-based inquiry, i.e., BioScientist (Bónus et al., 2024 ), Quest Atlantis (Hickey et al., 2009 ), allowed students to engage with a series of tasks, which focused on inquiry skills using simulation in which students interacted with suitable elements during the inquiry process. Table 3 illustrates an excerpt regarding components, tools and techniques in digital-based scientific inquiry assessment (See Supplemental material at https://osf.io/5bt82 ).

A screenshot of item 3 of Task 1 from the PISA 2015 item from the Running in Hot Weather unit [Source from OECD ( 2015 )]

Techniques for Developing and Validating Scientific Inquiry Assessment

Most studies referred to the American Education Research Association (AERA, 1999 ) for developing and validating scientific inquiry assessment tasks. This included defining the assessment framework, designing tasks and items, scoring rubrics, and conducting a pilot test (Arnold et al., 2018 ; Kuo et al., 2015 ; Lin & Shie, 2024 ; Lin et al., 2016 ; Nowak et al., 2013 ; Schwichow et al., 2016 ; Vo & Csapó, 2023 ).

Numerous methods and techniques were employed for scoring proficiency in assessing scientific inquiry. Full credit was applied to correct answers in multiple-choice tests and partial credit to score open-ended questions (Arnold et al., 2018 ; Kaberman & Dori, 2009 ; OECD, 2017 ; Sui et al., 2024 ; Teig et al., 2020 ). Interestingly, a high percentage of studies, as much as 36.8%, utilized a 3-point scale rubric in their assessments or evaluations (Intasoi et al., 2020 ). Log-file techniques were increasingly popular for assessing scientific inquiry in recent studies (Baker et al., 2016 ; McElhaney & Linn, 2011 ; Teig, 2024 ; Teig et al., 2020 ). Data-mining algorithms enhanced assessment accuracy (Gobert et al., 2015 ). Virtual Performance Assessments allowed to record a log data (Baker et al., 2016 ), containing students’ actions (e.g., clicks, double clicks, slider movements, drag and drop, changes in the text area) along with the timestamp for each action. Different actions and their timings were combined to reveal behavioural indicators, such as number of actions, number of trials, time before the first action, response time for each item, and total time for each unit. The process of assessment development and validation was found to be based on a construct modelling approach (Brown & Wilson, 2011 ; Kuo et al., 2015 ).

For validation approaches, the face validity of the test instrument was established based on faculty and student feedback (Kuo et al., 2015 ) or expert judgments (Šmida et al., 2024 ; Vo & Csapó, 2023 ; Wu et al., 2014 ). Construct validity focused on the test score as a measure of the psychological properties of the instrument. For validity analysis, most studies applied Rasch measurement model (Arnold et al., 2018 ; Chi et al., 2019 ; Intasoi et al., 2020 ; Kuo et al., 2015 ; Lin & Shie, 2024 ; Liu et al., 2008 ; Nowak et al., 2013 ; Pedaste et al., 2021 ; Quellmalz et al., 2013 ; Scalise & Clarke-Midura, 2018 ; Schwichow et al., 2016 ; Vo & Csapó, 2023 ; Wu et al., 2015 ), followed by factor analyses (Feyzíoglu, 2012 ; Lou et al., 2015 ; Pedaste et al., 2021 ; Samarapungavan et al., 2009 ; Šmida et al., 2024 ; Tosun, 2019 ). Predictive or criterion-related validity was used to demonstrate that the test scores are dependent on other variables, tests, or outcome criteria. In assessment of scientific inquiry, predictive validity referred to some standard tests, such as Lawson’s Classroom Test of Scientific Inquiry (e.g., Kuo et al., 2015 ; Wu et al., 2014 ), Louisiana Educational Assessment Program (e.g., Lou et al., 2015 ), General cognitive ability (e.g., Dori et al., 2018 ; Kruit et al., 2018 ) and science grades in school (Pedaste et al., 2021 ).

Most popular software employed for data analysis including the R (Sui et al., 2024 ; Van Vo & Csapó, 2021 ), ConQuest (Kuo et al., 2015 ; Lin & Shie, 2024 ; Nowak et al., 2013 ; Seeratan et al., 2020 ; Vo & Csapó, 2021 ), SPSS (Bónus et al., 2024 ; Temiz et al., 2006 ) and Winsteps (Arnold et al., 2018 ; Chi et al., 2019 ; Pedaste et al., 2021 ), and LISREL (Tosun, 2019 ).

Developmental Trend in Assessing Scientific Inquiry

The objective here was to investigate the evolving trends and patterns of scientific inquiry employed within the studies over time. The articles were sub-divided into two distinct temporal spans − 2000–2012 and 2013–2024. Figure 10 visualizes patterns of components of scientific inquiry competence which were used the studies in the 2000–2012 period (Fig. 10 a), the 2013–2024 period (Fig. 10 b) and a comparison of that between the two periods (Fig. 10 c). The graph of comparison was calculated by subtracting the weight of each connection in one network from the corresponding connections in another.

The results revealed that some main components, i.e., measure dependent variable (DM), reach reasonable conclusion (CR), identify independent variable (FI), set experiment (DS), control confounding variables (DC), vary independent variable (DV), identify dependent variable (FD), and formulate prediction (FP), were often used consistently over time. However, components such as using appropriate method (AU), evaluate methods (CE), defining task time (DT), defining replication (DR), and recognizing limitations (CL) demonstrated a heightened prevalence in the later period, indicating a heightened emphasis on these aspects of assessing scientific inquiry. Conversely, when examining the earlier period (2000–2012), components like identify independent variable (FI) and justify question / hypothesis (FJ) exhibited a more noticeable frequency of application.

Patterns of facets of scientific inquiry competence in selected studies simulated in the ENA model

To streamline the understanding of these tests in the scientific inquiry tasks, we employed co-occurrence networks adapted in Bibliometric analysis. The analysis revealed that battery independent tests and performance assessment are most frequently used with multiple-choice and open-ended constructs. However, the trend is toward the online and simulation ones with new techniques of log-file tracking and scaffolding (Figure 11 a).

When it comes to emphasizing vision in science education, empirical evidence has shown that the design of inquiry tests incorporated both the content of pure science, vision I scientific literacy, and the science-in-context applications related to science, vision II scientific literacy. This ensures a balanced evaluation that covers fundamental scientific principles as well as their real-world applications. However, it is noteworthy that recent studies have shown a growing preference for assessing scientific inquiry within science-in-context (Figure 11 b).

Trend of types and formats in assessing scientific inquiry. a Co-occurrence networks depicting types, formats and “vision” emphasis. b Types, formats and “vision” emphasis over time

Discussion and Conclusions

The paper utilized the PRISMA guideline for systematic review in combination with bibliometric analyses for reviewing scientific research literature to draw together a detailed overview of research on assessing scientific inquiry abilities in global educational settings.

Our review of the problem of assessing scientific inquiry allowed us illuminate this rapidly changing area of research. In the last two decades, while research on curriculum reforms in science inquiry-orientations have proceeded apace, research on digital modes of assessing scientific inquiry have only recently started to make an impact. Our analysis of sixty-three studies showed that scientific inquiry has been emphasized, integrated, and assessed in the settings of science education around the world. The bulk of this research, started in the US, was brought to global significance through the influence of transnational policy decision-makers, such as the OECD and mainly US-led networks of researchers. The US researchers published several academic papers in the earliest part of the timeline studied, and their findings remain today as foundational citations. This research was quickly followed by new networks forming from Germany, Turkey, Taiwan and China. Co-citation networks revealed that the US National Science Education Standards (NRC, 1996 ) remains as a foundational reference, even though the 2012 document should have had nearly equal significance. Surprisingly, the American Association for the Advancement of Science (AAAS) benchmarks were not cited as frequently in the case.

Over two decades, performance assessments and batteries of independent tests, employing both multiple-choice and open-ended formats, continue to be widely used for assessing scientific inquiry. Hands-on performance assessment remains one of the main modes of assessing competence in scientific inquiry. Moreover, a traditional written test can be easily administered, reliably scored, and is familiar to students, but falls short in effectively capturing the dynamics of real-life inquiry and may be significantly influenced by reading proficiency (Kruit et al., 2018 ). Besides, hands-on performance assessment is not efficient for large-scale assessments (Kuo et al., 2015 ). Therefore, there is a growing emphasis on developing authentic tests. These tests, which may include manipulatives, are considered to provide a more comprehensive assessment of students’ capability in conducting scientific inquiry through multiple formats (e.g., open-constructed, multiple-choice, multiple-true-false, short closed-constructed).

Our analysis showed that original components like formulating questions or hypotheses, designing experiments, analysing data, and drawing conclusions were consistently used for assessing scientific inquiry capabilities over time. However, certain sub-components, such as formulating prediction or hypotheses , formulating questions , setting experiment , varying independent variable , measuring dependent variable , controlling confounding variables , describing data , interpreting data , and reaching reasonable conclusions , were the most frequently used competences in the selected studies. Meanwhile, facets like specifying test time , defining replication , and recognizing limitations were shown to have an increasing prevalence in the last decade. This trend signals a possible enhanced emphasis on these facets or sub-components of scientific inquiry, particularly in digital-based environments. The growing focus on these areas may reflect the advancements in technology that allow for more precise measurement and analysis, thereby promoting a more rigorous approach to scientific inquiry.

In the last decade, online battery tests and simulation performance assessments have gained increasing popularity. These studies reflect the design and enactment of innovative assessments using advanced technology, such as Web-based Inquiry Science Environments (McElhaney & Linn, 2011 ), SimScientists (Quellmalz et al., 2012 , 2013 ), iSA–Earth Science (Lou et al., 2015 ), Multimedia-based assessment of scientific inquiry abilities (Kuo et al., 2015 ; Wu et al., 2015 ), Inq-ITS system (Inquiry Intelligent Tutoring System (Gobert et al., 2013 , 2015 ), Virtual Performance Assessments (Baker et al., 2016 ), Dynamic visualization to design animation-based activities (Sui et al., 2024 ).

In terms of emphasizing vision in science education, empirical evidence demonstrated that the design of inquiry tests included pure science content (vision I) and science-in-context considerations (vision II). However, recent studies increasingly preferred assessing scientific inquiry within real-world contexts. This trend reflects an understanding of the importance of students being able to apply scientific concepts to real-world problems, thus preparing them for the complex, interdisciplinary challenges they are likely to face in their futures. By focusing on context, these studies aim to enhance students’ ability to think critically and engage with science in a way that is relevant to their everyday lives and broader community issues. These are also partly reflected in alignment with national and international frameworks.

Implications

The paper not only identifies various aspects of studies and research within a specific field of assessing inquiry competence, but also provides systematic rationales related to the construction of the tools, tasks and techniques used to assess scientific inquiry capabilities in educational settings. This is valuable for science teachers as they create inquiry-oriented tasks in their classrooms. Additionally, new researchers can gain an overview of the research teams working in this area.

The foreseeable trend may be that the move towards dynamic and interactive inquiry assessments enables researchers to examine not just the accuracy of students’ responses (product data) but also the procedures and actions they employ to arrive at responses (process data) (Teig, 2024 ). Multi-faceted aspects of scientific inquiry can be observed during assessment tasks. Beside traditional components in formulating questions or hypotheses , designing experiments , analysing data , and drawing conclusions , some new aspects like task time , replication and recognizing limitations seem to more consider as they become measurable in technology-rich environment. Therefore, log-file analysis will be more popular approach in the field.

The development of scientific inquiry assessments should be considered as a multifaceted process of construct modelling. The combination of multiple validity approaches is encouraged in development of the assessment of scientific inquiry. Psychometric analysis through Rasch model is often employed in validating and scaling student performance. Alternative approaches to deal with log-file records are still in the early pioneering stages of development (e.g., Baker et al., 2016 ; McElhaney & Linn, 2011 ; Teig, 2024 ; Teig et al., 2020 ). An automated scoring engine demonstrated a promising approach to scoring constructed-response in assessment of inquiry ability (Liu et al., 2016 ). This opens a potential space for upcoming new research in this field with application of artificial intelligence.

The review illuminates the evolving landscape of scientific inquiry assessment development and validation, emphasizing the importance of a comprehensive and flexible approach to meet the diverse needs of educational and research settings. However, tackling such novel tasks necessitated not only an understanding of scientific inquiry assessment but also sophisticated technology and its corresponding infrastructures. For example, simulation tasks addressing complex real-world problems, such as climate change, water shortages, and global food security, necessitate the collaboration of various relevant stakeholders. It is crucial for research and educational technology institutions to play supportive roles for science teachers. More robust and published research on scientist-led K-12 outreach is essential for enhancing comprehension among scientists and K-12 stakeholders regarding the optimal practices and challenges associated with outreach initiatives (Abramowitz et al., 2024 ).

Science teachers were encouraged to integrate both pure science content and science-in-context applications into their teaching and assessment (Roberts & Bybee, 2014 ). This will involve teachers’ designing inquiry-based activities that apply scientific principles to real-world problems, helping students develop higher-order critical thinking skills and preparing them for future interdisciplinary challenges. Emphasizing real-world applications of scientific inquiry can help to make science education more relevant and engaging for students.

Moreover, the adoption of combined approaches to the literature review, integrating bibliometric and ENA analyses with systematic review PRISMA guidelines, demonstrates a meticulous and systematic approach to data synthesis. Beyond its immediate application here, this research design may serve as a model for future research endeavours, contributing to the advancement of novel methodologies.

Limitation of the Review

The review conducted here was limited to 63 empirical studies published in SCOPUS/WoS data between 2000 and 2024 and in English. It may not cover the full range of academic documents that are made available in other academic databases, potentially missing significant studies published in different languages or within other research repositories.

The nature of psychological issues is often controversial, and our suggested framework for assessing scientific inquiry competence is merely one of several approaches presented in the literature. Different scholars proposed various models, each with its own strengths and limitations, reflecting the ongoing debate and complexity within this field. Furthermore, the selection of articles was conducted and scored by the authors, which introduces the possibility of certain biases. These biases may stem from subjective interpretations, or unintentional preferences, potentially influencing the overall findings.

The application of advanced technology is sophisticated and diverse; we have highlighted only a few features without covering all aspects of digital-based assessment. Therefore, generalizations from the study need to be approached with caution. However, the study provides valuable insights into the fast-globalizing landscape of assessing scientific inquiry and will be of interest to researchers, educators, teachers in science education and those with an interest in grappling with similar problems of assessment.

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Effective weight optimization strategy for precise deep learning forecasting models using EvoLearn approach

• Jatin Bedi 1 ,
• Ashima Anand 1 ,
• Samarth Godara 2 ,
• Ram Swaroop Bana 3 ,
• Mukhtar Ahmad Faiz 3 , 4 ,
• Sudeep Marwaha 2 &
• Rajender Parsad 2  

Scientific Reports volume  14 , Article number:  20139 ( 2024 ) Cite this article

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Time series analysis and prediction have attained significant attention from the research community in the past few decades. However, the prediction accuracy of the models highly depends on the models’ learning process. In order to optimize resource usage, a better learning methodology, in terms of accuracy and learning time, is needed. In this context, the current research work proposes EvoLearn, a novel method to improve and optimize the learning process of neural-based models. The presented technique integrates the genetic algorithm with back-propagation to train model weights during the learning process. The fundamental idea behind the proposed work is to select the best components from multiple models during the training process to obtain an adequate model. To demonstrate the applicability of EvoLearn, the method is tested on the state-of-the-art neural models (namely MLP, DNN, CNN, RNN, and GRU), and performances are compared. Furthermore, the presented study aims to forecast two types of time series, i.e. air pollution and energy consumption time series, using the developed framework. In addition, the considered neural models are tested on two datasets of each time series type. From the performance comparison and evaluation of EvoLearn using a one-tailed paired T -test against the conventional back-propagation-based learning approach, it was found that the proposed method significantly improves the prediction accuracy.

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Introduction.

The process of extracting meaningful patterns/information from time-indexed data, termed “Time series analytics and modelling” 1 , has gained significant attention from the research community in recent years. It covers a broad spectrum of aspects, including time-series clustering, classification, regression/prediction and visualization. Time series analysis and prediction have shown great potential in nearly all application domains, including healthcare, utility services, transportation, manufacturing, financial services and agriculture 2 , 3 , 4 , 5 . Moreover, in the past few years, forecasting air pollution and energy consumption have been focal points for researchers as the insights from these domains help governments in decision-making on an extensive scale 6 , 7 .

With the precise forecasting of air pollution, it becomes more manageable to address and mitigate the risks of air pollution and secure a safe level of pollutant concentration in the target region. It also helps estimate risks to the atmosphere and the climate caused by inferior air quality standards. Precise forecasting can also ease day-to-day planning activities, avoid locations with high alert zones, and execute effective pollution control measures.

Furthermore, global energy consumption has also been rising every year. Governments and power companies need to explore models to forecast and plan energy use precisely, as it acts as the backbone of nationwide economies. Monitoring the state of electrical power loads, especially in the early detection of unusual loads and behaviours, is essential for power grid maintenance and energy theft detection.

In this direction, the presented study is aimed at forecasting air pollution time series and energy consumption time series corresponding to a total of three States/UTs of India. A wide variety of statistical, machine learning and deep neural models have been employed for the modelling and prediction tasks in the listed domains 8 , 9 , 10 , 11 . In the earlier studies, statistical/traditional techniques 12 , 13 , 14 , 15 including Autoregressive (AR), Autoregressive with moving average (ARMA), Autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA) models have been widely utilized for the time series modelling tasks. Despite their vast popularity, these models have been found less useful at improvising the generalization capabilities of the prediction approaches.

With the latest advancements in information technology and sensors, machine learning (ML) models 16 , 17 , 18 , 19 , 20 , including linear and polynomial regression models, support vector machines, decision trees, k-nearest neighbours, bayesian networks and artificial neural networks (ANN), were adopted for achieving the improved prediction accuracy. These ML models have achieved good generalization capabilities but could not efficiently capture highly non-linear featural aspects and sudden variations of the time-series data. To deal with this, various deep neural network models were introduced in the past to automatically capture nonlinear and chaotic hidden features of the temporal data. Several research studies have employed these different neural models 21 , 22 , 23 , 24 , 25 , 26 , including recurrent neural network (RNN), convolutional neural network (CNN), deep neural network (DNN), gated recurrent unit (GRU) and long short term memory network (LSTM) for the time-series modelling/prediction tasks. Deep neural models are very potent at capturing different featural and non-linear aspects of the time series. However, determining optimal architectural parameters of the neural network models in terms of the number of layers, activation functions, and the number of neurons per layer is a complex task. These architectural parameters have a direct impact on the target model’s accuracy and generalization capabilities.

To deal with the problem of tuning the hyperparameters of the neural-based models, one field that has attained great interest from the research community is integrating evolutionary algorithms with learnable models. This has greatly helped researchers in achieving improved prediction accuracy on data belonging to different application domains. In the past decades, numerous research studies have successfully explored different statistical, deep neural models and hybrid techniques for time-series forecasting on data of various domains like transportation, rainfall estimation, financial service, utility services etc.

Ho and Xie 27 presented an approach to investigate the use of auto-regressive models at system reliability forecasting. The performance comparison is done using the conventional equations driven by the Duane model. Dastorani et al. 14 examined different time-series models to forecast monthly rainfall events. Statistical methods, including AR, ARMA, ARIMA, and SARIMA were implemented to do accurate rainfall forecasting at nine different stations in Northeast Iran. Based on the experimental results, the authors concluded that the best time-series models could change based on the input data variability.

Nicholas et al. 17 proposed a support vector regression-based approach to capture non-linear and non-stationary time-series variations. Also, the authors stated some critical challenges to be considered while employing the proposed SVR approach for the prediction task, such as selecting optimal hyper-parameters and performance metrics. Martinez et al. deployed a k-nearest neighbour algorithm to estimate time-series. The performance of the proposed approach is assessed on the N3 competition dataset. Cuautle et al. introduced an artificial neural network-based method for estimating chaotic signals. In addition to statistical and machine learning models, sequential deep learning prediction models are also well utilized for the time-series prediction task. Connor et al. 28 proposed a robust learning algorithm to improve the stability of the recurrent neural network models. The proposed method was based on dealing with outliers present in the data. Ruan et al. 26 developed a deep learning-based approach for load-balancing and adaptive scheduling tasks in real-time systems. The approach has implemented an LSTM model for the specified task.

Soltani 29 proposed a way to combine wavelet decomposition and neural models for targeting higher accuracy. Shi et al. 30 integrated the ARIMA model with ANN and SVR to efficiently estimate wind speed and energy load time-series. Xu et al. 31 blended SARIMA models support vector regression models to estimate the demand of the aviation industry. From the performance comparison with conventional models, the authors stated that hybrid models provide better prediction accuracy. Du et al. 21 provided a deep neural network-based hybrid model for air pollution forecasting. The proposed hybrid approach (CNN + LSTM) captures the Spatial-temporal aspect of the series parameters to improve prediction accuracy.

Sheikhan and Mohammadi 32 proposed a Genetic Algorithm (GA)-based approach for electricity load forecasting. The authors employed ant-colony optimization algorithm for the feature selection task. Experimental results achieved on the real-world energy consumption dataset showed that the GA-ant colony optimization integrated approach provides better results than the traditional MLP model. Dong et al. 33 proposed a deep learning and k-nearest neighbours-based approach for load time series forecasting. Initially, the k-nearest neighbour algorithm was implemented to capture the historical featural aspect of the input series. Subsequently, a GA was employed to achieve multi-objective optimization by determining the smallest category of k-nearest neighbour and the highest accuracy. In a similar context, Anh et al. 34 utilized Bayesian optimization and GA for hyper-parameters optimization and feature selection (respectively) in a time-series prediction problem/task.

Niska et al. 35 identified that defining the architectural model for non-linear chaotic time-series is a challenging task. So, the authors, in their research study, proposed a GA-based approach to decide the architectural details of the multi-layer perceptron model. The performance evaluation of the model is done on a real-world air quality dataset. Puneet et al. 36 proposed a GA-based faster approach to discover the hyper-parameters of the DNN, RNN and CNN models. Compared to the existing hyperparameters optimization approach, which optimizes each hyperparameter in isolation, the proposed approach targets simultaneous optimization of multiple hyper-parameters. The parameter estimation speed of the proposed approach is compared with the Grid search method, and it is found that the proposed GA-based approach is 6.71 times faster than the Grid search method.

Li et al. 37 employed GA to determine the optimal value of the number of trainable parameters for training a convolutional neural network. The proposed approach converged with 97% precision in less than 15 generations. In a similar context, Yuan et al. 38 proposed a novel GA-based hierarchical evolutional strategy for deciding the hyper-parameters of graph neural networks. The authors presented a fast evaluation approach for graph neural networks using the early stopping criteria. The performance evaluation of the approach is done on two deep-graph neural network models. Shahid et al. 39 integrated a GA with the LSTM model (GLSTM) for the sequential modelling or prediction task. In the proposed approach, two parameters, window size and number of neurons in LSTM layers, were determined by using the GA. The prediction results were compared with the support vector machine model, and it was found that GLSTM provides 6% to 30% improvement in the prediction accuracy.

Ahmet kara 40 developed a hybrid approach for influenza outbreak forecasting using the LSTM model and GA. The author employed GA to obtain the optimal value for many LSTM hyper-parameters such as the number of epochs, number of LSTM layers, size of units in each LSTM layer, and window size. The experimental results of the proposed hybrid are shown on a dataset collected from the Centers for Disease Control and Prevention (CDC), USA. Huang et al. 41 proposed a hybrid approach integrating variational mode decomposition, GA and LSTM for financial data forecasting. In this research study, GA was utilized to determine the optimal parameters of the variational mode decomposition method. Cicek and Ozturk 42 integrated ANN with a biased random key GA for the time-series forecasting task. In the study, biased random key GA was implemented to determine the optimal value of neural model parameters, namely the number of hidden neurons, hidden neurons’ bias values, and the connection weights between nodes. The performance evaluation of this approach is done by comparing prediction results with conventional ANN model with back-propagation and Support vector machines and ARIMA models.

The research by Pin et al. 43 concentrates on enhancing predictive biomedical intelligence through transfer learning from biomedical text data for specific downstream tasks. In contrast, the proposed EvoLearn approach focuses on improving and optimizing the learning process of neural-based models for time series analysis and prediction. Moreover, the EvoLearn approach integrates GA with back-propagation for model training, focusing on selecting the best components from multiple models during the training process. Conversely, the existing research utilizes transfer learning from pre-trained language models (PLMs) and proposes a Stacked Residual Gated Recurrent Unit-Convolutional Neural Networks.

Another similar method employing the artificial bee colony (ABC)-integrated extreme learning machine(ELM) approach is introduced by Yang and Duan 44 . In this work, the ABC algorithm is applied to optimize the weights of a simpler model, the ELM, which inherently possesses fewer parameters. In contrast, the EvoLearn approach diverges from the existing method, primarily regarding the optimization methodology employed. In EvoLearn, GA is utilized to optimize the weights of DL models. Additionally, EvoLearn adopts an iterative alternation between GA and the backpropagation algorithm, which enhances optimization speed. This iterative process facilitates a more rapid convergence towards optimized weights. In contrast, the ABC-integrated ELM approach does not incorporate this iterative alternation, potentially resulting in a slower optimization process.

Another work by Wang et al. 45 introduced the adaptive differential evolution back propagation neural network (ADE-BPNN). The ADE–BPNN approach combines the adaptive differential evolution (ADE) algorithm with the backpropagation neural network (BPNN) to improve forecasting accuracy. In contrast, the proposed EvoLearn approach focuses on optimizing the parameters of pre-designed neural network architectures to enhance their predictive performance across various domains. Moreover, in the existing approach, ADE is initially employed to search for the global initial connection weights and thresholds of BPNN, followed by the thorough optimization of weights and thresholds using BPNN itself. Overall, while both approaches seek to enhance forecasting accuracy, they employ different optimization algorithms and strategies. EvoLearn focuses on optimizing the weights of pre-designed deep learning models, while ADE–BPNN combines ADE with BPNN to automatically design the initial connection weights and thresholds, thereby improving the forecasting performance of BPNN.

The approach introduced by Jalali et al. 46 focuses on electricity load forecasting using a deep neuroevolution algorithm to design CNN structures automatically. Specifically, it utilizes a modified evolutionary algorithm called enhanced grey wolf optimizer (EGWO) to optimize the architecture and hyperparameters of CNNs for improved load forecasting accuracy. This method aims to address the complexity of electricity load forecasting and the challenges in CNN architecture design by employing a novel optimization technique. While both approaches (EGWO-based method and EvoLearn) involve optimization algorithms, they differ in their specific focus and methodology. The EvoLearn approach aims to optimize the weights of pre-designed deep learning models, while the mentioned approach focuses on automatically designing CNN structures using a neuroevolution algorithm. Additionally, the mentioned approach specifically targets electricity load forecasting, whereas EvoLearn can be applied to various prediction tasks in different domains.

Based on the literature review, it is evident that the proposed EvoLearn method represents various novel contributions to the field. While previous research has commonly employed evolutionary algorithms for hyperparameter tuning, only a limited number have utilized them for tuning model weights, as we have done. Furthermore, in those studies where model weights are tuned akin to our approach, the utilization has typically involved separate stages of evolutionary optimization and backpropagation, rather than simultaneous and iterative integration as demonstrated in our methodology. This distinctive approach accelerates the optimization process significantly, distinguishing our method as notably innovative compared to prior research endeavors.

EvoLearn integrates GA with back-propagation by initially training neural networks using back-propagation to establish a population of diverse models with different weights. The GA then optimizes these weights through selection, crossover, and mutation, focusing on minimizing a combined error from both training and validation sets. This hybrid approach allows for a broader exploration of the weight space, preventing overfitting and improving generalization. Compared to conventional back-propagation alone, EvoLearn enhances model robustness and performance by leveraging evolutionary strategies to escape local minima.

Furthermore, the proposed strategy aids neural models in achieving better accuracy while avoiding overfitting. The major research contributions of the current research study are summarized as follows:

The present work introduces a methodology to train models to their greater potential and achieve better model accuracy. Moreover, the proposed fitness function used in the presented technique keeps the models from over-training.

The proposed approach provides a mechanism to generate optimal weight parameters. Best combinations of network weights are produced by integrating GA with the back-propagation.

The presented work introduces an evolutionary algorithm-based model-selection technique against the conventional method of selecting the best model among the set of trained learnable models.

With the integration of the GA along with the back-propagation algorithm, it was found that the trained models attain saturation earlier than the conventional learning technique. Therefore, the proposed approach can be used to train neural-based models faster.

Dataset description

To assess the performance of EvoLearn in comparison with the conventional Back-Propagation algorithm, in this study, several neural-based forecasting models are developed on two different datasets. The details of used datasets are as follows:

Air pollution dataset Increasing air pollution is of critical concern for each nation. Early prediction of rising air pollution (PM2.5 concentration) is critical for proactively mitigating the adverse effects. In the current study, we have employed the proposed EvoLearn approach to predict the PM2.5 concentration of two major states (Punjab and Delhi) in India.

Punjab Punjab has been well known for its agricultural activities and healthy lifestyle. However, with the increase in industrial activities and traffic, there has been a considerable rise in air pollution over the past few years. We gathered data on Punjab’s air pollution ( \(AP_{DT1}\) ) for three years, from January 2018 to January 2021. The dataset is sampled at an interval of 24 hours.

Delhi Delhi is the capital of the country India. For the past few years, it has been suffering from a very high concentration of air pollution. The Delhi government has taken many initiatives to control the level of air pollution in the capital. In this study, we have collected the air pollution dataset of Delhi ( \(AP_{DT2}\) ) for a period of four years, starting from January 2017 to January 2021, with a sampling rate of 24 hours.

Energy consumption dataset Providing sufficient energy supply to all the needy areas is a top priority for each nation. In this context, predicting timestamps ahead of energy demand can help government and private agencies coordinate many activities, such as generation plant scheduling, capacity planning, transmission network optimization etc. The current study gathers data from two Indian states ( Haryana ( \(E_{DT1}\) ) and Punjab ( \(E_{DT2}\) )) to accurately predict their future energy demand. The data for both states is collected every 24 hours for a total of seven years, starting in January 2014 and ending in April 2021.

Performance evaluation

Hyper-parameters selection.

The learning efficiency and accuracy of any prediction model highly depend on the model’s architectural parameters, also known as hyper-parameters. There are several hyper-parameters related to the neural network models used in the present study, like the number of layers in the models, the number of neurons per layer, the type of activation functions, the number of iterations and many more. There exist many strategies to estimate the optimal hyper-parameters value, such as Random search, Grid search and Bayesian optimization. The current research study implements a Grid search strategy for the hyper-parameters tuning task. The method works by initially defining a set of possible values corresponding to each hyper-parameter available for a target neural model. Subsequently, we train a model for each possible combination of hyper-parameters defined and related to the target model. Later, the accuracy of each model is evaluated separately using several performance metrics. Finally, we select the hyper-parameters corresponding to the model with the best prediction accuracy.

Models’ performances (MSE and MAE) on various datasets, trained with the EvoLearn approach and the conventional back-propagation approach.

Evaluation procedure for comparing learning performance

In order to have a fair performance estimation of the conventional method for weight optimization and the proposed EvoLearn approach, the below-listed training strategy is adopted.

The procedure of model selection for the evaluation of models trained using the conventional method: Optimal hyperparameters were first determined using the grid search method to select the best model developed with the conventional learning technique. Moreover, twenty models were trained separately for various numbers of epochs using the obtained hyperparameters. Later, the best-performing model was chosen based on its MSE performance on the testing dataset for the observations.

The procedure of model selection for the evaluation of models trained using the EvoLearn method: Optimal hyperparameters were first determined using the grid search method to select the best model developed with the proposed learning technique. An initial population of twenty models (weight matrices) was generated using the obtained hyperparameters. Each model present in the population was trained for various epoch numbers (represented by ‘x’), with two GA cycles after every five epochs. The best solution obtained from the last generation was chosen for the observations. The values of ‘x’ used in the development of MLP, DNN4, DNN7, CNN, RNN, and GRU-based models are 150, 150, 200, 250, 150, and 200, respectively.

Results comparison and prediction visualization

Six conventional and deep neural network models, namely MLP, DNN4 (DNN with four hidden layers), DNN7 (DNN with seven hidden layers), CNN, RNN, and GRU, were used to estimate the generalization capability and performance improvement achieved by employing the proposed EvoLearn approach. These prediction models were trained using the two different mechanisms. Firstly, the traditional back-propagation-based training was implemented to build prediction models on the four datasets listed in the " Results " Section. Secondly, the models were trained using the proposed EvoLearn (back-propagation integrated GA) approach on the same datasets. Lastly, the prediction performance achieved by using both mechanisms was compared in terms of two different metrics listed in the " Methods " section.

MSE and MAE comparison

In the current research study, we have trained six time-series prediction models corresponding to each training approach, namely the BackPropagation Approach and EvoLearn Approach. These models are trained on four datasets belonging to two different application domains (Energy Load and Air Pollution). Hence, a total of (6 \(\times \) 2 \(\times \) 4 = 48) models have been trained. The performance of these trained models is compared (Tables 1 and 2 ) in terms of Mean Squared Validation (MSE_Validation), Mean Squared Testing (MSE_Test) and Mean Absolute Testing errors (MAE_Test). In order to test if the proposed approach significantly improved the models’ performances, a one-tailed paired T-test was performed over the two groups of each of the performance metrics. The p-value was found to be less than 0.05 in every case, i.e. 9.43E-05, 5.60E-06, and 1.38E-07 for MSE_Validation, MSE_Test, and MAE_Test, respectively. The results reveal that the models’ performances were significantly improved by using the Evolearn approach. Figure 1 a–d depicts a comparative analysis of the prediction performance achieved by different neural models combined with the conventional and EvoLearn training approaches. From the MAE and MSE graphs shown in Fig. 1 , it is evident that the DNN4, DNN7, RNN and GRU models have performed well at capturing the non-linear variations of input time-series datasets. Out of these trained models, GRU models in combination with both model training approaches, have achieved the lowest prediction errors on all four datasets. Furthermore, it can be observed that a significant performance improvement has been gained by all neural models when trained in amalgamation with the EvoLearn approach compared to conventional back-propagation-based training. Hence, it can be concluded that the proposed EvoLearn approach helps neural models in achieving better prediction performance by learning better during the training phase.

Norm_DMSE and Norm_DMAE comparison of the models trained using EvoLearn and back-propagation.

Normalized difference MSE and MAE

In order to measure the performance improvement achieved by combining the proposed EvoLearn approach with different neural models, we have proposed the following two performance measures:

Normalized difference mean squared error It evaluates the normalised difference between the mean squared error value obtained by training neural models using the traditional and EvoLearn approaches. Mathematically, it is given as:

where \(MSE_{EvoLearn}\) and \(MSE_{conventional}\) represent the MSE value of a neural model trained using the EvoLearn approach and the Back-propagation approach, respectively.

Normalized difference mean absolute error It evaluates the normalised difference between the mean absolute error value achieved by training neural models using the traditional and EvoLearn approaches.

where \(MAE_{EvoLearn}\) and \(MAE_{conventional}\) represent the MAE value of a neural model trained using the EvoLearn approach and back-propagation approach, respectively.

Prediction results of proposed EvoLearn approach on testing datasets.

The current research work involves calculating the above-listed metrics for all neural learning models trained in this study. These normalized performance parameters are calculated on two datasets used in the study, and the corresponding results are depicted in Fig. 2 . From the Figure, it can be seen that the integration of the EvoLearn approach has contributed significantly to improving the efficiency of all neural models. However, the highest performance improvement has been achieved in the DNN4 neural network model on all four datasets. Following the same pattern, RNN and DNN7 have also seen a notable reduction in prediction errors by employing the proposed EvoLearn approach. Hence, it can be stated that the proposed method can be efficiently adopted to improve the prediction performance of the time-series prediction models.

Prediction outputs/visualization

The output prediction plots of the proposed EvoLearn + GRU model (Best model) on these datasets are illustrated in Fig. 3 . The prediction results in the figure are shown in red and blue colour, where blue represents the actual values, and the predicted values are shown in red colour. These prediction outputs represent the one-step-ahead forecasting outputs on the testing datasets comprising 15% of the complete datasets used in the study. From the prediction results depicted in Fig. 3 , it is evident that the proposed EvoLearn approach works well at capturing the non-linear and chaotic variations present in the data. Hence, the proposed approach can be efficiently utilized for the time-series analysis and prediction tasks.

Early saturation results.

Early saturation

From the experiments, it has been observed that the models trained through EvoLearn attained saturation earlier than the conventional training method. As shown in Fig. 4 , the red line denoted the MSE of the models at the difference of five epochs trained using Conventional and EvoLearn approaches, whereas the blue line denoted the Validation MSE of the models trained using these approaches. From the figures, it is clearly noticeable that the model trained using EvoLearn reaches the saturation point earlier than the other models. This is because, in each cycle of GA, the models’ weights get optimized by the sequential procedure of crossover, selection, evaluation, and mutation. It helps the new generation (model weights) to have the best node-weights from the previous generation (models).

Furthermore, another reason behind better (model) generation is the proposed fitness function (Sect. " Effects of data complexity/non-linear variations ": Eq. ( 15 )) that is designed to rank the models based on the performance achieved on both the training and validation set. Consequently, EvoLearn produces such models that perform better not only on the training set but also on the validation set. Therefore, it was also observed that the models trained using EvoLearn are less prone to overfitting. A point to be noted here is that the validation set is not used for “learning”, it is only used to assess the performance of the model on unseen data.

Effect of learnable parameters

Through the experiments, it has been observed that there is a slight inverse correlation (– 0.34) between the number of learnable parameters in models and DMSE. A possible explanation for this observation is that in previous studies, GA needs an exponentially higher number of generations to optimize a linearly-bigger solution 47 . Since GA works on search space optimization, a higher dimensional input exponentially increases the space to be explored for the optimal solution. Moreover, in our case, the input to GA is the weights matrices, and the bigger the matrices are, the more generations are needed in order to optimize the values. In our experiments, however, the number of generations to be reproduced while training is fixed irrespective of the size of the weight matrices. Therefore, it is acceptable to have a lesser optimization effect on larger models.

Non-linear variations present in air pollution versus energy dataset.

Effects of data complexity/non-linear variations

From the experiments, it has been noticed that the models trained with EvoLearn performed much better than the conventional method when trained on highly non-linear datasets. In other words, it has been observed that the models’ DMSE and DMAE trained using EvoLearn against the models trained with the conventional approach on a highly non-linear dataset (Air pollution) is more (Fig. 5 ) than on comparatively less non-linear datasets (Energy Demand). A possible reason behind this is that the proposed fitness function is designed to rank the models based on the models’ performance on both training and validation datasets. As discussed before, this observation is also the result of the fact that EvoLearn chooses the models for crossover based on their ability to perform better on unseen data.

EvoLearn’s computational complexity and resource requirements are substantial, particularly when training large-scale neural networks. The evolutionary algorithms demand significant computational power and memory for population-based optimization and fitness evaluations. The process involves numerous iterations and parallel computations, making high-performance computing resources essential. This complexity can limit practical applicability, especially for resource-constrained environments, but offers powerful optimization capabilities for well-resourced setups.

Potential limitations of EvoLearn include high computational costs, slow convergence rates, and difficulty in balancing exploration and exploitation. These challenges can be addressed by optimizing algorithms, employing efficient parallel computing, and using adaptive parameter tuning to enhance performance and scalability. Additionally, hybrid approaches combining evolutionary strategies with other optimization techniques can improve effectiveness and feasibility.

EvoLearn’s findings enable more accurate and efficient predictive models in environmental monitoring and energy management, leading to improved decision-making and resource optimization. Organizations can leverage this method to enhance their predictive analytics capabilities by integrating adaptive and robust model training processes into their workflows.

Time-series forecasting has been one of the hot subjects among scholars for the past several decades. Moreover, many neural-based designs have been proposed to make predictions using the prior sequence of data points. However, the forecasting precision of the models profoundly depends on the training method. Presently, there is a need for a better/faster training methodology in terms of accuracy and training time. In this direction, the present work proposed EvoLearn, a new technique to enhance the training method of neural-based models for forecasting air pollution and energy consumption time series. The presented procedure combines an evolutionary algorithm with the conventional back-propagation algorithm to optimize the model weights throughout the learning process. The basic concept behind EvoLearn is to pick the best parts from multiple models during the training process to produce a superior model. To validate the performance of the proposed method, five different models (MLP, DNN, CNN, RNN, and GRU) were trained on two time-series datasets (Air Pollution and Energy Consumption). Each model was separately trained with two different learning methods (Back-propagation and EvoLearn), and performances were compared. Statistical tests revealed that the proposed EvoLearn approach significantly improved the models’ forecasting performances over the models trained with a simple back-propagation learning algorithm. Furthermore, the GRU-based model with EvoLearn showed the highest forecasting accuracy. Additionally, from the sensitivity analysis, it was found that the models trained with EvoLearn avoided over-fitting and also attained the saturation point earlier than the conventional method. In future, the authors intend to incorporate evolutionary algorithms with NLP and image-based models to enhance the models’ classification accuracy.

This section describes the overall methodology of the proposed EvoLearn approach (shown in Fig. 6 ). Initially, it involves applying data preprocessing and data preparation to generate the desired target representation from the raw input data. Subsequently, several neural network models are developed using the conventional back-propagation and proposed EvoLearn training/learning strategy separately. Lastly, the performance comparison of both learning strategies (Conventional and EvoLearn) is carried out on datasets belonging to two different application domains. The detailed working of each step is explained as follows:

Methodology of the proposed EvoLearn approach.

Data preprocessing

Data preprocessing entails handling inconsistencies or irregularities present in the data. The goal is to improve data quality, which will aid learning models in attaining strong generalization capabilities. Hence, it is one of the crucial steps in building a forecasting model to solve the target problem. The sequence of preprocessing steps required to achieve the desired data quality is not always the same and may differ from problem to problem. In the present research work, we have applied the following preprocessing steps to achieve the desired data quality.

Data cleaning Data cleaning involves filling in missing entries, outlier identification and noise removal. Out of the four datasets used in the study, only a few missing values were present in the air pollution datasets. As a result, linear interpolation is employed in the present study to fill in those missing values.

Data aggregation The dataset collected from the WebCrawler is not a suitable format for input to a machine learning model. So, data aggregation is applied to combine the data from multiple .csv files to a single file required for building/implementing the target model.

Data transformation In the present study, min-max scaling is applied to transform the data into a particular range (0,1). The mathematical equation that corresponds to the scaling strategy used is as follows:

where X and \(X'\) represent the input feature and the normalized output feature, respectively.

Data preparation

Data preparation involves structuring the time series to feed it to the prediction models as input. Defining the degree/amount of historical time-indexed information, a.k.a lag value or look-back, to be used for current timestamp forecasting is essential for building a time-series prediction model. The present research study follows the procedure below to prepare a time series for input to the prediction model.

Consider that we have an input time-series \({\mathcal {T}}\) represented as \(<t_1,\ t_2,\ t_3.....,\ t_n>\) where \(t_i\) denotes the time-series value at timestamp i , and n is the total number of samples present in the input time-series. For the purpose of input to the prediction models, we create a new set S from the existing time series, in which each object consists of a tuple of two time-series as shown below by Eq. ( 6 ).

Where, \(X_i\) denotes the set of independent variables to be used as input features for the prediction models and is given by Eq. ( 7 ). Here, Y is the target prediction variable and is given by \(<t_i>\) .

Splitting dataset

The following step is to divide the dataset \(S_i\) into three parts: training (70%), validation (15%), and testing dataset (15%). The training and validation sections are used to develop/select neural models and obtain an unbiased evaluation of a model fit while estimating optimal hyper-parameters respectively. The testing section is used to assess the accuracy of the trained model on the unknown data.

Models_Initialization_BackPropagation().

Model construction and training

This phase involves building a hybrid approach for the time-series prediction task. The proposed approach works by integrating back-propagation with GA for weight optimization of the shallow and deep neural models. The internal working of each of the undertaken models is as follows:

Multi-layer perceptron (MLP) and deep neural network (DNN) ANN consists of a sequence of layers connected by means of interconnections. Furthermore, each layer in an ANN comprises a set of nodes. Every MLP architecture consists of three main layers, namely the input layer, hidden layers, and output layer. The input layer is responsible for feeding input to the network and does not involve performing any computation on the input data. Following the input layer, there can be one or more hidden layers which are responsible for capturing hidden complexities or features present in the input data. The mathematical equation for a node (j) present in the hidden layer is given as:

where \(w_{ij}\) represents the connection weight from node i in the previous layer to current node j in the hidden layer, act represents the activation function, n denotes the number of nodes in the previous layer and \(x_i\) is the input value at node i in the input layer. This process is repeated for each neuron present in the hidden layer, and the whole process is executed for each hidden layer present in the MLP. Moreover, depending on the complexity of the given problem, there can be multiple hidden layers present in an MLP architecture that allows naming it as a Deep Neural Network model (as it involves defining a large number of hidden layers). The major aim of defining multiple hidden layers is to capture non-linear complexities and multiple feature aspects of the input data.

Convolutional neural networks (CNN) Convolutional neural networks 48 are a class of deep neural network models that researchers have widely adopted to solve complex problems relating to several different application domains. Compared to the traditional neural network models, CNN is based on the concept of receptive field to capture both local and global characteristics patterns of the data. The CNN architecture can be defined as an integration of two separate components, namely the convolutional part and the Regression/Classification part. The convolutional part involves defining several components, such as convolutional filters and pooling to extract deep featural aspects of the input data. The second part connects the extracted feature to the MLP, such as architecture for the target regression or classification tasks. The details of the several CNN components are as follows: The input layer may consist of ’ k ’ neurons where ’ k ’ denotes the size of the input time-series vector. Subsequently, there can be multiple convolutional filters (with different lengths). The aim of defining these filters is to extract different hidden features present in the input-time series, i.e. to define a non-linear transformation function ’ f ’ in each filter to capture different timescale features. The convolutional layer may be followed by a pooling layer (min, max or average pooling) to downsample the convolution output. Finally, after multiple layers of convolution and pooling operations, the output time-series will represent the series of featural maps extracted from the input series. These featural maps are then fed to the dense neural connected layers to generate the target regression or classification output.

Recurrent neural networks (RNN) ANNs are based on the concept that each input sample is independent of the other samples present in the input data. However, the scenario may vary depending on the type of problem under study. In the time-series domain, variations in different timescale aspects might be related to each other and constitute an important factor in estimating the current and future timescale variations. RNNs 49 introduced in 1980, are based on using feedback loops for remembering the previous events’ occurrence information and then using the captured hidden information for estimating the future or current timestamps. The architectural details of the model at each timestamp are given as follows:

At any timestamp t , the activation of the current state is given by:

And, the corresponding output at timestamp t is given by:

where \(W_{hh}\) , \(W_{xh}\) , \(W_{hy}\) are the shared weight coefficients, \(x_t\) , \(y_t\) and \(h_t\) represents the input, output and hidden state at timestamp t .

Gated recurrent unit (GRU) The RNN model works well at capturing the sequential information present in the input data. However, they suffer from the exploding and vanishing gradient problem, which is why these models are not very efficient and reliable at handling the long-term sequential dependencies present in the series. This vanishing/exploding gradient problem has been resolved with the introduction of Gated Recurrent Unit (GRU) models 50 . The GRU model implements a gating mechanism to control several activities, such as the amount of information to be retained from the previous state, which information to retain, information to be thrown, updating the current state, etc. The network involves implementing two gates: reset gate and update gate.

Reset gate This gate entails using the information from both the previous timestamp hidden state and the input at the current timestamp. The aim is to identify the more relevant information from the hidden state and define the new state based on current input and previously filtered relevant information.

Update gate This gate involves computing the final output based on the state and input information. Mathematically, it is given as:

Following are the architectural details of each of the considered models:

Deep neural network (DNN): A sequential neural network with seven layers. The input layer has 16 neurons for the input of shape ‘(window_size,)‘, followed by five hidden layers with 15, 12, 10, 9, and 5 neurons, respectively, using ReLU activation, and a single output neuron with sigmoid activation.

Multilayer perceptron (MLP): A sequential model with two layers. The input layer has 4 neurons with ReLU activation and L2 regularization, accepting input of shape ‘(window_size,)‘, and an output layer with a single neuron using sigmoid activation.

Gated recurrent unit (GRU): A neural network with three layers. The input layer accepts sequences of shape ‘(window_size, 1)‘, followed by a GRU layer with 4 neurons using ReLU activation and a dense output layer with 1 neuron using linear activation.

Convolutional neural network (CNN): A sequential model with three convolutional layers and two dense layers. It includes Conv1D layers with 32, 16, and 4 filters, respectively, each followed by MaxPooling1D layers with a pool size of 2, then a Flatten layer. The dense layers include 5 neurons with ReLU activation and a single output neuron with sigmoid activation.

Recurrent neural network (RNN): A sequential model with two layers. It starts with a SimpleRNN layer with 4 units and no return sequences, accepting input of shape ‘(x_train.shape[1], 1)‘, followed by a dense output layer with ‘output_size‘ neurons using sigmoid activation.

All the models use the Adam optimizer and mean squared error (MSE) loss function. The process of integrating GA with the above-mentioned models involves the following sequence of steps:

fitness_function (model_weights).

Step 1 The very first step in implementing genetic algorithm for the optimization task is to create an initial population. The present research work provides a unique strategy to generate an initial population to be used by the GA-integrated back-propagation algorithm. The sequence of sub-steps involved in this step is as follows:

The strategy works by initially defining the architectural representation of the target neural learning models with randomly initialized weight parameters (lines 2 to 7 in Algorithm 1).

Following this, the defined models are complied and trained using the back-propagation 51 . The set of weights generated after running \(n\_epochs\) of back-propagation represents one candidate of the initial population. This training procedure with random initialization is repeated \(init\_pop\_size\) number of times, where \(init\_pop\_size\) represents the number of candidates or size of the initial population (lines 8 to 11 in Algorithm 1).

Step 2 The next step in the sequence is to evaluate the generated chromosomes (models’ weights) through the proposed fitness function for selecting the candidate models.

The proposed fitness function (Eq. 16 ) helps in avoiding over-training of the models, as the models’ assessment is based on the combined error value obtained on the training as well as the validation dataset. The detailed step-wise working of the fitness function is summarized in Algorithm 2.

Here, M represents the model which is to be assessed, \(data_{train}\) represents the training dataset, and \(data_{valid}\) represents the validation dataset.

Keras_GA_Model (models_weights).

Step 3 This step entails developing/training the proposed GA and back-propagation integrated learning models for the prediction tasks. The overall working of this stage is described in Algorithm 3. It consists of carrying out a two-phase procedure, each of which includes a series of sub-steps.

GA-phase This phase involves employing GA in the learning task. The sequence of steps involved in this phase is as follows: Firstly, the initial population generated by employing the back-propagation (explained in step 1) is passed as an input to the GA. Secondly, the fitness value of all candidate solutions in the input population is calculated. Thirdly, the rank selection (based on the fitness function value) chooses \(init\_pop\_size/4\) best weights candidates for input to the reproduction phase. Finally, the reproduction phase is utilized to generate a new population of weights using the scattered crossover with 0.5 probability and random mutation with a mutant of 10%.

BP-phase The second phase comprises incorporating back-propagation to the above stated GA-based learning task. In this back-propagation phase, the last generation (weights candidates) from the previous phase is used to initialize the weights of the neural models. A total of \(init\_pop\_size\) neural models are trained by running \(n_epochs\) iterations of the back-propagation algorithm with candidates set-based weight initialization. The final weights matrix obtained (size = \(init\_pop\_size\) * the number of learnable weights parameters) after back-propagation becomes new input to the GA, which is then passed to GA phase of this step. This training process is repeated for a defined number of cycles ( \(num\_iterations\) times), alternating between GA and back-propagation-based learning during each epoch.

Step 4 In this step, the performance of the proposed GA and back-propagation integrated neural models is evaluated on the test dataset. After the last cycle, the best model among the generated population is selected for validation.

Model evaluation and performance metrics

Model evaluation is crucial to assess the accuracy and reliability of any prediction model. In the present study, the following well-known metrics are employed to evaluate the performance of the proposed GA and back-propagation integrated neural models.

MSE (mean squared error) 52 : It estimates the quality of the prediction model by calculating the average of square of errors between the actual and the predicted values. Mathematically, it is given as:

where m denotes the number of samples present in the dataset, \(y_s\) & \({\bar{y}}_s\) represent the actual and predicted values respectively.

MAE (mean absolute error) 52 : It measures the average magnitude of a model’s prediction error by calculating the absolute difference between the actual and predicted outcomes.

Data availibility

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Thapar Institute of Engineering And Technology, Patiala, Punjab, India

Jatin Bedi & Ashima Anand

ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India

Samarth Godara, Sudeep Marwaha & Rajender Parsad

ICAR-Indian Agricultural Research Institute, New Delhi, India

Ram Swaroop Bana & Mukhtar Ahmad Faiz

Afghanistan National Agricultural Sciences and Technology University, Kandahar, Afghanistan

Mukhtar Ahmad Faiz

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J.B., A.A. and S.G. designed the proposed models, conceived and conducted the experiments. R.S.B. and M.A.F. collected the data and analysed the results. S.M. and R.P. designed the study and interpreted the results. All authors reviewed the manuscript.

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Bedi, J., Anand, A., Godara, S. et al. Effective weight optimization strategy for precise deep learning forecasting models using EvoLearn approach. Sci Rep 14 , 20139 (2024). https://doi.org/10.1038/s41598-024-69325-3

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Received : 08 December 2023

Accepted : 02 August 2024

Published : 29 August 2024

DOI : https://doi.org/10.1038/s41598-024-69325-3

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