does homework have an impact on school climate

• Future Students
• Current Students
• Faculty/Staff

News and Media

• News & Media Home
• Research Stories
• School's In
• In the Media

You are here

More than two hours of homework may be counterproductive, research suggests.

A Stanford education researcher found that too much homework can negatively affect kids, especially their lives away from school, where family, friends and activities matter.   "Our findings on the effects of homework challenge the traditional assumption that homework is inherently good," wrote Denise Pope , a senior lecturer at the Stanford Graduate School of Education and a co-author of a study published in the Journal of Experimental Education .   The researchers used survey data to examine perceptions about homework, student well-being and behavioral engagement in a sample of 4,317 students from 10 high-performing high schools in upper-middle-class California communities. Along with the survey data, Pope and her colleagues used open-ended answers to explore the students' views on homework.   Median household income exceeded $90,000 in these communities, and 93 percent of the students went on to college, either two-year or four-year.   Students in these schools average about 3.1 hours of homework each night.   "The findings address how current homework practices in privileged, high-performing schools sustain students' advantage in competitive climates yet hinder learning, full engagement and well-being," Pope wrote.   Pope and her colleagues found that too much homework can diminish its effectiveness and even be counterproductive. They cite prior research indicating that homework benefits plateau at about two hours per night, and that 90 minutes to two and a half hours is optimal for high school.   Their study found that too much homework is associated with:   • Greater stress : 56 percent of the students considered homework a primary source of stress, according to the survey data. Forty-three percent viewed tests as a primary stressor, while 33 percent put the pressure to get good grades in that category. Less than 1 percent of the students said homework was not a stressor.   • Reductions in health : In their open-ended answers, many students said their homework load led to sleep deprivation and other health problems. The researchers asked students whether they experienced health issues such as headaches, exhaustion, sleep deprivation, weight loss and stomach problems.   • Less time for friends, family and extracurricular pursuits : Both the survey data and student responses indicate that spending too much time on homework meant that students were "not meeting their developmental needs or cultivating other critical life skills," according to the researchers. Students were more likely to drop activities, not see friends or family, and not pursue hobbies they enjoy.   A balancing act   The results offer empirical evidence that many students struggle to find balance between homework, extracurricular activities and social time, the researchers said. Many students felt forced or obligated to choose homework over developing other talents or skills.   Also, there was no relationship between the time spent on homework and how much the student enjoyed it. The research quoted students as saying they often do homework they see as "pointless" or "mindless" in order to keep their grades up.   "This kind of busy work, by its very nature, discourages learning and instead promotes doing homework simply to get points," said Pope, who is also a co-founder of Challenge Success , a nonprofit organization affiliated with the GSE that conducts research and works with schools and parents to improve students' educational experiences..   Pope said the research calls into question the value of assigning large amounts of homework in high-performing schools. Homework should not be simply assigned as a routine practice, she said.   "Rather, any homework assigned should have a purpose and benefit, and it should be designed to cultivate learning and development," wrote Pope.   High-performing paradox   In places where students attend high-performing schools, too much homework can reduce their time to foster skills in the area of personal responsibility, the researchers concluded. "Young people are spending more time alone," they wrote, "which means less time for family and fewer opportunities to engage in their communities."   Student perspectives   The researchers say that while their open-ended or "self-reporting" methodology to gauge student concerns about homework may have limitations – some might regard it as an opportunity for "typical adolescent complaining" – it was important to learn firsthand what the students believe.   The paper was co-authored by Mollie Galloway from Lewis and Clark College and Jerusha Conner from Villanova University.

Clifton B. Parker is a writer at the Stanford News Service .

More Stories

⟵ Go to all Research Stories

Get the Educator

Subscribe to our monthly newsletter.

Stanford Graduate School of Education

482 Galvez Mall Stanford, CA 94305-3096 Tel: (650) 723-2109

• Contact Admissions
• GSE Leadership
• Site Feedback
• Web Accessibility
• Career Resources
• Faculty Open Positions
• Explore Courses
• Academic Calendar
• Office of the Registrar
• Cubberley Library
• StanfordWho
• StanfordYou

Improving lives through learning

• Stanford Home
• Maps & Directions
• Search Stanford
• Emergency Info
• Terms of Use
• Non-Discrimination
• Accessibility

© Stanford University , Stanford , California 94305 .

Homework could have an impact on kids’ health. Should schools ban it?

Professor of Education, Penn State

Disclosure statement

Gerald K. LeTendre has received funding from the National Science Foundation and the Spencer Foundation.

Penn State provides funding as a founding partner of The Conversation US.

View all partners

Reformers in the Progressive Era (from the 1890s to 1920s) depicted homework as a “sin” that deprived children of their playtime . Many critics voice similar concerns today.

Yet there are many parents who feel that from early on, children need to do homework if they are to succeed in an increasingly competitive academic culture. School administrators and policy makers have also weighed in, proposing various policies on homework .

So, does homework help or hinder kids?

For the last 10 years, my colleagues and I have been investigating international patterns in homework using databases like the Trends in Mathematics and Science Study (TIMSS) . If we step back from the heated debates about homework and look at how homework is used around the world, we find the highest homework loads are associated with countries that have lower incomes and higher social inequality.

Does homework result in academic success?

Let’s first look at the global trends on homework.

Undoubtedly, homework is a global phenomenon ; students from all 59 countries that participated in the 2007 Trends in Math and Science Study (TIMSS) reported getting homework. Worldwide, only less than 7% of fourth graders said they did no homework.

TIMSS is one of the few data sets that allow us to compare many nations on how much homework is given (and done). And the data show extreme variation.

For example, in some nations, like Algeria, Kuwait and Morocco, more than one in five fourth graders reported high levels of homework. In Japan, less than 3% of students indicated they did more than four hours of homework on a normal school night.

TIMSS data can also help to dispel some common stereotypes. For instance, in East Asia, Hong Kong, Taiwan and Japan – countries that had the top rankings on TIMSS average math achievement – reported rates of heavy homework that were below the international mean.

In the Netherlands, nearly one out of five fourth graders reported doing no homework on an average school night, even though Dutch fourth graders put their country in the top 10 in terms of average math scores in 2007.

Going by TIMSS data, the US is neither “ A Nation at Rest” as some have claimed, nor a nation straining under excessive homework load . Fourth and eighth grade US students fall in the middle of the 59 countries in the TIMSS data set, although only 12% of US fourth graders reported high math homework loads compared to an international average of 21%.

So, is homework related to high academic success?

At a national level, the answer is clearly no. Worldwide, homework is not associated with high national levels of academic achievement .

But, the TIMSS can’t be used to determine if homework is actually helping or hurting academic performance overall , it can help us see how much homework students are doing, and what conditions are associated with higher national levels of homework.

We have typically found that the highest homework loads are associated with countries that have lower incomes and higher levels of social inequality – not hallmarks that most countries would want to emulate.

Impact of homework on kids

TIMSS data also show us how even elementary school kids are being burdened with large amounts of homework.

Almost 10% of fourth graders worldwide (one in 10 children) reported spending multiple hours on homework each night. Globally, one in five fourth graders report 30 minutes or more of homework in math three to four times a week.

These reports of large homework loads should worry parents, teachers and policymakers alike.

Empirical studies have linked excessive homework to sleep disruption , indicating a negative relationship between the amount of homework, perceived stress and physical health.

What constitutes excessive amounts of homework varies by age, and may also be affected by cultural or family expectations. Young adolescents in middle school, or teenagers in high school, can study for longer duration than elementary school children.

But for elementary school students, even 30 minutes of homework a night, if combined with other sources of academic stress, can have a negative impact . Researchers in China have linked homework of two or more hours per night with sleep disruption .

Even though some cultures may normalize long periods of studying for elementary age children, there is no evidence to support that this level of homework has clear academic benefits . Also, when parents and children conflict over homework, and strong negative emotions are created, homework can actually have a negative association with academic achievement.

Should there be “no homework” policies?

Administrators and policymakers have not been reluctant to wade into the debates on homework and to formulate policies . France’s president, Francois Hollande, even proposed that homework be banned because it may have inegaliatarian effects.

However, “zero-tolerance” homework policies for schools, or nations, are likely to create as many problems as they solve because of the wide variation of homework effects. Contrary to what Hollande said, research suggests that homework is not a likely source of social class differences in academic achievement .

Homework, in fact, is an important component of education for students in the middle and upper grades of schooling.

Policymakers and researchers should look more closely at the connection between poverty, inequality and higher levels of homework. Rather than seeing homework as a “solution,” policymakers should question what facets of their educational system might impel students, teachers and parents to increase homework loads.

At the classroom level, in setting homework, teachers need to communicate with their peers and with parents to assure that the homework assigned overall for a grade is not burdensome, and that it is indeed having a positive effect.

Perhaps, teachers can opt for a more individualized approach to homework. If teachers are careful in selecting their assignments – weighing the student’s age, family situation and need for skill development – then homework can be tailored in ways that improve the chance of maximum positive impact for any given student.

I strongly suspect that when teachers face conditions such as pressure to meet arbitrary achievement goals, lack of planning time or little autonomy over curriculum, homework becomes an easy option to make up what could not be covered in class.

Whatever the reason, the fact is a significant percentage of elementary school children around the world are struggling with large homework loads. That alone could have long-term negative consequences for their academic success.

• Trends in Mathematics and Science Study (TIMSS)
• Elementary school
• Academic success

Centre Director, Transformative Media Technologies

Stephen Knight Lecturer in Medieval Literature

Postdoctoral Research Fellowship

Social Media Producer

Dean (Head of School), Indigenous Knowledges

Advertisement

Homework and Children in Grades 3–6: Purpose, Policy and Non-Academic Impact

• Original Paper
• Open access
• Published: 12 January 2021
• Volume 50 , pages 631–651, ( 2021 )

Cite this article

You have full access to this open access article

• Melissa Holland   ORCID: orcid.org/0000-0002-8349-7168 1 ,
• McKenzie Courtney 2 ,
• James Vergara 3 ,
• Danielle McIntyre 4 ,
• Samantha Nix 5 ,
• Allison Marion 6 &
• Gagan Shergill 1  

19k Accesses

14 Citations

127 Altmetric

16 Mentions

Explore all metrics

Increasing academic demands, including larger amounts of assigned homework, is correlated with various challenges for children. While homework stress in middle and high school has been studied, research evidence is scant concerning the effects of homework on elementary-aged children.

The objective of this study was to understand rater perception of the purpose of homework, the existence of homework policy, and the relationship, if any, between homework and the emotional health, sleep habits, and parent–child relationships for children in grades 3–6.

Survey research was conducted in the schools examining student ( n  = 397), parent ( n  = 442), and teacher ( n  = 28) perception of homework, including purpose, existing policy, and the childrens’ social and emotional well-being.

Preliminary findings from teacher, parent, and student surveys suggest the presence of modest impact of homework in the area of emotional health (namely, student report of boredom and frustration ), parent–child relationships (with over 25% of the parent and child samples reporting homework always or often interferes with family time and creates a power struggle ), and sleep (36.8% of the children surveyed reported they sometimes get less sleep) in grades 3–6. Additionally, findings suggest misperceptions surrounding the existence of homework policies among parents and teachers, the reasons teachers cite assigning homework, and a disconnect between child-reported and teacher reported emotional impact of homework.

Conclusions

Preliminary findings suggest homework modestly impacts child well-being in various domains in grades 3–6, including sleep, emotional health, and parent/child relationships. School districts, educators, and parents must continue to advocate for evidence-based homework policies that support children’s overall well-being.

Similar content being viewed by others

Understanding the Quality of Effective Homework

Relationships between perceived parental involvement in homework, student homework behaviors, and academic achievement: differences among elementary, junior high, and high school students.

Collaboration between School and Home to Improve Subjective Well-being: A New Chinese Children’s Subjective Well-being Scale

Avoid common mistakes on your manuscript.

Introduction

Children’s social-emotional health is moving to the forefront of attention in schools, as depression, anxiety, and suicide rates are on the rise (Bitsko et al. 2018 ; Child Mind Institute 2016 ; Horowitz and Graf 2019 ; Perou et al. 2013 ). This comes at a time when there are also intense academic demands, including an increased focus on academic achievement via grades, standardized test scores, and larger amounts of assigned homework (Pope 2010 ). This interplay between the rise in anxiety and depression and scholastic demands has been postulated upon frequently in the literature, and though some research has looked at homework stress as it relates to middle and high school students (Cech 2008 ; Galloway et al. 2013 ; Horowitz and Graf 2019 ; Kackar et al. 2011 ; Katz et al. 2012 ), research evidence is scant as to the effects of academic stress on the social and emotional health of elementary children.

Literature Review

The following review of the literature highlights areas that are most pertinent to the child, including homework as it relates to achievement, the achievement gap, mental health, sleep, and parent–child relationships. Areas of educational policy, teacher training, homework policy, and parent-teacher communication around homework are also explored.

Homework and Achievement

With the authorization of No Child Left Behind and the Common Core State Standards, teachers have felt added pressures to keep up with the tougher standards movement (Tokarski 2011 ). Additionally, teachers report homework is necessary in order to complete state-mandated material (Holte 2015 ). Misconceptions on the effectiveness of homework and student achievement have led many teachers to increase the amount of homework assigned. However, there has been little evidence to support this trend. In fact, there is a significant body of research demonstrating the lack of correlation between homework and student success, particularly at the elementary level. In a meta-analysis examining homework, grades, and standardized test scores, Cooper et al. ( 2006 ) found little correlation between the amount of homework assigned and achievement in elementary school, and only a moderate correlation in middle school. In third grade and below, there was a negative correlation found between the variables ( r  =  − 0.04). Other studies, too, have evidenced no relationship, and even a negative relationship in some grades, between the amount of time spent on homework and academic achievement (Horsley and Walker 2013 ; Trautwein and Köller 2003 ). High levels of homework in competitive high schools were found to hinder learning, full academic engagement, and well-being (Galloway et al. 2013 ). Ironically, research suggests that reducing academic pressures can actually increase children’s academic success and cognitive abilities (American Psychological Association [APA] 2014 ).

International comparison studies of achievement show that national achievement is higher in countries that assign less homework (Baines and Slutsky 2009 ; Güven and Akçay 2019 ). In fact, in a recent international study conducted by Güven and Akçay ( 2019 ), there was no relationship found between math homework frequency and student achievement for fourth grade students in the majority of the countries studied, including the United States. Similarly, additional homework in science, English, and history was found to have little to no impact on respective test scores in later grades (Eren and Henderson 2011 ). In the 2015 “Programme of International Student Assessment” results, Korea and Finland are ranked among the top countries in reading, mathematics, and writing, yet these countries are among those that assign the least amount of homework (Organization for Economic Cooperation and Development [OECD] 2016 ).

Homework and Mental Wellness

Academic stress has been found to play a role in the mental well-being of children. In a study conducted by Conner et al. ( 2009 ), students reported feeling overwhelmed and burdened by their exceeding homework loads, even when they viewed homework as meaningful. Academic stress, specifically the amount of homework assigned, has been identified as a common risk factor for children’s increased anxiety levels (APA 2009 ; Galloway et al. 2013 ; Leung et al. 2010 ), in addition to somatic complaints and sleep disturbance (Galloway et al. 2013 ). Stress also negatively impacts cognition, including memory, executive functioning, motor skills, and immune response (Westheimer et al. 2011 ). Consequently, excessive stress impacts one’s ability to think critically, recall information, and make decisions (Carrion and Wong 2012 ).

Homework and Sleep

Sleep, including quantity and quality, is one life domain commonly impacted by homework and stress. Zhou et al. ( 2015 ) analyzed the prevalence of unhealthy sleep behaviors in school-aged children, with findings suggesting that staying up late to study was one of the leading risk factors most associated with severe tiredness and depression. According to the National Sleep Foundation ( 2017 ), the recommended amount of sleep for elementary school-aged children is 9 – 11 h per night; however, approximately 70% of youth do not get these recommended hours. According to the MetLife American Teacher Survey ( 2008 ), elementary-aged children also acknowledge lack of sleep. Perfect et al. ( 2014 ) found that sleep problems predict lower grades and negative student attitudes toward teachers and school. Eide and Showalter ( 2012 ) conducted a national study that examined the relationship between optimum amounts of sleep and student performance on standardized tests, with results indicating significant correlations ( r  = 0.285–0.593) between sleep and student performance. Therefore, sleep is not only impacted by academic stress and homework, but lack of sleep can also impact academic functioning.

Homework and the Achievement Gap

Homework creates increasing achievement variability among privileged learners and those who are not. For example, learners with more resources, increased parental education, and family support are likely to have higher achievement on homework (Hofferth and Sandberg 2001 ; Moore et al. 2018 ; Ndebele 2015 ; OECD 2016 ). Learners coming from a lower socioeconomic status may not have access to quiet, well-lit environments, computers, and books necessary to complete their homework (Cooper 2001 ; Kralovec and Buell 2000 ). Additionally, many homework assignments require materials that may be limited for some families, including supplies for projects, technology, and transportation. Based on the research to date, the phrase “the homework gap” has been coined to describe those learners who lack the resources necessary to complete assigned homework (Moore et al. 2018 ).

Parent–Teacher Communication Around Homework

Communication between caregivers and teachers is essential. Unfortunately, research suggests parents and teachers often have limited communication regarding homework assignments. Markow et al. ( 2007 ) found most parents (73%) report communicating with their child’s teacher regarding homework assignments less than once a month. Pressman et al. ( 2015 ) indicated children in primary grades spend substantially more time on homework than predicted by educators. For example, they found first grade students had three times more homework than the National Education Association’s recommendation of up to 20 min of homework per night for first graders. While the same homework assignment may take some learners 30 min to complete, it may take others up to 2 or 3 h. However, until parents and teachers have better communication around homework, including time completion and learning styles for individual learners, these misperceptions and disparities will likely persist.

Parent–Child Relationships and Homework

Trautwein et al. ( 2009 ) defined homework as a “double-edged sword” when it comes to the parent–child relationship. While some parental support can be construed as beneficial, parental support can also be experienced as intrusive or detrimental. When examining parental homework styles, a controlling approach was negatively associated with student effort and emotions toward homework (Trautwein et al. 2009 ). Research suggests that homework is a primary source of stress, power struggle, and disagreement among families (Cameron and Bartel 2009 ), with many families struggling with nightly homework battles, including serious arguments between parents and their children over homework (Bennett and Kalish 2006 ). Often, parents are not only held accountable for monitoring homework completion, they may also be accountable for teaching, re-teaching, and providing materials. This is particularly challenging due to the economic and educational diversity of families. Pressman et al. ( 2015 ) found that as parents’ personal perceptions of their abilities to assist their children with homework declined, family-related stressors increased.

Teacher Training

As homework plays a significant role in today’s public education system, an assumption would be made that teachers are trained to design homework tasks to promote learning. However, only 12% of teacher training programs prepare teachers for using homework as an assessment tool (Greenberg and Walsh 2012 ), and only one out of 300 teachers reported ever taking a course regarding homework during their training (Bennett and Kalish 2006 ). The lack of training with regard to homework is evidenced by the differences in teachers’ perspectives. According to the MetLife American Teacher Survey ( 2008 ), less experienced teachers (i.e., those with 5 years or less years of experience) are less likely to to believe homework is important and that homework supports student learning compared to more experienced teachers (i.e., those with 21 plus years of experience). There is no universal system or rule regarding homework; consequently, homework practices reflect individual teacher beliefs and school philosophies.

Educational and Homework Policy

Policy implementation occurs on a daily basis in public schools and classrooms. While some policies are made at the federal level, states, counties, school districts, and even individual school sites often manage education policy (Mullis et al. 2012 ). Thus, educators are left with the responsibility to implement multi-level policies, such as curriculum selection, curriculum standards, and disability policy (Rigby et al. 2016 ). Despite educational reforms occurring on an almost daily basis, little has been initiated with regard to homework policies and practices.

To date, few schools provide specific guidelines regarding homework practices. District policies that do exist are not typically driven by research, using vague terminology regarding the quantity and quality of assignments. Greater variations among homework practices exist when comparing schools in the private sector. For example, Montessori education practices the philosophy of no examinations and no homework for students aged 3–18 (O’Donnell 2013 ). Abeles and Rubenstein ( 2015 ) note that many public school districts advocate for the premise of 10 min of homework per night per grade level. However, there is no research supporting this premise and the guideline fails to recognize that time spent on homework varies based on the individual student. Sartain et al. ( 2015 ) analyzed and evaluated homework policies of multiple school districts, finding the policies examined were outdated, vague, and not student-focused.

The reasons cited for homework assignment, as identified by teachers, are varied, such as enhancing academic achievement through practice or teaching self-discipline. However, not all types of practice are equally effective, particularly if the student is practicing the skill incorrectly (Dean et al. 2012 ; Trautwein et al. 2009 ). The practice of reading is one of the only assignments consistently supported by research to be associated with increased academic achievement (Hofferth and Sandberg 2001 ). Current literature supports 15–20 min of daily allocated time for reading practice (Reutzel and Juth 2017 ). Additionally, research supports project-based learning to deepen learners’ practice and understanding of academic material (Williams 2018 ).

Research also shows that homework only teaches responsibility and self-discipline when parents have that goal in mind and systematically structure and supervise homework (Kralovec and Buell 2000 ). Non-academic activities, such as participating in chores (University of Minnesota 2002 ) and sports (Hofferth and Sandberg 2001 ) were found to be greater predictors of later success and effective problem-solving.

Consistent with the pre-existing research literature, the following hypotheses are offered:

Homework will have some negative correlation with children’s social-emotional well-being.

The purposes cited for the assignment of homework will be varied between parents and teachers.

Schools will lack well-formulated and understood homework policies.

Homework will have some negative correlation with children’s sleep and parent–child relationships.

This quantitative study explored, via perception-based survey research, the social and emotional health of elementary children in grades 3 – 6 and the scholastic pressures they face, namely homework. The researchers implemented newly developed questionnaires addressing student, teacher, and parent perspectives on homework and on children’s social-emotional well-being. Researchers also examined perspectives on the purpose of homework, the existence of school homework policies, and the perceived impact of homework on children’s sleep and family relationships. Given the dearth of prior research in this area, a major goal of this study was to explore associations between academic demands and child well-being with sufficient breadth to allow for identification of potential associations that may be examined more thoroughly by future research. These preliminary associations and item-response tendencies can serve as foundation for future studies with causal, experimental, or more psychometrically focused designs. A conceptual framework for this study is offered in Fig.  1 .

Conceptual framework

Research Questions

What is the perceived impact of homework on children’s social-emotional well-being across teachers, parents, and the children themselves?

What are the primary purposes of homework according to parents and teachers?

How many schools have homework policies, and of those, how many parents and teachers know what the policy is?

What is the perceived impact of homework on children’s sleep and parent–child relationships?

The present quantitative descriptive study is based on researcher developed instruments designed to explore the perceptions of children, teachers and parents on homework and its impact on social-emotional well-being. The use of previously untested instruments and a convenience sample preclude any causal interpretations being drawn from our results. This study is primarily an initial foray into the sparsely researched area of the relationship of homework and social-emotional health, examining an elementary school sample and incorporating multiple perspectives of the parents, teachers, and the children themselves.

Participants

The participants in this study were children in six Northern California schools in grades third through sixth ( n  = 397), their parents ( n  = 442), and their teachers ( n  = 28). The mean grade among children was 4.56 (minimum third grade/maximum sixth grade) with a mean age of 9.97 (minimum 8 years old/maximum 12 years old). Approximately 54% of the children were male and 45% were female, with White being the most common ethnicity (61%), followed by Hispanic (30%), and Pacific Islander (12%). Subjects were able to mark more than one ethnicity. Detailed participant demographics are available upon request.

Instruments

The instruments used in this research include newly developed student, parent, and teacher surveys. The research team formulated a number of survey items that, based on existing research and their own professional experience in the schools, have high face validity in measuring workload, policies, and attitudes surrounding homework. Further psychometric development of these surveys and ascertation of construct and content validity is warranted, with the first step being their use in this initial perception-based study. Each of the surveys, developed specifically for this study, are discussed below.

Student Survey

The Student Survey is a 15-item questionnaire wherein the child was asked closed- and open-ended questions regarding their perspectives on homework, including how homework makes them feel.

Parent Survey

The Parent Survey is a 23-item questionnaire wherein the children’s parents were asked to respond to items regarding their perspectives on their child’s homework, as well as their child’s social-emotional health. Additionally, parents were asked whether their child’s school has a homework policy and, if so, if they know what that policy specifies.

Teacher Survey

The Teacher Survey is a 22-item questionnaire wherein the children’s teacher was asked to respond to items regarding their perspectives of the primary purposes of homework, as well as the impact of homework on children’s social-emotional health. Additionally, teachers were asked whether their school has a homework policy and, if so, what that policy specifies.

Data was collected by the researchers after following Institutional Review Board procedures from the sponsoring university. School district approval was obtained by the lead researcher. Upon district approval, individual school approval was requested by the researchers by contacting site principals, after which, teachers of grades 3 – 6 at those schools were asked to voluntarily participate. Each participating teacher was provided a packet including the following: a manila envelope, Teacher Instructions, Administration Guide, Teacher Survey, Parent Packet, and Student Survey. Surveys and classrooms were de-identified via number assignment. Teachers then distributed the Parent Packet to each child’s guardian, which included the Parent Consent and Parent Survey, corresponding with the child’s assigned number. A coded envelope was also enclosed for parents/guardians to return their completed consent form and survey, if they agreed to participate. The Parent Consent form detailed the purpose of the research, the benefits and risks of participating in the research, confidentiality, and the voluntary nature of completing the survey. Parents who completed the consent form and survey sent the completed materials in the enclosed envelope, sealed, to their child’s teacher. After obtaining returned envelopes, with parent consent, teachers were instructed to administer the corresponding numbered survey to the children during a class period. Teachers were also asked to complete their Teacher Survey. All completed materials were to be placed in envelopes provided to each teacher and returned to the researchers once data was collected.

Analysis of Data

This descriptive and quantitative research design utilized the Statistical Package for the Social Sciences (SPSS) to analyze data. The researchers developed coding keys for the parent, teacher, and student surveys to facilitate data entry into SPSS. Items were also coded based on the type of data, such as nominal or ordinal, and qualitative responses were coded and translated where applicable and transcribed onto a response sheet. Some variables were transformed for more accurate comparison across raters. Parent, teacher, and student ratings were analyzed, and frequency counts and percentages were generated for each item. Items were then compared across and within rater groups to explore the research questions. The data analysis of this study is primarily descriptive and exploratory, not seeking to imply causal relationships between variables. Survey item response results associated with each research questionnaire are summarized in their respective sections below.

The first research question investigated in this study was: “What is the perceived impact of homework on children’s social-emotional well-being across teachers, parents, and children?” For this question the examiners looked at children’s responses to how homework makes them feel from a list of feelings. As demonstrated in Table 1 , approximately 44% of children feel “Bored” and about 25% feel “Annoyed” and “Frustrated” toward homework. Frequencies and percentages are reported in Table 1 . Similar to the student survey, parents also responded to a question regarding their child’s emotional experience surrounding homework. Based on parent reports, approximately 40% of parents perceive their child as “Frustrated” and about 37% acknowledge their child feeling “Stress/Anxiety.” Conversely, about 37% also report their child feels “Competence.” These results are reported in Table 1 .

Additionally, parents and teachers both responded to the question, “How does homework affect your student’s social and emotional health?” One notable finding from parent and teacher reports is that nearly half of both parents and teachers reported homework has “No Effect” on children’s social and emotional health. Frequencies and percentages are reported in Table 2 .

The second research question investigated in this study was: “What are parent and teacher perspectives on the primary purposes of homework?” For this question the examiners looked at three specific questions across parent and teacher surveys. Parents responded to the questions, “Does homework relate to your child’s learning?” and “How often is homework busy work?” While the majority of parents reported homework “Always” (45%) or “Often” (39%) relates to their child’s learning, parents also feel homework is “Often” (29%) busy work. The corresponding frequencies and percentages are summarized in Table 3 . Additionally, teachers were asked, “What are the primary reasons you assign homework?” The primary purposes of homework according to the teachers in this sample are “Skill Practice” (82%), “Develop Work Ethic” (61%), and “Teach Independence and Responsibility” (50%). The frequencies and percentages of teacher responses are displayed in Table 4 . Notably, on this survey item, teachers were instructed to choose one response (item), but the majority of teachers chose multiple items. This suggests teachers perceive themselves as assigning homework for a variety of reasons.

The third research question investigated was, “How many schools have homework policies, and of those, how many parents and teachers know what the policy is?” For this question the examiners analyzed parent and teacher responses to the question, “Does your school have a homework policy?” Frequencies and percentages are displayed in Table 5 . Notably, only two out of the six schools included in this study had homework policies. Results indicate that both parents and teachers are uncertain regarding whether or not their school had a homework policy.

The fourth research question investigated was, “What is the perceived impact of homework on children’s sleep and parent–child relationships?” Children were asked if they get less sleep because of homework and parents were asked if their child gets less sleep because of homework. Finally, teachers were asked about the impact of sleep on academic performance. Frequencies and percentages of student, parent, and teacher data is reported in Table 6 . Results indicate disagreement among parents and children on the impact of homework on sleep. While the majority of parents do not feel their child gets less sleep because of homework (77%), approximately 37% of children report sometimes getting less sleep because of homework. On the other hand, teachers acknowledge the importance of sleep in relation to academic performance, as nearly 93% of teachers report sleep always or often impacts academic performance.

To investigate the perceived impact of homework on the parent–child relationship, parents were asked “How does homework impact your child’s relationships?” Almost 30% of parents report homework “Brings us Together”; however, 24% report homework “Creates a Power Struggle” and nearly 18% report homework “Interferes with Family Time.” Additionally, parents and children were both asked to report if homework gets in the way of family time. Frequencies and percentages are reported in Table 7 . Data was further analyzed to explore potentially significant differences between parents and children on this perception as described below.

In order to prepare for analysis of significant differences between parent and child perceptions regarding homework and family time, a Levene’s test for equality of variances was conducted. Results of the Levene’s test showed that equal variances could not be assumed, and results should be interpreted with caution. Despite this, a difference in mean responses on a Likert-type scale (where higher scores equal greater perceived interference with family time) indicate a disparity in parent ( M  = 2.95, SD  = 0.88) and child ( M  = 2.77, SD  = 0.99) perceptions, t (785) = 2.65, p  = 0.008. Results suggest that children were more likely to feel that homework interferes with family time than their parents. However, follow up testing where equal variances can be assumed is warranted upon further data collection.

The purpose of this research was to explore perceptions of homework by parents, children, and teachers of grades 3–6, including how homework relates to child well-being, awareness of school homework policies and the perceived purpose of homework. A discussion of the results as it relates to each research question is explored.

Perceived Impact of Homework on Children’s Social-Emotional Well-Being Across Teachers, Parents, and Students

According to self-report survey data, children in grades 3–6 reported that completing homework at home generates various feelings. The majority of responses indicated that children felt uncomfortable emotions such as bored, annoyed, and frustrated; however, a subset of children also reported feeling smart when completing homework. While parent and teacher responses suggest parents and teachers do not feel homework affects children’s social-emotional health, children reported that homework does affect how they feel. Specifically, many children in this study reported experiencing feelings of boredom and frustration when thinking about completing homework at home. If the purpose of homework is to enhance children’s engagement in their learning outside of school, educators must re-evaluate homework assignments to align with best practices, as indicated by the researchers Dean et al. ( 2012 ), Vatterott ( 2018 ), and Sartain et al. ( 2015 ). Specifically, educators should consider effects of the amount and type of homework assigned, balancing the goal of increased practice and learning with potential effects on children’s social-emotional health. Future research could incorporate a control group and/or test scores or other measures of academic achievement to isolate and better understand the relationships between homework, health, and scholastic achievement.

According to parent survey data, the perceived effects of homework on their child’s social and emotional well-being appear strikingly different compared to student perceptions. Nearly half of the parents who participated in the survey reported that homework does not impact their child’s social-emotional health. Additionally, more parents indicated that homework had a positive effect on child well-being compared to a negative one. However, parents also acknowledge that homework generates negative emotions such as frustration, stress and anxiety in their children.

Teacher data indicates that, overall, teachers do not appear to see a negative impact on their students’ social-emotional health from homework. Similar to parent responses, nearly half of teachers report that homework has no impact on children’s social-emotional health, and almost one third of teachers reported a positive effect. These results are consistent with related research which indicates that teachers often believe that homework has positive impacts on student development, such as developing good study habits and a sense of responsibility (Bembenutty 2011 ). It should also be noted, not a single teacher reported the belief that homework negatively impacts children’s’ social and emotional well-being, which indicates clear discrepancies between teachers’ perceptions and children’s feelings. Further research is warranted to explore and clarify these discrepancies.

Primary Purposes of Homework According to Teachers and Parents

Results from this study suggest that the majority of parents believe that homework relates and contributes to their child’s learning. This finding supports prior research which indicates that parents often believe that homework has long-term positive effects and builds academic competencies in students (Cooper et al. 2006 ). Notably, however, nearly one third of parents also indicate that homework is often given as busy work by teachers. Teachers reported that they assigned homework to develop students’ academic skills, work ethic, and teach students responsibility and promote independence. While teachers appear to have good intentions regarding the purpose of homework, research suggests that homework is not an effective nor recommended practice to achieve these goals. Household chores, cooking, volunteer experiences, and sports may create more conducive learning opportunities wherein children acquire work ethic, responsibility, independence, and problem-solving skills (Hofferth and Sandberg 2001 ; University of Minnesota 2002 ). Educators should leverage the use of homework in tandem with other student life experiences to best foster both academic achievement and positive youth development more broadly.

Homework Policies

As evident from parent responses, the majority of parents are unaware if their child’s school has a homework policy and many teachers are also uncertain as to whether their school provides restrictions or guidelines for homework (e.g., amount, type, and purpose). Upon contacting school principals, it was determined that only two of the six schools have a school-wide homework policy. Current data indicates the professionals responsible for assigning homework appear to be unclear about whether their school has policies for homework. Additionally, there appears to be a disconnect between parents and teachers regarding whether homework policies do exist among the sampled schools. The research in the current study is consistent with previous research indicating that policies, if they do exist, are often vague and not communicated clearly to parents (Sartain et al. 2015 ). This study suggests that homework policies in these districts require improved communication between administrators, teachers, and parents.

Perceived Impact of Homework on Children’s Sleep and Parent–Child Relationships

Regarding the importance of sleep on academic performance, nearly all of the teachers included in this study acknowledged the impact that sleep has on academic performance. There was disagreement among children and parents on the actual impact that homework has on children’s sleep. Over one third of children report that homework occasionally detracts from their sleep; however, many parents may be unaware of this impact as more than three quarters of parents surveyed reported that homework does not impact their child’s sleep. Thus, while sleep is recognized as highly important for academic achievement, homework may be adversely interfering with students’ full academic potential by compromising their sleep.

In regard to homework’s impact on the parent child-relationship, parents in this survey largely indicated that homework does not interfere in their parent–child relationship. However, among the parents who do notice an impact, the majority report that homework can create a power struggle and diminish their overall family time. These results are consistent with Cameron and Bartell’s ( 2009 ) research which found that parents often believe that excessive amounts of homework often cause unnecessary family stress. Likewise, nearly one third of children in this study reported that homework has an impact on their family time.

This study provides the foundation for additional research regarding the impact of academic demands, specifically homework, on children’s social-emotional well-being, including sleep, according to children, parents, and teachers. Additionally, the research provides some information on reasons teachers assign homework and a documentation of the lack of school homework policies, as well as the misguided knowledge among parents and teachers about such policies.

The preexisting literature and meta-analyses indicate homework has little to no positive effect on elementary-aged learners’ academic achievement (Cooper et al. 2006 ; Trautwein and Köller 2003 ; Wolchover 2012 ). This led to the question, if homework is not conducive to academic achievement at this level, how might it impact other areas of children’s lives? This study provides preliminary information regarding the possible impact of homework on the social-emotional health of elementary children. The preliminary conclusions from this perception research may guide districts, educators, and parents to advocate for evidence-based homework policies that support childrens’ academic and social-emotional health. If homework is to be assigned at the elementary level, Table 8 contains recommended best practices for such assignment, along with a sample of specific guidelines for districts, educators, and parents (Holland et al. 2015 ).

Limitations and Recommendations for Future Research

Due to the preliminary nature of this research, some limitations must be addressed. First, research was conducted using newly developed parent, teacher, and student questionnaires, which were not pilot tested or formally validated. Upon analyzing the data, the researchers discovered limitations within the surveys. For example, due to the nature of the survey items, the variables produced were not always consistently scaled. This created challenges when making direct comparisons. Additionally, this limited the sophistication of the statistical procedures that could be used, and reliability could not be calculated in typical psychometric fashion (e.g., Cronbach’s Alpha). Secondly, the small sample size may limit the generalizability of the results, especially in regard to the limited number of teachers (n  = 28) we were able to survey. Although numerous districts and schools were contacted within the region, only three districts granted permission. These schools may systematically differ from other schools in the region and therefore do not necessarily represent the general population. Third, this research is based on perception, and determining the actual impacts of homework on child wellness would necessitate a larger scale, better controlled study, examining variables beyond simple perception and eliminating potentially confounding factors. It is possible that individuals within and across rater groups interpreted survey items in different ways, leading to inconsistencies in the underlying constructs apparently being measured. Some phrases such as “social-emotional health” can be understood to mean different things by different raters, which could have affected the way raters responded and thus the results of this study. Relatedly, causal links between homework and student social-emotional well-being cannot be established through the present research design and future research should employ the use of matched control groups who do not receive homework to better delineate the direct impact of homework on well-being. Finally, interpretations of the results are limited by the nested nature of the data (parent and student by teacher). The teachers, parents, and students are not truly independent groups, as student and parent perceptions on the impact of homework likely differ as a function of the classroom (teacher) that they are in, as well as the characteristics of the school they attend, their family environment, and more. The previously mentioned challenge of making direct comparisons across raters due to the design of the surveys, as well as small sample size of teachers, limited the researchers’ ability to address this issue. Future research may address this limitation by collecting data and formulating related lines of inquiry that are more conducive to the analysis of nested data. At this time, this survey research is preliminary. An increased sample size and replication of results is necessary before further conclusions can be made. Researchers should also consider obtaining data from a geographically diverse population that mirrors the population in the United States, and using revised surveys that have undergone a rigorous validation process.

Abeles, V., & Rubenstein, G. (2015). Beyond measure: Rescuing an overscheduled, overtested, underestimated generation . New York: Simon & Schuster.

Google Scholar  

American Psychological Association (APA). (2009). Stress in America 2009 . https://bit.ly/39FiTk3 .

American Psychological Association (APA). (2014). Stress in America: Are teens adopting adults’ stress habits? https://bit.ly/2SIiurC .

Baines, L. A., & Slutsky, R. (2009). Developing the sixth sense: Play. Educational HORIZONS ,  87 (2), 97–101. https://bit.ly/39LLGU4 .

Bembenutty, H. (2011). The last word: An interview with harris cooper—Research, policies, tips, and current perspectives on homework. Journal of Advanced Academics, 22 (2), 340–349. https://doi.org/10.1177/1932202X1102200207 .

Article   Google Scholar  

Bennett, S., & Kalish, N. (2006). The case against homework: How homework is hurting our children and what we can do about it . New York: Crown Publishers.

Bitsko, R. H., Holbrook, J. R., Ghandour, R. M., Blumberg, S. J., Visser, S. N., Perou, R., & Walkup, J. T. (2018). Epidemiology and impact of health care provider:Diagnosed anxiety and depression among US children. Journal of Developmental and Behavioral Pediatrics, 39 (5), 395. https://doi.org/10.1097/DBP.0000000000000571 .

Article   PubMed   PubMed Central   Google Scholar  

Cameron, L., & Bartell, L. (2009). The researchers ate the homework! Toronto: Canadian Education Association.

Carrion, V. G., & Wong, S. S. (2012). Can traumatic stress alter the brain? Understanding the implications of early trauma on brain development and learning. Journal of Adolescent Health, 51 (2), S23–S28. https://doi.org/10.1016/j.jadohealth.2012.04.010 .

Cech, S. J. (2008). Poll of US teens finds heavier homework load, more stress over grades. Education Week, 27 (45), 9.

Child Mind Institute. Children’s mental health report . (2015). https://bit.ly/2SSzE4v .

Child Mind Institute. (2016). 2016 children’s mental health report . https://bit.ly/2SQ9kbj .

Conner, J., Pope, D., & Galloway, M. (2009). Success with less stress. Educational Leadership, 67, 54–58.

Cooper, H. (2001). Homework for all–in moderation. Educational leadership, 58 (7), 34–38.

Cooper, H., Robinson, J. C., & Patall, E. A. (2006). Does homework improve academic achievement? A synthesis of research, 1987–2003. Review of Educational Research, 76 (1), 1–62. https://doi.org/10.3102/00346543076001001 .

Dean, C. B., Hubbell, E. R., Pitler, H., & Stone, B. J. (2012). Classroom instruction that works: Research-based strategies for increasing student achievement . Alexandria: ASCD.

Eide, E. R., & Showalter, M. H. (2012). Sleep and student achievement. Eastern Economic Journal, 38 (4), 512–524. https://doi.org/10.1057/eej.2011.33 .

Eren, O., & Henderson, D. J. (2011). Are we wasting our children’s time by giving them more homework? Economics of Education Review, 30 (5), 950–961. https://doi.org/10.1016/j.econedurev.2011.03.011 .

Galloway, M., Conner, J., & Pope, D. (2013). Nonacademic effects of homework in privileged, high-performing high schools. The Journal of Experimental Education, 81 (4), 490–510. https://doi.org/10.1080/00220973.2012.745469 .

Greenberg, J., & Walsh, K. (2012). What teacher preparation programs teach about k-12 assessment: A review . Washington: National Council on Teacher Quality.

Güven, U., & Akçay, A. O. (2019). Trends of homework in mathematics: Comparative research based on TIMSS study. International Journal of Instruction., 12 (1), 1367–1382.

Hofferth, S. L., & Sandberg, J. F. (2001). How American children spend their time. Journal of Marriage and Family, 63 (2), 295–308. https://doi.org/10.1111/j.1741-3737.2001.00295.x .

Holland, M. L., Sisson, H., & Abeles, V. (2015, January). Academic demands, homework, and social–emotional health. NASP Communique, 45 (5), 10.

Holte, K. (2015). Homework in primary school: Could it be made more child-friendly? Studia Paedagogica, 21 (4), 13–33. https://doi.org/10.5817/SP2016-4-1 .

Horowitz, J. M., & Graf, N. (2019). Most U.S. teens see anxiety and depression as a major problem among their peers. Pew Research Center. https://pewrsr.ch/32kUmhU .

Horsley, M., & Walker, R. (2013). Reforming homework: Practices, learning and policies . South Yarra: Palgrave Macmillan.

Kackar, H. Z., Shumow, L., Schmidt, J. A., & Grzetich, J. (2011). Age and gender differences in adolescents’ homework experiences. Journal of Applied Developmental Psychology, 32 (2), 70–77. https://doi.org/10.1016/j.appdev.2010.12.005 .

Katz, I., Buzukashvili, T., & Feingold, L. (2012). Homework stress: Construct validation of a measure. The Journal of Experimental Education, 80 (4), 405–421. https://doi.org/10.1080/00220973.2011.610389 .

Kralovec, E., & Buell, J. (2000). The end of homework: How homework disrupts families, overburdens children, and limits learning . Boston: Beacon Press.

Leung, G. S., Yeung, K. C., & Wong, D. F. (2010). Academic stressors and anxiety in children: The role of paternal support. Journal of Child and Family Studies, 19 (1), 90–100. https://doi.org/10.1007/s10826-009-9288-4 .

Markow, D., Kim, A., & Liebman, M. (2007). The Metlife survey of the American teacher . New York: Metlife, Inc. https://files.eric.ed.gov/fulltext/ED530021.pdf .

MetLife. (2008). MetLife American teacher survey grades the state of homework . (ED500012). https://www.metlife.com/aboutus/newsroom/2008/february/metlife-american-teacher-survey-grades-the-state-of-homework/ .

Moore, R., Vitale, D., & Stawinoga, N. (2018). The digital divide and educational equity: A look at students with very limited access to electronic devices at home (Insights in Education and Work) . Iowa City: ACT Inc.

Mullis, I. V. S., Martin, M. O., Minnich, C. A., Stanco, G. M., Arora, A., & Centurino, V. A. S. (2012). TIMSS 2011 encyclopedia: Education policy and curriculum in mathematics and science (Vols. 1, 2). Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College. https://timssandpirls.bc.edu/timss2011/encyclopedia-timss.html .

National Sleep Foundation. (2017). How much sleep do babies and kids need? https://bit.ly/37E157D .

Ndebele, M. (2015). Socio-economic factors affecting parents’ involvement in homework: Practices and perceptions from eight Johannesburg public primary schools. Perspectives in Education, 33 (3), 72–91.

O’Donnell, M. (2013). Maria Montessori: A critical introduction to key themes and debates . Bloomsbury Academic: Bloomsbury Academic.

Organization for Economic Co-operation and Development (OECD). (2016). PISA 2015 results in focus. https://bit.ly/2T1NUb0 .

Perfect, M. M., Levine-Donnerstein, D., Archbold, K., Goodwin, J. L., & Quan, S. F. (2014). The contribution of sleep problems to academic and psychosocial functioning. Psychology in the Schools, 51 (3), 273–295. https://doi.org/10.1002/pits.21746 .

Perou, R., Bitsko, R. H., Blumberg, S. J., Pastor, R., Ghandour, R. M., Gfroerer, J. C., et al. (2013). Mental health surveillance among children: United States, 2005–2011. MMWR, 62 (2), 1–35.

Pope, D. (2010). Beyond ‘doing school’: From ‘stressed-out’ to ‘engaged in learning.’ Education Canada, 50 (1), 4–8.

Pressman, R. M., Sugarman, D. B., Nemon, M. L., Desjarlais, J., Owens, J. A., & Schettini-Evans, A. (2015). Homework and family stress: With consideration of parents’ self-confidence, educational level, and cultural background. The American Journal of Family Therapy, 43 (4), 297–313. https://doi.org/10.1080/01926187.2015.1061407 .

Reutzel, D. R., & Juth, S. (2017). Supporting the development of silent reading fluency: An evidence-based framework for the intermediate grades (3–6). International Electronic Journal of Elementary Education, 7 (1), 27–46.

Rigby, J. G., Woulfin, S. L., & März, V. (2016). Understanding how structure and agency influence education policy implementation and organizational change. American Journal of Education, 122 (3), 295–302. https://doi.org/10.1086/685849 .

Sartain, C., Glenn, L., Jones, J., Merritt, D., (2015). A policy analysis of district homework policies [Doctoral dissertation, Saint Louis University]. ProQuest Dissertations and Theses. https://bit.ly/2vSDxOT .

Sisson, H. F. (2015). Academic demands on the social-emotional health of primary and secondary grade students [Master’s thesis, California State University, Sacramento]. Sacramento State Scholarworks. https://bit.ly/2vNyMGq .

Tokarski, J. E. (2011). Thoughtful homework or busywork: Impact on student success [Master’s thesis, Dominican University of California]. Dominican Scholar. https://bit.ly/38UrQWw .

Trautwein, U., & Köller, O. (2003). The relationship between homework and achievement: Still much of a mystery. Educational Psychology Review, 15 (2), 115–145. https://doi.org/10.1023/A:1023460414243 .

Trautwein, U., Niggli, A., Schnyder, I., & Lüdtke, O. (2009). Between-teacher differences in homework assignments and the development of students’ homework effort, homework emotions, and achievement. Journal of Educational Psychology, 101 (1), 176–189. https://doi.org/10.1037/0022-0663.101.1.176 .

University of Minnesota (2002). Involving children in household tasks: Is it worth the effort . https://bit.ly/2TkcUe7

Vatterott, C. (2018). Rethinking homework: Best practices that support diverse needs (2nd ed.). Alexandria: ASCD.

Westheimer, K., Abeles, V., & Truebridge, S. (2011).  End the race: A facilitation guide and companion resource to the film race to nowhere . Reel Link Films. https://bit.ly/2T1FG2D .

Williams, D. L. (2018). The impact of project-based learning on fourth-grade students’ understanding in reading. [Doctoral dissertation, Capella University] ProQuest Dissertations and Theses. https://bit.ly/2SIjvzD .

Wolchover, N. (2012, March 30). Too much homework can lower test scores, researchers say. Huffington Post . https://bit.ly/38EdJoi .

Zhou, Y., Siu, A. F., & Wai, S. T. (2015). Influences of unhealthy sleep behaviors on the excessive daytime sleepiness and depressive symptoms in children. Journal of Child and Family Studies, 24 (7), 2120–2126. https://doi.org/10.1007/s10826-014-0013-6 .

Download references

Author information

Authors and affiliations.

College of Education, California State University, 6000 J Street, Sacramento, CA, 95819, USA

Melissa Holland & Gagan Shergill

Buckeye Union School District, 5049 Robert J. Mathews Parkway, El Dorado Hills, CA, 95762, USA

McKenzie Courtney

Institute for Social Research, CSUS, 304 S Street, Suite 333, Sacramento, CA, 95811, USA

James Vergara

Dry Creek Joint Elementary School District, 8849 Cook Riolo Road, Roseville, CA, 95747, USA

Danielle McIntyre

Natomas Unified School District, 1901 Arena Blvd, Sacramento, CA, 95834, USA

Samantha Nix

Panama-Buena Vista Union School District, 4200 Ashe Rd, Bakersfield, CA, 93313, USA

Allison Marion

You can also search for this author in PubMed   Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by all authors. All authors contributed on this first draft and on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Melissa Holland .

Ethics declarations

Conflict of interest.

All authors declare that they have no conflict of interest.

Ethics Approval

Ethics approval was granted by the Institutional Review Board at the sponsoring university. This research was approved by the Institutional Review Board (IRB) at the sponsoring university. Data was collected by the researchers after following the IRB procedures. This study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments.

Consent to Participate

Consent forms used in this research available upon request addressed to the corresponding author.

Access to Data and Material

Available upon request addressed to the corresponding author.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Holland, M., Courtney, M., Vergara, J. et al. Homework and Children in Grades 3–6: Purpose, Policy and Non-Academic Impact. Child Youth Care Forum 50 , 631–651 (2021). https://doi.org/10.1007/s10566-021-09602-8

Download citation

Accepted : 05 January 2021

Published : 12 January 2021

Issue Date : August 2021

DOI : https://doi.org/10.1007/s10566-021-09602-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Social emotional health
• Elementary students
• Find a journal
• Publish with us
• Track your research

Texas A&M University

College of education & human development.

Analyzing Homework’s Impact

By SEHD Communications

It has been a debate for decades. Children are unhappy about doing homework and teachers insist that homework is key to helping students learn.

In recent years, parents have joined in the debate, complaining their children are stressed out because of an increased workload. That has prompted school districts across the country to re-examine their homework policies.

When we learned of Katy ISD’s plans to implement several no-homework nights this school year, we took the homework debate to Dr. Jeffrey Liew, professor of educational psychology.

Dr. Liew points to research that says not all homework is created equally – it depends on a variety of factors such as the type of assignments, the school subject, and the grade level.

Several studies show elementary students who do homework fare no better in school than students who do not. There is a slight uptick in higher grade levels, but the impact is minimal. However, Dr. Liew says homework can be beneficial to learning and achievement – because some subjects and school projects require more practice or time to complete than others.

“Ultimately, it is important to think about why homework is being assigned and how effectively homework assignments will serve to achieve learning objectives or goals,” he said. “Homework can benefit children’s learning, but not when homework assignments are used simply as a way to keep children busy or preoccupied after school.”

Most of Dr. Liew’s own research relates to child development. When we asked him about homework’s impact on free play and creative thinking, he said he is not surprised when he sees students struggle with certain life skills after they graduate from high school and go off to college or enter the workforce.

“When homework assignments dictate children’s after-school schedules, that leaves them little to no time for social or recreational activities. It also leaves them little to no time for learning critical life skills such as developing self-interests, self-initiative or autonomy, and peer relationships. These social emotional learning or life skills are critical for their development as a whole-person – skills for success that they will need for a lifetime.”

When children come home without homework, or on the designated no-homework nights, Dr. Liew has suggestions for parents.

“Rather than always controlling what their children should do when there is free time, parents who give their children some freedom to suggest and choose what they could do together are helping promote their children’s planning and decision-making skills.”

The goal for many parents on these no-homework nights is to also build and strengthen the relationships and bonds with their children. While some teachers argue parents can also accomplish that with homework, as it turns out, some parents don’t feel they have the background needed to help. This causes stresses for both children and parents.

“Having some homework assignments, especially when children progress into the higher grades can really help them master concepts and skills. But too much homework may be counterproductive for both students and parents” explained Dr. Liew. “And schools need to consider the diversity in home situations, as not all students come from homes with parents in a position to help their children with homework.”

For Dr. Liew, he sees the homework debate as an opportunity to focus attention on the need for ways to improve and enhance the schooling experience to ensure quality and equity in education for all children.

About the Writer

Ashley is the Media Relations Coordinator and responsible for news coverage in the Department of Teaching, Learning and Culture as well as the Department of Educational Psychology.

For media inquiries, contact  Ashley Green.

Fundraising

Recent posts.

Aggie Teacher Recognized for Leadership in the Classroom

The WORDS Project: SEHD Researcher’s Partnership To Enhance Literacy

The WORDS Project: SEHD Researcher’s Partnership To Enhance Literacy Dr. Marc Goodrich…

Student Spotlight: Lauren Hummel

KNSM Student Manages Goals On and Off the Pitch

Hart Ray ‘24, a recent graduate of our Sport Management program worked as a student manager for the Fightin’ Texas Aggie Women’s Soccer Team.

Tennis Star Leading On and Off the Court

Sport Management Student Mary Stoiana ’25 is applying classroom lessons on the tennis courts and leading team to new heights.

Meet KNSM’s New Faculty

Meet the new faculty members of SEHD’s Department of Kinesiology & Sport Management.

Honoring Former Student’s Legacy

The School of Education and Human Development dedicated a tree and bench honoring the memory and legacy of former educator Margo K. Dailey.

NASA Recognizes Former Student’s Support of Underrepresented Groups

Former graduate student Dr. Austin Mardon ’90 has been honored with NASA’s Diversity, Equity, Inclusion and Accessibility (DEIA) Medal.

Dr. Khalil Dirani Appointed EAHR Department Head

Dr. Khalil M. Dirani has been appointed the new head of our School’s Department of Educational Administration and Human Resource Development.

Former Graduate Student Competes for America’s Favorite Teacher Title

Aggie graduate Joyce Welch is competing for the title of America’s Favorite Teacher.

• child development
• educational psychology
• jeffrey liew

About SEHD Communications

Related articles.

Can't find what you are looking for?

May 2020 — Volume 20, No. 2

Why School Climate Matters and What Can Be Done to Improve It

A positive school climate—where students feel a sense of safety and belonging and where relational trust prevails— improves academic achievement, test scores, grades, and engagement and helps reduce the negative effects of poverty on academic achievement. To bring about such environments, teachers, paraprofessionals, and school and district leaders must be prepared to create the school and classroom structures that encourage secure relationships.

Related Content

Fostering healthy school climates for pandemic recovery.

This webinar discusses school climate in the context of coronavirus recovery.

What Learning and Developmental Science Says about Optimal Learning Environments

We know what leads children toward healthy development. We just need to build it.

Why School Climate Matters for Teachers and Students

Teachers in strong climates get better faster, stay longer, and propel their students to greater heights.

Also In this Issue

Educators’ abilities to forge strong relationships with students lie at the heart of strong schools.

School Climate and Measurement

All schools should be using climate data, which can be used statewide for accountability, too.

School Discipline Reform Is Still Needed, but Is Discipline Policy Still the Solution?

Addressing disparities requires a broader, deeper look at school culture, process, and practice.

Measures of SEL and School Climate in California

CORE Districts plumb the possibilities of using holistic measures to improve schools.

Creating Equitable School Climates

How states and districts are weaving equity into their efforts to improve climate.

Removing Barriers to LGBTQ Student Safety and Achievement

Comprehensive protections from bullying and harassment help everyone.

Featured Items

Business concept vector illustration of a businessman looking the red lines which leading to the top of a mountain

New State Strategic Plans Zero In on Learning Recovery

Image credit: iStock

Preschool for All

A group if University students sit on the grass outside on campus as they take a break between classes. They are each dressed casually and have their cell phones out as they catch up on social media.

Six Questions to Advance Media Literacy and Digital Citizenship

Upcoming events, july 9, 2024 — new member institute new member institute 2024, october 23, 2024 — annual conference annual conference 2024, from the states, states face challenges building a school mental health workforce.

Challenges persist in attracting and retaining this school-based mental health professionals, but some states, including Virginia, Ohio, Colorado, Michigan, and others, are making progress.

States Address Access to Menstrual Products in Schools

States such as Georgia, New York, Utah, and others have increasingly opted to combat “period poverty" by expanding access to free menstrual products in schools.

Advanced Search

Whitepapers

• Newsletters
• New Product Awards
• K-12 Grants
• Tech Tactics in Education

Share this Page

• del.icio.us
• Printable Format
• E-Mail this page

School Climate Really Does Affect Academics

• By Dian Schaffhauser

A recent study found that middle schools where students reported a "more positive" school climate had higher academic performance, and that changes in the school climate also correlated with changes in academic performance. School climate encompasses the social, emotional and physical characteristics of a school community.

The study, produced by the Institute of Education Sciences National Center for Educational Evaluation and Regional Assistance , a division of the United States Department of Education , examined data taken from three sources: the California Healthy Kids Survey , the California Standardized Testing and Reporting program, and the California Basic Educational Data System . (The STAR program has since between replaced by the California Assessment of Student Performance and Progress System .) Researchers used grade seven data from 978 middle schools in California collected from the school years running from 2004-05 through 2010-11.

California data was specifically used, the researchers stated, because it has been a "leader" among states in shifting its practices to incorporate school climate as an aspect of school performance. For example, the state has legislated districts to work with its community members to identify needs related to the improvement of school climate, to create action plans for addressing those needs and to figure out how progress will be measured. Those requirements are part of California's district funding stipulations.

School climate is measured through a set of student survey questions that ask how students feel about six areas: safety/connectedness, caring relationships with adults, meaningful student participation, levels of substance use at school, bullying/discrimination, and student delinquency. Those schools with "positive" school climates showed high levels of the first three characteristics and low levels of the latter three.

The researchers used regression modeling to examine the connection between the student-reported school climate and the students' average academic performance across schools. They also looked at how academic performance changed in relation to changes in the school climate. Both models controlled for racial and ethnic composition, the percentage of English learners, and the percentage of students eligible for free and reduced-price meals.

Research has long asserted that a more positive school climate will produce higher academic performance. However, those studies have tended to focus on comparing academic performance across schools with different school climates using data collected "at a single point in time," according to researchers Adam Voight, director of the Center for Urban Education at Cleveland State University , and Thomas Hanson, associate director of the Health & Human Development Program at WestEd , an education research firm that runs the Healthy Kids survey for California.

What has been less studied is how changes in a school's climate over time are associated with changes in the school's academic performance. This research project undertook to examine just that kind of longitudinal impact.

The study reported three distinct findings:

• Schools with a more positive student-reported school climate had higher average academic performance. For example, a school with a school climate that was 10 percentage points higher than that of another school had an average test score that was 2.5 percentage points higher in English language arts and 3.4 percentage points higher in math. The test score tie was strongest for three elements of school climate measurement: safety and connectedness, substance use at school, and student delinquency.
• School-level changes in student-reported school climate over time were often related to simultaneous changes in academic performance over time. A 10 percentage point increase in school climate was associated with a 0.5 percentage point increase in the average English language arts test score and a 0.7 percentage point increase in the average math test score over a two-year period.
• The impact of changes in school climate on academic performance within a school over time was smaller than the differences in academic performance across schools with different school climate values in a given year. For example, the researchers explained, in a given year schools at the 50th percentile on school climate were at the 48th percentile on math performance, on average, while schools at the 60th percentile on school climate were at the 51st percentile on math performance. This would suggest that an improvement of 10 percentage points in school climate would be associated with an average 3 percentage point increase in academic performance. However, what the study found was that over time, schools with a 10 percentage point increase in student perceptions of school climate averaged a less than 1 percentage point increase in academic performance.

Thus, the report noted, even though schools with positive school climate values had substantially better academic performance than those with lower school climate values, the differences weren't an accurate guide for predicting the magnitude of the increase. There might be two reasons for that, according to the researchers. First, there might be more longitudinal variation in school climate than in academic performance across the seven years of data analyzed. Second, school climate could be affected by "random temporal factors," whereas differences across schools in school climate might reflect true difference in school climate; the longitudinal association could "understate" the longer-term impact of changing school climate on academic performance.

" How are middle school climate and academic performance related across schools and over time? " is available on the IES website here .

About the Author

Dian Schaffhauser is a former senior contributing editor for 1105 Media's education publications THE Journal, Campus Technology and Spaces4Learning.

• Most Popular Articles
• Most Emailed Articles

Teachers Report Increase in AI Usage

Call for speakers reopened: tech tactics in education 2024, report: gen z students expect ai to negatively impact society, microsoft beefs up copilot education features, expands khanmigo for teachers, k–12 grants & upcoming events, microsoft, khan academy partner to make khanmigo teaching tool free.

Reduce Copper Wire Phone Costs and Keep Your Campus Safe

• Innovation Unleashed: Exploring AI's Impact on Education IT Documentation
• 10 Proven Strategies for Effective Educator Growth Planning

All Webcasts

The Kokomo K-12 Blueprint

Working in education has never been more demanding and challenging than it is today. Download this whitepaper today to learn about a Unified Strategy to keep schools safe and students healthy! Read more...

All Whitepapers

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Front Psychol

The Impact of School Climate on Well-Being Experience and School Engagement: A Study With High-School Students

Elisabetta lombardi.

1 Department of Psychology, Catholic University of Milan, Milan, Italy

Daniela Traficante

Roberta bettoni.

2 Carlo Besta Neurological Institute (IRCCS), Milan, Italy

Ilaria Offredi

Marisa giorgetti.

3 Scientific Institute for Rehabilitation Medicine, Eugenio Medea (IRCCS), Bosisio Parini, Italy

Mirta Vernice

4 Department of Psychology, University of Milano-Bicocca, Milan, Italy

Associated Data

The datasets generated for this study are available on request to the corresponding author.

The aim of this work is to investigate the factors promoting students’ engagement at school and supporting their well-being experience. According to the Positive Education there is a strong relationship between school environment and student’s well-being. Moreover, the quality of the school climate perceived by the students was found to influence engagement in school activities, as well. In this study, 153 students ( M = 67) attending 10th grade were presented with tests and questionnaires to assess individual assets (personality traits, literacy skills), emerging appraisals (school-climate, well-being experience) and emerging actions (school engagement), according to the Student Well-Being Model. Path analysis showed that the best model does include neither individual assets nor direct effect of school climate on engagement, as the effect of school climate on engagement is mediated by well-being experience. The main result is that school climate has been confirmed as an important factor to be considered to improve engagement in school activities, but it is effective only when its influence can modify the well-being experience of the students. Moreover, the lack of significant effects of individual assets in the model suggests that improving school climate means to support well-being experience and, indirectly school engagement, irrespective to learning abilities and personality traits. This work encourages working in/with schools to implement positive education programs that support and sustain a positive school climate and culture for school-community wellbeing.

Introduction

In recent years, there is a growing interest in educational policies and research promoting student engagement at school in order to contrast the students’ passivity and the dropout rate ( Archambault et al., 2009 ). As such, dropping out of high school has consequences for students’ well-being, including less lifetime earnings, more risky health behaviors, and poorer mental health ( Archambault et al., 2009 ). In 2017, the dropout rate in Italy (13.8%) was higher than the EU average rate (10.7%) (source: MIUR, Italian Ministery of Education, University and Research, 2017 ), with more impact in the regions of the South of Italy. Furthermore, the percentage of 18–24 years old people who can be defined NEETs (Neither in Employment Nor in Education or Training) in 2017 was around 25.7% in Italy, a percentage which is nearly double the EU average percentage (14.3%) (source: European Union Commissione Europea, 2018 ).

In this scenario, research is needed to identify and support all factors that can reduce boredom and passiveness among young people. School enjoyment is influenced by different factors involving values, reading, and writing skills, expectations of social context (i.e., peers, teachers, and families) and is affected by both school and out-of-school contexts ( Jennings, 2003 ; Ainley and Ainley, 2011 ). These aspects have been proved to affect learning outcomes and student’s engagement. The latter has been considered a key-factor to promote school completion and prevent dropout ( Christenson and Thurlow, 2004 ; Ainley and Ainley, 2011 ; Christenson et al., 2012 ). Longitudinal studies showed that engagement in high-school is associated with educational and occupational outcomes in adulthood, as it not only predicts academic attainment, but also influences learner’s self-concept, along with adult educational and occupational achievement, irrespective from socioeconomic factors and personality traits ( Abbott-Chapman et al., 2014 ). In this view, student’s engagement in school activities is a key protective factor against the risk of dropout ( Finn, 2006 ; Archambault et al., 2009 ). Leaving school before completing high school education is often the outcome of problems that can be related to little support in school context or to health, personal, or emotional difficulties young people face. It can be also associated with socio-economic phenomena (i.e., the economic crisis), which have strong impact on family background ( Berti et al., 2017 ). At the school level, a negative school climate (i.e., bullying or poor relationships between pupils and teachers) may trigger drop-out. Early school leaving, in addition, has significant societal and individual consequences, including the increased risk of unemployment, poverty, lower health, and social exclusion ( Psacharopoulos and Patrinos, 2018 ). Data from 2012 indicated that in Europe 5.5 million of youth and young adults (18–24 years old) have not earned a high school diploma and were not currently enrolled in education and training ( European Union Commissione Europea, 2013 ). In this scenario the study of the individual and contextual component affecting engagement in study activities can offer useful cues to face with huge social problems.

Engagement has been described in literature as a multidimensional construct, consisting mainly of three interrelated dimensions: emotion or affect, behavior, and cognition ( Fredericks et al., 2004 ; Lam et al., 2014 ). The affective or emotional dimension of engagement refers to the young people’s attraction to school with the absence of negative emotions and the presence of positive emotions (i.e., interest, joy) during task involvement ( Skinner et al., 2009 ). The behavioral aspect of engagement refers to factors (i.e., attention, effort, and persistence) that are in accordance with school expectations, learning-related tasks, and involvement in different school activities, even though not related to learning ( Skinner et al., 2009 ). The cognitive face of engagement refers to the strategies used by the student in learning activity, the execution of a particular work style, and self-regulated learning ( Fredericks et al., 2004 ; Wang et al., 2011 ). Very few studies have considered student engagement as a multi-dimensional construct. A recent large study by Fatou and Kubiszewski (2018) , with high school students (enrolled in grades 10, 11, and 12), was aimed at examining possible associations between student engagement and school climate perceived by students. The main result of that work was that student engagement was associated with perceived school climate; more specifically, the researchers presented a model that explained a large proportion of the variance in students’ engagement by incorporating the perceived school climate. Such model was useful, in particular, for predicting affective engagement.

These findings support the idea that the school climate might play an important role to favor a positive school experience in students. Numerous approaches contribute to a conceptualization of school climate and there is not a unique definition of it. School climate is generally viewed as a multidimensional construct that encompasses a school’s atmosphere, culture, values, resources, and social networks ( Wang and Degol, 2016 ). Furthermore, especially in the United States context, it is defined by the school norms, goals, values, interpersonal relationships, teaching and learning practices, organizational structures ( National School Climate Council, 2007 ) and is studied in terms of school safety (e.g., anti-bullying). The U.S. Department of Education (2014) , dispensed guidelines to promote and improve school climate and in 2018 the Office of Safe and Healthy Students proposed a compendium of school climate survey ( American Institutes for Research, 2018 ). Several programs aimed at improving school climate have been developed to promote the quality of scholastic life ( O’Brennan and Bradshaw, 2013 ). In fact, there is evidence that students are more engaged in school and attain higher academic achievement in schools with a positive school climate ( Wang and Degol, 2016 ; Konold et al., 2018 ). School climate can be studied at the group level, by aggregating the data collection of the different actors (students, teachers, managers, parents) involved in the school context ( Cornell et al., 2016 ). However, considering the perception of school climate also at an individual level can be very important, as several findings show that the feelings about school life have a great impact on student’s well-being ( Gage et al., 2016 ).

School has been recognized as one of the most important developmental context, where students can acquire skills and competencies supporting their successful adaptation ( Hamilton and Hamilton, 2009 ). However, there is still a limited perspective on factors that foster an optimal school environment ( Norrish et al., 2013 ). These limits come from the prevalence of problem-focused approaches, instead of studies aimed to promote a positive educational context ( Froh et al., 2011 ). In response to an excessive emphasis on research and practice related to weakness and disease, Positive Psychology movement redirected scientific inquiry toward the exploration of conditions promoting well-being in absence of pathology and illness ( Seligman and Csikszentmihalyi, 2000 ; Snyder and McCullough, 2000 ; Sheldon and King, 2001 ; Rusk and Waters, 2013 ). Understanding factors associated with positive psychological experiences could provide meaningful guidance to plan interventions that improve the optimal functioning of children and young people at multiple levels.

The application of Positive Psychology in educational context gave rise to a new paradigm, the Positive Education. Seligman (2011) defined this approach as “traditional education focused on academic skill development, complemented by approaches that nurture wellbeing and promote good mental health” (p. 127). This conceptualization has implications for research, stressing the importance of the relationship between school environment and student health and well-being. “The fundamental goal of Positive Education is to promote flourishing or positive mental health within school community” ( Norrish et al., 2013 , p. 148). Seligman’s (2011) PERMA (Positive emotion, Engagement, Relationships, Meaning, and Accomplishments) model of flourishing claims that positive emotions, engagement, relationship, meaning, and accomplishment are the keys to happiness and well-being.

In this vein, Soutter et al. (2014) proposed a conceptual framework to investigate student well-being (the Student Well-Being Model: SWBM), in which seven domains are considered, and organized in three overarching categories ( Figure 1 ): Having, Being, and Relating (Assets for well-being category); Feeling and Thinking (Appraisals category); Functioning and Striving (Actions category). The way these components interact is modeled according to the emergence mechanism: locally acting components give rise to higher-level entities ( Roeser and Galloway, 2002 ), that interact with the other levels through feedback loops. In addition, the evolution of student well-being throughout the lifetime is also considered. It is worth noting that this model draws from Bronfenbrenner’s (1979) model of human development, as its components are considered embedded in the intersecting spheres of students’ lives, i.e., the classroom, school, family, community and natural and built environments. The aim of Soutter et al.’s (2014) work is to offer a framework for developing qualitative and quantitative measures of students’ well-being and for promoting well-being in school programs.

Student Well-Being Model (source: Soutter et al., 2014 ).

Among locally interacting assets, personality traits and attitude toward learning are expected to play a main role in school experience. Personality traits, described according to the so-called Five-Factors Approach ( Block, 2010 ), are supposed to affect the way a student is used to face effort and duties (conscientiousness, neuroticism), to cope in front to new challenges (openness to experience), to interact with adults and peers (agreeableness, extraversion). Moreover, it is worth noting that students need to feel a close match between their current skills and abilities and instructional and curricular requirements, in order to have a positive school experience ( Traficante et al., 2017 ). Due to the high social value attributed to literacy, students who are struggling in reading and writing often develop deep distrust in their own abilities, low motivation, helpless behavior, low self-esteem, and anxiety in being involved in school activities, as they anticipate their own failure ( Morgan et al., 2008 ; Graham et al., 2012 ; Mason et al., 2012 ). Moreover, reading and writing abilities affect the social status of the child among the classmates ( Elbaum et al., 1999 ; Cornoldi and De Beni, 2001 ; Mugnaini et al., 2009 ; Andolfi et al., 2015 ), with effects on school well-being. However, during the adolescence, students with learning disabilities might be able to apply compensative strategies that allow them to adequately deal with the school requests. Thanks to the support of the school context, the consequences of school experience difficulties might be reduced, the students can reach functional levels of learning and the real difficulties might occur only when they are under pressure ( Fenzi and Cornoldi, 2015 ).

The aim of this work is to study, according to SWBM, how personal traits and literacy skills (locally interacting assets) influence students’ representations of school environment and of their experience of flourishing (emergent appraisals), and how these appraisals affect engagement in school activities (emergent actions). Fatou and Kubiszewski (2018) analyzed the effect of school-climate perception on engagement in high school, but in the present study other factors were considered as predictors of school engagement, and different models of relationships between different components were assessed. In particular, individual characteristics (personal traits and literacy skills) were expected to influence school-climate perception and well-being experience. Moreover, students’ well-being experience was supposed to influence engagement in school activities beyond the effect of school-climate perception, assessed by Fatou and Kubiszewski (2018) .

Materials and Methods

Participants.

One hundred fifty-nine (159, M = 15.6 years, SD = 6.2 months; Males = 70; 44%) high school students attending the 10th grade took part in the study. In this group there were 21 students with learning disabilities (13.2%), 2 students with sensory disabilities (1.2%), 4 students with other special needs (2.5%), and 28 students with Italian as their second language (17.6%). All students attended the 10th grade of three high-schools in the North of Italy during the 2018–2019 school year. Fifty-one participants were attending a technical institute (33.3%), 38 a vocational school (24.9%), and 61 a scientific high school (41.8%). Participants came from the middle class ( M = 6.86, SD = 1.60), according to the Family Affluence Scale (FAS; Currie et al., 2008 ).

Locally Interacting Assets

Literacy skills.

• 1. Decoding ability is evaluated considering speed and accuracy in reading a list of pseudo-words ( DDE-2 , Sartori et al., 2007 ). Reading speed was measured both as the overall reading time (in seconds) and as the number of syllables per second. Reading accuracy was measured as the number of errors in reading aloud.
• 2. Reading comprehension was assessed through a standardized text reading test ( Advanced MT 2 , Cornoldi et al., 2010 ). Students were presented with 10 multiple-choice questions (four alternatives), after reading the text silently. The score was the number of correct answers (range 0–10).
• 3. Accuracy in spelling was assessed through a text dictation test ( Advanced MT 3 , Cornoldi et al., 2017 ). The experimenter dictated at a constant rhythm of one word every 2 s. The score was the number of incorrectly written words.

Personality traits

Italian adaptation of Big Five Inventory (BFI – John et al., 2008 ; It. ad. Ubbiali et al., 2013 ) was used to evaluate the personality traits. The questionnaire consists of 44 utterances referring to five trait dimensions of personality: extraversion (8 items, e.g., “ I am a person who …generates a lot of enthusiasm”), agreeableness (9 items, e.g., “ I am a person who …likes to cooperate with others”), conscientiousness (9 items, e.g., “ I am a person who …makes plans and follows through with them”), neuroticism (8 items, e.g., “ I am a person who …is depressed, blue”), and openness to experience (10 items, e.g., “ I am a person who …is original, comes up with new ideas”). Answers were given on a 5-point Likert scale, from 1 = “strongly disagree” to 5 = “strongly agree.” Mean score for each dimension was carried out (range 1–5).

Emergent Appraisals

School climate.

The Georgia School Climate Survey (GSCS) is annually administered as an anonymous survey in the Georgia, United States. The survey was developed by the Georgia Department of Education (GADOE) Assessment and Accountability Division, the Georgia Department of Public Health, and Georgia State University. The 20 items downloaded from the official website https://www.gadoe.org/Curriculum-Instruction-and-Assessment/Curriculum-and-Instruction/GSHS-II/Pages/Georgia-Student-Health-Survey-II.aspx cover the following areas: school connectedness, peer social support, adult social support, cultural acceptance, social/civic learning, physical environment, school safety, peer victimization, order and discipline, and parents’ involvement (e.g., “I feel connected to others at school” ; “Teachers treat me with respect”; “My school building is well maintained”; “I feel safe in my school”). These items were administered after being translated in Italian and back-translated by an English native speaker. Answers were given on a 4-point Likert scale from 1 = “strongly disagree” to 4 = “strongly agree.” The overall school climate score ranged from 1 to 80.

Well-being experience

The Comprehensive Inventory of Thriving (CIT – Su et al., 2014 ) aims at assessing the general well-being through 54 items, pertaining to seven dimensions: (1) Relationships (6 scales, 18 items), composed by Support (e.g., “There are people who give me support and encouragement”), Community (e.g., I pitch in to help when my local community needs something done”), Respect (e.g., “People are polite to me”), Loneliness (e.g., “Often I feel left out”), Belonging (e.g., “I feel a sense of belonging in my Country”), and Trust (e.g., “Most people I meet are honest”); (2) Engagement (3 items: e.g., “I get fully absorbed in activities I do”); (3) Mastery (5 scales, 15 items), composed by Skills (e.g., “I use my skills a lot in my everyday life”), Learning (e.g., “I always learn something every day”), Accomplishment (e.g., “I am achieving most of my goals”), Self-Efficacy (e.g., “I believe that I am capable in most things”) and Self-worth (e.g., “The work I do is important for other people”); (4) Autonomy (3 items: e.g., “Other people decide most of my life decisions”); (5) Meaning (3 items: e.g., “I know what gives meaning to my life”); (6) Optimism (3 items: e.g., “I expect more good things in my life than bad”); (7) Subjective Well-being (3 scales, 9 items), composed by Life Satisfaction (e.g., “I am satisfied with my life”), Positive Feelings (e.g., “Most of the time, I feel happy”), and Negative Feelings (e.g., “Most of the time, I feel sad”). Items pertaining to the scales Loneliness, Autonomy, and Negative feelings were negatively phrased, so they were reversed. The rest of the items are phrased in a positive direction such that high scores mean that respondents view themselves positively in important areas of functioning. Participants were instructed to respond to each item on a scale from 1 = “strongly disagree” to 5 = “strongly agree.” Mean scores for each subscale were carried out (range: 1–5), and the CIT total score was the summed raw scores (range: 54–270).

Emergent Actions

Italian adaptation of the Student Engagement Scale ( Lam et al., 2014 ; It. ad. Mameli and Passini, 2017 ), is a questionnaire that assesses the three dimensions of student engagement by three scales. The Affective engagement scale estimates students’ interests and positive inclination for learning and school (9 items: e.g., “I think what we are learning in school is interesting”); the Behavioral engagement scale investigates students’ involvement in school and extra-school activities and the effort in learning (12 items: e.g., “In class I work as hard as I can”). The Cognitive engagement scale measures students’ investment in learning processes and strategies (12 items: e.g., “Make up my own examples to help me understand the important concepts I learn from school”). In the first two scales (Affective and Behavioral engagement), students were asked to indicate their level of agreement on a 7-point Likert scale from 1 = “strongly disagree” to 7 = “strongly agree.” In the Cognitive engagement scale, students were asked to answer a 7-point Likert scale of frequency from 1 = “never” to 7 = “always.” The mean score for each subscale was carried out (range: 1–7).

After receiving the school-manager’s approval to carry out the research, the caregivers and the students were informed on the aim and procedure of the study. Parents provided a written consent for their children’s participation in the study and students gave informed written consent to the study, according to the General Data Protection Regulation (GDPR 2016/79, 25/05/2018). Students completed the questionnaires and the tests in two group sessions and their decoding ability was assessed in one individual session. The present study was approved by the Scientific and Ethics Committee of the Department of Psychology of Catholic University of Milan, in accordance with the Helsinki Declaration.

Data Analysis

Normative scoring.

Standardized scores were computed from Italian normative data for literacy tests. Raw scores were recoded into z -scores, and the higher the value of z -scores is, the higher is the student’s ability.

Reliability Assessment

Reliability of each scale of the administered questionnaires was assessed through the Cronbach’s alpha, in order to include only reliable measures into the analyses.

Descriptive Statistics

Descriptive statistics were computed for each scale, in order to have a full description of the group of participants and verify the metric features of the variables included in the analyses.

Inferential Analyses

Canonical correlations (Pearson’s r ) within all the measures were analyzed, in order to identify the relationships within all the variables of interest. Moreover, three linear regression analyses were carried out with engagement scales (Affective, Behavioral, Cognitive) as dependent variables one at a time, and four different set of independent variables: (a) personality traits (extraversion, agreeableness, conscientiousness, neuroticism, openness to experience), (b) literacy skills (decoding, comprehension, spelling), (c) well-being (total score), and (d) school climate (total score). Finally, path-analysis (SEM) was implemented by mean of Mplus 7.11 software ( Muthén and Muthén, 1998-2015 ), to test the direct and indirect effects of individual assets, school climate and well-being on students’ engagement.

A score above 25th percentile rank at Standard Progressive Matrices test (SPM; Raven, 1954 , 2008 ) was used as inclusion criterion, in order to obtain a good adherence to the tasks. The participants showing a SPM score above the 25th percentile was 153 ( M = 15.6 years, SD = 6.5 months, Male = 67, 44%). In this group there were 18 students with learning disabilities (11.8%), 2 students with sensory disabilities (1.3%), and 3 students with other special needs (2%).

Descriptive Statistics and Reliability Indexes

Descriptive statistics on the scores from the assessment of reading, writing and comprehension skills ( Table 1 ) demonstrate the heterogeneity of the students considered in this study, as the minimum values show the presence of severe learning difficulties.

Literacy measures: descriptive statistics of raw- and z -scores.

As shown in Table 2 , all the factors of Big Five Inventory show a good internal consistency: Cronbach’s alpha coefficients were adequate, as they ranged from 0.67 to 0.82. Also the subscales of Comprehensive Inventory of Thriving show a good internal consistency (from 0.60 to 0.88), as well as the Georgia School Climate (α = 0.80). Descriptive statistics and reliability indexes of the engagement scales used to assess the different dimensions of the students’ engagement show that all the scales of the Student Engagement Scale have a good internal consistency. Cronbach’s alpha coefficients were good as they ranged from 0.86 to 0.93.

Personality traits, well-being, school-climate, and engagement: descriptive statistics and reliability indexes.

Correlation and Regression Analyses

Correlation analysis ( Supplementary Table S1 ) was carried out to assess the associations between individual assets (personality traits, literacy skills), emergent appraisals (school climate, well-being experience), and emergent actions (engagement). First of all, it is worth noting that both comprehension and spelling accuracy are correlated with decoding ability, but are not related to each other. In other words, a good ability in transcoding graphemes-to-phonemes is associated both to a good text comprehension and to accuracy in spelling, but the latter two skills are not associated to each other. Moreover, good text comprehension is associated with high scores in Consciousness ( r = 0.20) and Openness to experience ( r = 0.172), with the perception of a positive school climate ( r = 0.21) and with high level of engagement in learning activities (Affective: r = 0.179; Behavior: r = 0.169). Accuracy in spelling is associated with Neuroticism ( r = 0.21): students who feel anxious and need to have a high level of control in their lives seem to be more accurate in spelling.

Overall, Supplementary Table S1 shows strong correlations among personality traits, perception of school climate and engagement in learning activities, in the expected direction. In order to disentangle the specific effects exerted by personality traits, literacy, well-being experience, and perception of the school climate on engagement in school activities, three multiple linear regression analyses (with backward method) were carried out on each of engagement dimensions.

Affective Engagement Scale

Table 3 shows the variables that contribute to the explanation of about 50% of the variance of the Affective engagement score ( R = 0.71; R 2 = 0.51; F 8 , 134 = 17.44, p < 0.001). It is worth noting that only individual features and well-being experience seem to influence the affective engagement of students in school activities, whereas school climate has been excluded in previous steps. The personality profile of the student affectively involved in the learning process is characterized by conscientiousness, openness to experience and also by some degree of neuroticism. Students who are satisfied by their social relationships are usually engaged in their activities, and have an optimistic view of life and future. Their level of text comprehension is good. The negative coefficient of reading speed suggests that students who are attending 10th grade, in spite of their difficulties in reading, seem to be particularly engaged in learning activities.

Affective engagement scale: linear regression coefficients.

Behavioral Engagement Scale

Six variables contribute to the explanation of about 50% of the variance in Behavioral engagement score ( R = 0.72; R 2 = 0.52; F 6 , 136 = 24.85, p < 0.001). Table 4 shows that students’ involvement in school and extra-school activities and the effort in learning are affected not only by individual features but also by perception of school climate. In other words, school context seems to influence the actual level of students’ participation to the school and extra-school activities. Students who are prone to being involved in school activity are characterized by conscientiousness, agreeableness, and attitude to be engaged, but seem to have low satisfaction in relationship. Also in this regression model the coefficient corresponding to reading speed is negative oriented, suggesting the students with less reading skills are more involved in school activities.

Behavioral engagement scale: linear regression coefficients.

Cognitive Engagement Scale

Only three variables were selected by the backward method ( Table 5 ): openness to experience, reading speed, and sense of mastery. This model explained about 40% of the variance on Cognitive engagement score ( R = 0.62; R 2 = 0.39; F 3 , 139 = 29.53, p < 0.001) and suggests that the application of metacognitive and strategic approach to learning activity is an attitude developed by students who are prone to face new experiences and feel a sense of mastery when faced with new challenges. This attitude seems to be less developed in students with lower level of reading skills.

Cognitive engagement scale: linear regression coefficients.

Path Analysis

In order to draw a global representation of factors affecting engagement, structural equation a modeling technique was applied for the opportunity of testing and comparing different models of direct and indirect effects ( Table 6 ).

Path analysis: fit indexes of assessed models.

Fit indexes of Model 1, including individual assets (the latent variables “literacy skills” and “personality traits”) as independent variables, emergent appraisals (the latent variable “well-being” and the observed variable “school-climate”) as mediating variables, and emergent actions (the latent variable “school engagement”) as outcome were not satisfactory, due to the low impact of individual assets on school-climate appraisal and of literacy skills on well-being. For this reason, individual assets were excluded from the analyses and two different models were tested. In Model 2 ( Figure 2 ) the direct impact of school climate on student’s engagement was tested, according to Fatou and Kubiszewski (2018) work, but also the direct impact of school climate on well-being and of well-being on engagement were assessed, due to the stress of Positive Education on the effect of school community on well-being ( Seligman, 2011 ). Fit indexes improved a lot in comparison to the previous model, but the direct effect of school climate on engagement was far from significance level.

Path analysis: Model 2.

So, in Model 3 ( Figure 3 ), such a direct effect was deleted and the impact of school climate on engagement was modeled as fully mediated by the impact that school climate exert on well-being experience. All the effects in Model 3 are highly significant, so it has been considered the best model.

Path analysis: Model 3.

The aim of this work is to assess the relationships within the components proposed by the SWBM by Soutter et al. (2014) in the experience of students attending 10th grade, in order to identify the aspects which should become the targets of interventions, planned according to the Positive Education approach. Recently, Fatou and Kubiszewski (2018) found that the quality of the school climate perceived by the students explains a high proportion of variance in the level of engagement in school activities, showing a direct impact of school environment on the interest the students develop in learning and in participating to educational proposals. In the present work this model was extended through the inclusion of other variables suggested by SWB model. In particular, the impacts of personality traits and literacy skills (assets) on school climate and well-being experience (appraisals), and the effects of appraisal components on engagement in school activities (actions) were assessed. Our results support our hypotheses, showing an impact of assets and appraisals on the student actions and revealing that well-being experience influence engagement in school activities beyond the effect of school-climate perception.

Correlational analyses showed that higher ability in text comprehension is associated to consciousness, openness to experience, perception of positive school climate, and high level of affective and behavioral engagement. The association between ability in text comprehension and deep interest in knowledge and in cultural experience suggests that students with high level of openness to experience, and attitude to acquire new information are more likely to develop reading habit. Moreover, both text comprehension and social and emotional competence, which can contribute to a positive appraisal of school environment and activities, require inferential skills and an attitude to assume different points of view. According to research demonstrating the relationship between social competence, perceived social support and engagement (e.g., Estell and Perdue, 2013 ), these results show that the characteristics of personality that underline social functioning are associated with positive representation of school climate. Furthermore, the associations of personal traits with the affective and behavioral engagement are relevant because it suggests that consciousness and openness to experience are related with cognitive and emotional involvement in study activities.

It is worth noting also the lack of significant correlation between literacy and well-being. Previous work (see Traficante et al., 2017 ) suggested that well-being experience of primary-school children is mainly affected by literacy skills, as education, in low grades, is focused on learning to read and to write. Differently from what has been found in primary school children, in 10th grade literacy skills seem not to influence students’ well-being anymore. Furthermore, this work suggests that students who are attending 10th grade, in spite of their difficulties in reading, seem to be particularly engaged in learning activities. This is in line with the evidence that high-school students with specific learning disabilities (SLD) can develop adaptive strategies to deal with the school requests and focus on functional level of learning ( Fenzi and Cornoldi, 2015 ). Accordingly, a recent work on the students with SLD included in this sample, focused on the impact of low literacy skills on well-being experience ( Sarti et al., 2019 ) did not found any significant difference between clinical and control groups. On the contrary, students with SLD showed an increasing sense of thriving related to a growing trust and perceived support from others.

The complex pattern of relationships within all the variables of interest was further analyzed through linear regression models. These models showed that affective engagement is affected by personality traits (consciousness, openness, and neuroticism) and literacy skills, as the higher the ability in text comprehension is, the more interested the student is in learning activities. However, it is worth noting that, consistently with the previous remark on students with learning disabilities, the lower the decoding skills are, the higher the affective engagement is in school. This unexpected result can be explained by taking into account that attending high school, in Italy, is not mandatory. So, if a student with learning disabilities chooses to study after finishing middle school, he/she must be very interested in learning activity. Moreover, regression coefficients show that students with a higher level of affective engagement are people with a positive attitude toward social relationships and have an active and positive representation of his/her life. Attitude to be engaged in school projects and extra-school educational activities (behavioral engagement) is predicted by traits concerning sociality (agreeableness, relationships) and involvement (conscientiousness, engagement) and is affected by school climate, as the higher the sense of belonging to the institution is, the higher the behavioral engagement. Again, students with lower decoding skills seem to be more active in their school, and are more prone to apply cognitive strategies in school activities (cognitive engagement). Such metacognitive attitude is also predicted by the sense of mastery and by openness to experiences.

Finally, path analyses allowed to disentangle this complex pattern of reciprocal relationships, through the assessment of different models, in which, according to SWBM ( Soutter et al., 2014 ), individual assets (personality traits, literacy skills) were considered independent variables affecting appraisals (school-climate, well-being experience) and actions (school engagement). Results showed that the best model includes neither individual assets nor direct effect of school climate on engagement, which was suggested by Fatou and Kubiszewski (2018) . The effect of school climate on engagement is mediated by well-being experience. In other words, school climate has been confirmed as an important factor to be considered to improve engagement in school activities, but it is effective only when its influence can modify the well-being experience of the students.

These results support the perspective of Positive Education, as intervention on school environment is expected to exert positive effects not only on students’ well-being, but also on their engagement in school activities and learning, irrespective to students’ assets. This work encourages working in/with schools to implement positive education programs that support and sustain a positive school climate and culture for school-community wellbeing.

Data Availability Statement

Ethics statement.

The studies involving human participants were reviewed and approved by the Scientific and Ethics Committee of the Department of Psychology of Catholic University of Milan. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author Contributions

EL and DT carried out data analyses and wrote the manuscript. All the authors contributed to data collection, the discussion of the results, and the planning and discussion of the draft.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We are grateful to Chiara Bulgarelli for helping in data collection. A special thanks to students, parents, and schools for their collaboration.

Funding. This publication was supported by the Cariplo Foundation (Grant 2017-NAZ-0131 “New technologies for education and their impact on students’ well-being and inclusion”).

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2019.02482/full#supplementary-material

• Abbott-Chapman J., Martin K., Ollington N., Venn A., Dwyer T., Gall S. (2014). The longitudinal association of childhood school engagement with adult educational and occupational achievement: findings from an Australian national study. Br. Educ. Res. J. 40 102–120. 10.1002/berj.3031 [ CrossRef ] [ Google Scholar ]
• Ainley M., Ainley J. (2011). Student engagement with science in early adolescence: the contribution of enjoyment to students’ continuing interest in learning about science. Contemp. Educ. Psychol. 36 4–12. 10.1016/j.cedpsych.2010.08.001 [ CrossRef ] [ Google Scholar ]
• American Institutes for Research. (2018). School Climate Survey Compendia. Washington, D.C: American Institutes for Research. [ Google Scholar ]
• Andolfi V. R., Confalonieri E., Nardò F., Traficante D. (2015). Star bene a scuola: il ruolo dell’abilità di letto-scrittura. Psicologia dell’educazione 3 133–152. [ Google Scholar ]
• Archambault I., Janosz M., Morizot J., Pagani L. (2009). Adolescent behavioral, affective, and cognitive engagement in school: relationship to dropout. J. School Health 79 408–415. 10.1111/j.1746-1561.2009.00428.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Berti A. E., Ajello A. M., Aprea C., Castelli I., Lombardi E., Marchetti A., et al. (2017). Adolescents’ and young adults’ naïve understandings of the economic crisis. Eur. J. Psychol. 13 143–161. 10.5964/ejop.v13i1.1187 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Block J. (2010). The five-factor framing of personality and beyond: some ruminations. Psychol. Inq. 21 2–25. 10.1080/10478401003596626 [ CrossRef ] [ Google Scholar ]
• Bronfenbrenner U. (1979). The Ecology of Human Development: Experiments by Nature and Design. Cambridge, MA: Harvard University Press. [ Google Scholar ]
• Christenson S. L., Reschly A. L., Wylie C. (2012). Handbook of Research on Student Engagement. New York, NY: Springer Verlag. [ Google Scholar ]
• Christenson S. L., Thurlow M. L. (2004). School dropouts: prevention considerations, interventions, and challenges. Curr. Dir. Psychol. Sci. 13 36–39. 10.1111/j.0963-7214.2004.01301010.x [ CrossRef ] [ Google Scholar ]
• Cornell D., Shukla K., Konold T. (2016). Authoritative school climate and student academic engagement, grades, and aspirations in middle and high schools. AERA Open 2 1–18. 10.1177/2332858416633184 [ CrossRef ] [ Google Scholar ]
• Cornoldi C., De Beni R. (2001). “Imparare a Studiare 2”: Strategie, Stili Cognitivi, Metacognizione e Atteggiamenti Nello Studio. Trento: Erickson. [ Google Scholar ]
• Cornoldi C., Pra Baldi A., Friso G. (2010). Prove MT Avanzate di Lettura e Matematica 2 per il Biennio Della Scuola Secondaria di Secondo Grado. Firenze: Giunti O.S. [ Google Scholar ]
• Cornoldi C., Pra Baldi A., Giofrè D. (2017). Prove MT Avanzate-3-Clinica. Firenze: Giunti Edu. [ Google Scholar ]
• Currie C., Molcho M., Boyce W., Holstein B., Torsheim T., Richter M. (2008). Researching health inequalities in adolescents: the development of the health behavior in school-aged children (HBSC) family affluence scale. Soc. Sci Med. 66 1429–1436. 10.1016/j.socscimed.2007.11.024 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Elbaum B., Vaughn S., Hughes M., Moody S. W. (1999). Grouping practices and reading outcomes for students with disabilities. Except. Child. 65 399–415. 10.1177/001440299906500309 [ CrossRef ] [ Google Scholar ]
• Estell D. B., Perdue N. H. (2013). Social support and behavioral and affective school engagement: the effects of peers, parents, and teachers. Psychol. Schools 50 325–339. 10.1002/pits.21681 [ CrossRef ] [ Google Scholar ]
• European Union Commissione Europea, (2013). Labour Force Survey 2012 ∗ . Available at: https://ec.europa.eu/eurostat/statistics-explained/index.php/Archive:Labour_force_survey_overview_2012 (accessed September 3, 2019). [ Google Scholar ]
• European Union Commissione Europea, (2018). Relazione di monitoraggio del settore dell’istruzione e della formazione - Italia ∗ . Available at: https://ec.europa.eu/education/sites/education/files/document-library-docs/et-monitor-report-2018-italy_it.pdf (accessed June 13, 2019). [ Google Scholar ]
• Fatou N., Kubiszewski V. (2018). Are perceived school climate dimensions predictive of students’ engagement? Soc. Psychol. Educ. 21 427–446. 10.1007/s11218-017-9422-x [ CrossRef ] [ Google Scholar ]
• Fenzi V., Cornoldi C. (2015). Le difficoltà ortografiche di adolescenti con dislessia. Dislessia 12 75–86. [ Google Scholar ]
• Finn J. D. (2006). The Adult Lives of at-Risk Students: The Roles of Attainment and Engagement in High School (NCES 2006-328). Washington, DC: U.S. Department of Education, National Center for Education Statistics. [ Google Scholar ]
• Fredericks J. A., Blumenfeld P. C., Paris A. H. (2004). School engagement: potential of the concept, state of the evidence. Rev. Educ. Res. 74 59–109. 10.3102/00346543074001059 [ CrossRef ] [ Google Scholar ]
• Froh J. J., Huebner E. S., Youssef A. J., Conte V. (2011). Acknowledging and appreciating the full spectrum of the human condition: school Psychology’s (limited) focus on positive psychological functioning. Psychol. Schools 48 110–123. 10.1002/pits.20530 [ CrossRef ] [ Google Scholar ]
• Gage N. A., Larson A., Sugai G., Chafouleas S. M. (2016). Student perceptions of school climate as predictors of office discipline referrals. Am. Educ. Res. J. 53 492–515. 10.3102/0002831216637349 [ CrossRef ] [ Google Scholar ]
• Graham S., Berninger V., Abbott R. (2012). Are attitudes toward writing and reading separable constructs? A study with primary grade children. Read. Writ. Q. 28 51–69. 10.1080/10573569.2012.632732 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hamilton S., Hamilton M. (2009). “ The transition to adulthood: challenges of poverty and structural lag ,” in Handbook of Adolescent Psychology , 3rd Edn, eds Lerner R., Steinberg L. (Hoboken, NJ: John Wiley & Sons; ), 492–526. [ Google Scholar ]
• Italian Ministery of Education, University and Research, (2017). La dispersione scolastica nell’a.s. 2015/2016 e nel passaggio all’a.s. 2016/2017 ∗ . Available at: https://www.miur.gov.it/documents/20182/0/Focus+La+dispersione+scolastica/9bc1c11b-1c40-4029-87ba-40b8ba2bc2bf?version=1.1 (accessed June 13, 2019). [ Google Scholar ]
• Jennings G. (2003). An exploration of meaningful participation and caring relationships as contexts for school engagement. Calif. School Psychol. 8 43–52. 10.1007/BF03340895 [ CrossRef ] [ Google Scholar ]
• John O. P., Naumann L. P., Soto C. J. (2008). “ Paradigm shift to the integrative Big Five trait taxonomy: history, measurement, and conceptual issues ,” in Handbook of Personality: Theory and Research , eds John O. P., Robins R. W., Pervin L. A. (New York, NY: The Guilford Press; ), 114–158. [ Google Scholar ]
• Konold T., Cornell D., Jia Y., Malone M. (2018). School climate, student engagement, and academic achievement: a latent variable, multilevel multi-informant examination. AERA Open 4 1–17. 10.1177/2332858418815661 [ CrossRef ] [ Google Scholar ]
• Lam S., Jimerson S., Wong B. P. H., Kikas E., Shin H., Veiga F. H., et al. (2014). Understanding and measuring student engagement in school: the results of an international study from 12 countries. School Psychol. Q. 29 213–232. 10.1037/spq0000057 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mameli C., Passini S. (2017). Measuring four-dimensional engagement in school: a validation of the student engagement scale and of the agentic engagement scale. TPM Test. Psychom. Methodol. Appl. Psychol. 24 527–541. 10.1037/t27797-000 [ CrossRef ] [ Google Scholar ]
• Mason L. H., Meadan H., Hedin L. R., Cramer A. M. (2012). Avoiding the struggle: instruction that supports students’ motivation in reading and writing about content material. Read. Writ. Q. 28 70–96. 10.1080/10573569.2012.632734 [ CrossRef ] [ Google Scholar ]
• Morgan P. L., Farkas G., Tufis P. A., Sperling R. A. (2008). Are reading and behavior problems risk factors for each other? J. Learn. Disabil. 41 417–436. 10.1177/0022219408321123 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mugnaini D., Lassi S., La Malfa G., Albertini G. (2009). Internalizing correlates of dyslexia. World J. Pediatr. 5 255–264. 10.1007/s12519-009-0049-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Muthén L. K., Muthén B. O. (1998-2015). Mplus User’s Guide (Version 7). Los Angeles, CA: Muthén & Muthén. 10.1007/s12519-009-0049-7 [ CrossRef ] [ Google Scholar ]
• National School Climate Council, (2007). The School Climate Challenge: Narrowing the Gap Between School Climate Research and School Climate Policy, Practice Guide- Lines and Teacher Education Policy ∗ . Available at: http://www.schoolclimate.org/climate/advocacy.php (accessed June 13, 2019). [ Google Scholar ]
• Norrish J. M., Williams P., O’Connor M., Robinson J. (2013). An applied framework for positive education. Int. J. Wellbeing 3 147–161. 10.5502/ijw.v3i2.2 [ CrossRef ] [ Google Scholar ]
• O’Brennan L., Bradshaw C. (2013). Importance of School Climate (National Education Association Research Brief) ∗ . Available at: https://www.cta.org/~/media/Documents/Issues%20%20Action/Safety/NEA%20School%20Climate%20Research_Brief-4pg%20(1).pdf?dmc=1&ts=20191027T0555257668 (accessed June 1, 2019). [ Google Scholar ]
• Psacharopoulos G., Patrinos H. A. (2018). Returns to investment in education: a decennial review of the global literature. Educ. Econ. 26 445–458. 10.1080/09645292.2018.1484426 [ CrossRef ] [ Google Scholar ]
• Raven J. C. (1954). SPM Standard Progressive Matrices (It. tr. Matrici Progressive di Raven, Serie A, B, C, D, E). Firenze: Giunti OS. [ Google Scholar ]
• Raven J. C. (2008). SPM Standard Progressive Matrice. Firenze: Giunti OS. [ Google Scholar ]
• Roeser R., Galloway M. G. (2002). “ Studying motivation to learn in early adolescence: a holistic perspective ,” in Academic Motivation of Adolescents: Adolescence and Education , Vol. II , eds Urdan T., Pajares F. (Greenwich, CT: Information Age Publishing; ), 331–372. [ Google Scholar ]
• Rusk R. D., Waters L. E. (2013). Tracing the size, reach, impact, and breadth of positive psychology. J. Posit. Psychol. 8 207–221. 10.1080/17439760.2013.777766 [ CrossRef ] [ Google Scholar ]
• Sarti D., Bettoni R., Offredi I., Tironi M., Lombardi E., Traficante D., et al. (2019). Tell me a story: an insight on socio-emotional functioning in adolescents with specific learning disabilities, and its relations with well-being and smartphone use. Front. Psychol. 10.3389/fpsyg.2019.02369 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sartori G., Job R., Tressoldi P. (2007). DDE-2: Batteria per la Valutazione della Dislessia e della Disortografia Evolutiva–2, Seconda Edizione. Firenze: Giunti O.S. [ Google Scholar ]
• Seligman M. (2011). Flourish. New York, NY: Free Press. [ Google Scholar ]
• Seligman M. E., Csikszentmihalyi M. (2000). Positive Psychology: An Introduction. Washington, DC: American Psychological Association. [ PubMed ] [ Google Scholar ]
• Sheldon K. M., King L. (2001). Why positive psychology is necessary. Am. Psychol. 56 216–217. 10.1037/0003-066X.56.3.216 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Skinner E. A., Kindermann T. A., Furrer C. J. (2009). A motivational perspective on engagement and disaffection. Educ. Psychol. Meas. 69 493–525. 10.1177/0013164408323233 [ CrossRef ] [ Google Scholar ]
• Snyder C. R., McCullough M. E. (2000). A Positive Psychology field of dreams: “If you build it, they will come…”. J. Soc. Clin. Psychol. 19 151–160. 10.1521/jscp.2000.19.1.151 [ CrossRef ] [ Google Scholar ]
• Soutter A. K., O’Steen B., Gilmore A. (2014). The student well-being model: a conceptual framework for the development of student wellbeing indicators. Int. J. Adolesc. Youth 19 496–520. 10.1080/02673843.2012.754362 [ CrossRef ] [ Google Scholar ]
• Su R., Tay L., Diener E. (2014). The development and validation of the Comprehensive Inventory of Thriving (CIT) and the Brief Inventory of Thriving (BIT). Appl. Psychol. Health Wellbeing 6 251–279. 10.1111/aphw.12027 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Traficante D., Andolfi V. R., Wolf M. (2017). Literacy abilities and well-being in children: findings from the application of EUREKA, the Italian adaptation of the RAVE-O Program. Form@ re 17 12–38. [ Google Scholar ]
• U.S. Department of Education, (2014). School Climate and Discipline ∗ . Available at: https://www.ed.gov/news/press-releases/us-departments-education-and-justice-release-school-discipline-guidance-package- (accessed June 13, 2019). [ Google Scholar ]
• Ubbiali A., Chiorri C., Hampton P., Donati D. (2013). Italian big five inventory. Psychometric properties of the Italian adaptation of the big five inventory (BFI). Boll. Psicol. Applicata 266 37–48. [ Google Scholar ]
• Wang M. T., Degol J. L. (2016). School climate: a review of the construct, measurement, and impact on student outcomes. Educ. Psychol. Rev. 28 315–352. 10.1007/s10648-015-9319-1 [ CrossRef ] [ Google Scholar ]
• Wang M. T., Willett J. B., Eccles J. S. (2011). The assessment of school engagement: examining dimensionality and measurement invariance across gender and race/ethnicity. J. School Psychol. 49 465–480. 10.1016/j.jsp.2011.04.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]

ORIGINAL RESEARCH article

The impact of school climate and school identification on academic achievement: multilevel modeling with student and teacher data.

• 1 School of Education, RMIT University, Brunswick, VIC, Australia
• 2 Research School of Psychology, Australian National University, Canberra, ACT, Australia
• 3 Psychology, School of Psychological and Clinical Sciences, Charles Darwin University, Darwin, NT, Australia
• 4 School of Psychology, University of Newcastle, University Drive, Callaghan, NSW, Australia
• 5 Student Engagement and Wellbeing, Australian Capital Territory Education Directorate, Braddon, ACT, Australia

School climate is a leading factor in explaining student learning and achievement. Less work has explored the impact of both staff and student perceptions of school climate raising interesting questions about whether staff school climate experiences can add “value” to students' achievement. In the current research, multiple sources were integrated into a multilevel model, including staff self-reports, student self-reports, objective school records of academic achievement, and socio-economic demographics. Achievement was assessed using a national literacy and numeracy tests ( N = 760 staff and 2,257 students from 17 secondary schools). In addition, guided by the “social identity approach,” school identification is investigated as a possible psychological mechanism to explain the relationship between school climate and achievement. In line with predictions, results show that students' perceptions of school climate significantly explain writing and numeracy achievement and this effect is mediated by students' psychological identification with the school. Furthermore, staff perceptions of school climate explain students' achievement on numeracy, writing and reading tests (while accounting for students' responses). However, staff's school identification did not play a significant role. Implications of these findings for organizational, social, and educational research are discussed.

Introduction

Effective teaching and learning is the result of complex group and psychological processes. However, the precise organizational factors and psychological mechanisms behind these processes are still under investigation. Identifying the means to improve students' learning outcomes remains the subject of continuous academic inquiry and a key objective of government and international bodies. As a result of this interest, an immense body of work centerd on the construct of “school climate” has emerged. School climate refers to social characteristics of a school in terms of relationships among students and staff/teachers, learning and teaching emphasis, values and norms, and shared approaches and practices ( Anderson, 1982 ; Moos, 1987 ; Thapa et al., 2013 ). Among other factors, empirical evidence has confirmed that school climate is powerful in affecting students' academic achievement ( Brand et al., 2008 ; Chen and Weikart, 2008 ; Collins and Parson, 2010 ). However, the extent to which both of student and staff perceptions of school climate influence student achievement is less clear. Furthermore, the precise psychological processes underpinning the climate-achievement link requires further investigation.

Seeking to fill these gaps, the current research examines the impact of student and staff perceptions of school climate on students' achievement. Very few studies have investigated both groups' perceptions of school climate in relation to academic achievement and even fewer using a robust, national, standardized measure to assess achievement. The present research also offers a theoretical analysis of the psychological processes underlying this relationship, using the social identity approach ( Tajfel and Turner, 1979 ; Turner et al., 1987 ). This analysis builds on work that has applied the social identity approach to various staff and student outcomes ( Bizumic et al., 2009 ; Turner et al., 2014 ; Reynolds et al., 2017 ) and has relevance for school-based interventions directed at improving school outcomes.

In the following sections, the construct of school climate is described, along with the links between (a) student perceptions of school climate and students' academic achievement and (b) staff perceptions of school climate and students' academic achievement. Next the theoretical framework, the social identity approach is introduced. Finally, some methodological challenges confronting researchers in this field are described.

What is School Climate?

The school climate construct is complex and multi-dimensional. It has been described as the unwritten personality and atmosphere of a school, including its norms, values, and expectations ( Brookover et al., 1978 ; Haynes et al., 1997 ; Petrie, 2014 ). Further, it has been described as the “quality and character of school life” ( Cohen et al., 2009 , p. 182). Importantly, rather than concerning administrative or physical attributes of the school (e.g., teachers' salary or schools' physical resources), school climate research hones in on the psychosocial school atmosphere, and the inter-group interactions that affect student learning and school functioning ( Johnson and Stevens, 2006 ; Lubienski et al., 2008 ; Reyes et al., 2012 ).

School climate is a leading predictor of students' emotional and behavioral outcomes. It affects students' adaptive psychosocial adjustment ( Brand et al., 2008 ), mental health outcomes ( Roeser et al., 2000 ; Brand et al., 2003 ) and self-esteem ( Way et al., 2007 ). School climate also influences students' behavior, such as rates of bullying and aggression ( Espelage et al., 2014 ; Turner et al., 2014 ), student delinquency ( Gottfredson et al., 2005 ), and alcohol and drug use ( Brand et al., 2003 ). Finally, and of particular relevance to this research, school climate perception has also been found to affect students' academic achievement ( Brookover et al., 1978 ; Brand et al., 2008 ).

The Challenge of Defining and Measuring School Climate

The multiplicity of definitions for school climate has led to confusion and hindered research progress ( Hoy and Hannum, 1997 ; Thapa et al., 2013 ; Ramelow et al., 2015 ; Wang and Degol, 2015 ; Lee et al., 2017 ). This lack of definitional consensus has meant that school climate is measured inconsistently ( Thapa et al., 2013 ). Various scales have been used, with their different sub-scales flowing from different articulations of the construct. Despite this limitation, three sub-factors of the construct ( Moos and Moos, 1978 ) are clearly represented in the literature and school climate scales. (1) School's academic emphasis as personal growth or goal orientation; “the extent to which a school is driven by a quest for academic excellence” ( Hoy et al., 1991 , p. 71); (2) interpersonal relationships within a school, which are judged by their quality and consistency ( Haynes et al., 1997 ); and (3) shared norms, goals, and values; the common understanding of accepted and endorsed behavior ( Frederickson, 1968 ). These defining sub-factors have brought some conceptual clarity to the construct.

The assessment of school climate involves asking particular groups of interest to report their perceptions. These groups' perceptions include parents' ( Esposito, 1999 ), students' ( Fan et al., 2011 ), principals' ( Brookover et al., 1978 ), and teachers' ( Johnson and Stevens, 2006 ; Brand et al., 2008 ; Bear et al., 2014 ). Perspective matters because each group may perceive school climate differently. Often, though, only one group's perceptions have been assessed, usually students in most studies.

Students' Perceptions of Climate and Academic Achievement

Variance in achievement beyond individual factors and socio-economic status has consistently been explained by students' school climate ratings ( Hoy and Hannum, 1997 ; Brand et al., 2008 ; Collins and Parson, 2010 ). Brookover et al. (1978) conducted a seminal study establishing this student-climate-achievement link. The authors tested the effect of students' perceptions of school climate on mean school achievement in three samples of racially diverse elementary schools. They found that school climate explained a significant amount of the between-school variance in mean school achievement and that the strength of the relationship was similar to that explained by economic status (SES) and ethnicity.

Subsequent research supports these findings ( Goddard et al., 2000b ; Heck, 2000 ; Thapa et al., 2013 ). For example, Hoy and Hannum (1997) and Tschannen-Moran et al. (2006) found that positive school climate was associated with students' academic achievement, after controlling for SES. Contrastingly, a negative school climate has been found to reduce student participation in school activities and student learning ( Chen and Weikart, 2008 ). This climate-achievement relationship appears to be robust for students across different grades, backgrounds, and cultures ( Gregory et al., 2007 ; Jia et al., 2009 ). It also appears to endure for years ( Hoy et al., 1998 ), which has been further supported by longitudinal studies (e.g., Brand et al., 2008 ).

Various sub-factors of school climate have been found to exert a powerful impact on academic achievement. For example, academic emphasis ( Hoy and Sabo, 1998 ; Goddard et al., 2000b ), academic optimism ( Smith and Hoy, 2007 ), and strong teacher-student relationships ( Crosnoe et al., 2004 ; Tschannen-Moran et al., 2006 ) have been found to be particularly influential. In particular, student-teacher relationships effectively work as a protective factor for school adjustment including academic achievement as well as conduct and behavioral problems, especially for adolescents transiting from middle school to high school (e.g., Longobardi et al., 2016 ). However, many of the reviewed studies are limited because of how they measured academic achievement. Many have relied on regional or state-wide tests and unstandardized measures (e.g., self-reported performance or grade point average, [GPA]). Although various studies have used standardized literacy and numeracy assessment data (e.g., Goddard et al., 2000b ; Sweetland and Hoy, 2000 ; Tschannen-Moran et al., 2006 ; Brand et al., 2008 ), studies using standardized nation -wide tests are limited.

Staff Perceptions of School Climate and Academic Achievement

While studying the climate-achievement link from the student perspective is illustrative, the staff perspective is also relevant ( Fisher and Fraser, 1983 ; Johnson et al., 2007 ; Liu et al., 2014 ). Measuring staff perspectives of school climate is important for several reasons. First, discrepancies have been found between students' and teachers' perceptions. Teachers' ratings are more sensitive to classroom level factors and students are more sensitive to school-level factors ( Mitchell et al., 2010 ; Wang and Eccles, 2014 ). Teachers also rate teacher-student relations more positively than students do ( Raviv et al., 1990 ). Second, and importantly, teachers have the largest impact on student learning out of all school reform initiatives ( Heck, 2000 ; Lindjord, 2003 ; Schacter and Thum, 2004 ). Therefore, measuring staff perceptions might expose areas for reform and intervention.

A relatively small pool of literature measures the effect of staff's perceptions of school climate on student outcomes. These links have often been vague, with methodological challenges undermining the research to date. The dominant focus has been how staff perceptions of school climate affect staff's functioning ( Heck, 2000 ). For example, staff perceptions have been measured against staff well-being ( Boyd et al., 2005 ; Grayson and Alvarez, 2008 ), staff morale and job satisfaction ( Ma and MacMillan, 1999 ; Collie et al., 2012 ). The impact of staff perceptions on student outcomes, such as student achievement has been explored to a much lesser extent. Nevertheless, there is a general trend observed in the relationship between staff climate perception and student achievement.

Early studies highlight that staff perceptions of the schools as a work environment and expectations of students affect student outcomes ( Moos, 1987 ; Esposito, 1999 ). More recent studies support these findings. For example, Johnson and Stevens (2006) found teachers' perceptions of school climate had a positive relationship with fourth graders' scores on standardized tests using structural equation modeling. However, a drawback of their design was their use of aggregated mean scores for staff perceptions and student academic performance by school. This design assumes there is no difference within schools. By ignoring and compressing individual variation, important statistical information is also lost ( Hox, 2010 ) and standard error estimates may be incorrect ( Garson, 2013 ). This methodological approach is a common limitation in educational research, and will be further described later in this introduction.

A more comprehensive study exploring the impact of staff climate perceptions on student achievement was carried out by Brand et al. (2008) . There were three particularly relevant findings. First, teachers' school climate perceptions were significantly associated with eighth graders' reading and mathematics scores. Second, teachers' reports of students' achievement orientation were significantly correlated with students' mathematics achievement and reading performance. Third, teachers' climate perceptions were significant predictors of less robust measures of achievement, such as GPA and students' academic efficacy. Their statistical design was strong, as they used hierarchical linear modeling to control for the nested structure of the data. The authors also controlled for student's SES, used a longitudinal design (3-year period) and large samples with up to 114, 240 students from 243 schools.

Additionally, the authors used a paired school climate scale to measure student and teacher perceptions. They then compared the effect of teacher perceptions to the effect of student perceptions on the same variables. Out of all aspects of teachers' and students' perspectives of school climate, achievement orientation emerged as the strongest predictor of student achievement. Furthermore, schools had higher achievement levels when teachers perceived positive student-student relationships (“peer sensitivity”) and lower levels of disruption, which was not the case with student perspectives.

Although there is less literature exploring the relationship between staff perceptions of school climate and achievement (compared with literature from the student perspective), there is general support showing that staff perceptions of school climate predict student achievement. However, the way in which school climate perception comes to affect student achievement is still to be explored.

How Does School Climate Perception Affect Student Achievement?

Explaining precisely how school climate perception comes to affect student outcomes has been a challenge for researchers. In any case, various theories have been put forward (see Wang and Degol, 2015 for a comprehensive review), including social cognitive theory, self-determination theory, and bio-ecological theory. However, the social identity approach offers an alternative and integrative analysis, which will be adopted in the current research.

Social cognitive theory has been a particularly popular theoretical explanation for the climate-achievement link as it relates to students and staff ( Bandura, 1993 , 1997 ). Authors have suggested that students need collective efficacy to activate the influence of the school climate, in particular for the aspect of academic press, on their achievement ( Hoy et al., 2002 ). This approach has also been applied in explaining the impact of staff perspectives on student achievement ( Hoy and Woolfolk, 1993 ; Goddard et al., 2000a ). For example, Caprara et al. (2006) found that teachers' self-efficacy beliefs were significantly related to students' academic achievement. Goddard et al. (2000a) additionally found that collective teacher efficacy significantly predicted students' reading and mathematics performance. Specifically, the authors found that a “one unit increase in a school's collective teacher efficacy score” was related to increase of “more than 40% of a standard deviation in student achievement” (p. 501).

Self-determination theory has also been widely applied ( Deci and Ryan, 1985 ). Authors have proposed that students and staff need to meet the psychological basic needs of relatedness, competence, and autonomy in order for students to achieve ( Connell and Wellborn, 1991 ; Roeser et al., 1998 ; Reeve, 2012 ; Taylor et al., 2014 ). Bronfenbrenner's bio-ecological theory has also been investigated through analyzing how the layers of the environment (e.g., individual, family, and school) affect student learning ( Bronfenbrenner, 1979 , 1986 ; Rosenfeld et al., 2000 ; Stewart, 2007 ; Hampden-Thompson and Galindo, 2017 ).

The theories have much to offer in understanding the climate-achievement link, in terms of intrapsychic individual psychology. Yet, exploring a whole school approach and group dynamics in a school may offer further theoretical and practical implications. Indeed, specific theories within social psychology that focus on group-level processes provide a novel perspective to explain the effect of school climate on achievement. Hence, the social identity approach is put forward as an integrative theoretical explanation for this school climate-achievement link.

Background to the Social Identity Approach

The “social identity approach” consists of social identity theory ( Tajfel and Turner, 1979 ) and self-categorization theory ( Turner et al., 1987 ). The key point of the social identity approach is that a group, system or organization (e.g., school) influences individual behavior (e.g., student or staff member) when an individual feels psychologically part of that group, system, or organization ( Tajfel and Turner, 1979 ). Membership to these higher-level systems is not defined by external criteria (e.g., the category of student, label of staff member, or any other demographic characteristic). Rather, it is defined by a feeling of psychological membership, identification, and connectedness.

The social identity approach makes an important distinction between a personal identity and a social identity (“I” or “me” vs. “we” or “us”; Turner et al., 1987 ). When an individual finds a group psychologically meaningful (becoming “we” or “us”), the group's values and needs become normative and are integrated into personal ones ( Turner et al., 1994 ). The process of social identification entails members becoming motivated to achieve the group's goals and putting more effort into ensuring these goals are realized ( Haslam et al., 2000 ). In other words, the individual's psychological connection with the group triggers the influence of organizational factors on their behavior and makes them more likely to act in alignment with the group's norms and values ( Turner, 1985 ; Turner and Reynolds, 2011 ).

In the school context, norms, values, and beliefs of the “school” group are embodied in the school climate construct. A central goal of the school as a group is often to have a strong academic emphasis, supportive staff-student relations, and shared values and approach (factors which are conducive to successful student learning) ( Bizumic et al., 2009 ; Reynolds et al., 2017 ). It is possible to conceptualize school climate as the facilitator of students' and staff's identification and school identification as the psychological process through which school climate comes to affect their behavior.

Students' school identification might affect their academic performance in the following way. If the school climate is positive and supportive, and this, in turn, facilitates the student to identify with the school as a salient group, then the student is more likely to reflect and embed the school values and norms, focusing on learning and achievement, with their behavior ( Reynolds et al., 2017 ).

Along these lines, Reynolds et al. (2017) found that the relationship between students' school climate perceptions and students' numeracy and writing scores was fully mediated by students' school identification. However, the measure of school climate was limited in their study and featured only one general dimension of school climate, which was shared values and approach.

More broadly, related concepts to social identification have also been captured by the educational literature, and studied in relation to student outcomes. For example, connectedness, student-school bonding, attachment, and sense of belonging to school have been studied ( Osterman, 2000 ; Libbey, 2004 ; Blum, 2005 ; Vieno et al., 2005 ; Waters et al., 2009 ). One particularly relevant study for students found the relationship between school climate and students' conduct problems was mediated by students' school connectedness ( Loukas et al., 2006 ). School belonging also was a critical variable, especially for multiracial modeling of student achievement ( Burke and Kao, 2013 ; Hernández et al., 2014 ; Gummadam et al., 2016 ).

The social identity approach can also be applied to explain the link between staff school climate perception and student achievement ( Reynolds et al., 2017 ). The outcome of interest in the present research is student achievement. Thus, the relevant outcome is the behavior of the students . Therefore, it seems illogical to also propose staff school identification as a psychological mediator of the students. However, there remains different reasons to assume that staff school identification could play an important role. Rather than mediate, staff school identification might moderate the influence of their climate perception on student achievement. That is, the level of staff's psychological membership to the school might adjust the impact of school climate on students' achievement. For example, when staff strongly identify themselves with the school, staff might be more motivated to strive for better academic results from their students in the classroom and dedicate more effort to fostering supportive relations with students. These behaviors are conducive to students' academic engagement, which may translate to students' improved student achievement, only when staff social identity as a school member is high. That is, the strength of the path from staff school climate perception to student achievement would be dependent on the level of staff school identification, as a regulator. If staff social identification is weak, then the impact of their school climate perception on student achievement may be far weaker posing different impact strength from for the case with higher staff school identification.

Unlike the application of the social identity approach to students, this specific theoretical proposition with respect to staff school identification has not been directly investigated. However, a link between staff behavior (more broadly) and student performance has been well-established. For example, Mohammadpour (2012) found after controlling for some student and school factors, teacher emphasis on homework had a significant association with student achievement. MacNeil et al. (2009 , p. 155) also emphasized the importance of teacher morale and motivation for student outcomes, finding that “highly motivated teachers have greater success in terms of student performance.” Teachers and administrators' feeling of a sense of school cohesion influenced students' academic achievement ( Stewart, 2007) . Additionally, teacher empowerment was found to be a significant predictor of students' results on standardized tests ( Sweetland and Hoy, 2000 ). The important point to distill from these studies is that psychological phenomena applying to staff have been found to affect the behavioral outcomes (specifically, achievement levels) of students.

Importantly, most of these studies have only looked at certain variables as predictors of students' academic achievement, and not as psychological mechanisms or moderators. This study takes a novel approach by proposing that students' school identification is a mediator and staff's school identification is a moderator of the relationship between their perceptions of school climate and student achievement. This approach is important because “social identity processes not only help explain student behavior at school but point to pathways that can be used to shape it” ( Reynolds and Branscombe, 2015 , p. 171). A better understanding of the underlying processes may be especially informative in designing effective and efficient interventions to improve achievement outcomes.

The Current Study

The extant literature has demonstrated that students' and members of staff's ratings of school climate have a significant impact on students' academic outcomes. Nevertheless, there a number of gaps and issues in this body of work to be addressed. First, although some parallel measures have assessed both students' and staff's school climate perception (e.g., Brand et al., 2008 ), little is known about whether staff school climate perception plays a significant role when student perceptions and other covariates are taken into account in a single statistical model. Second, many studies of academic achievement have used unstandardized tests and single-informant school climate perspectives. Third, the nested hierarchical inter-correlations of student and staff data within schools has often been ignored, which can be addressed through the use of multilevel modeling ( D'haenens et al., 2010 ; Wang and Degol, 2015 ). Finally, there is room for theoretical and empirical exploration of the psychological processes accounting for the climate-achievement relationship.

In aiming to address these gaps, the present study proposes MLM procedures, standardized achievement data and multi-informant data (student and staff perceptions and educational records) to examine both the impact of student and staff perceptions of school climate on students' standardized literacy and numeracy tests. The models should also control for demographic variables including gender, parental education, school size, and SES. Further, it will expand our knowledge and inform school reformers to investigate whether those relationships operate as a function of students' and staff's psychological identification with the school climate, i.e., “school identification.”

In the present study there are three informant sources are integrated in a single study design; survey responses both from staff and students, as well as NAPLAN data and demographic information from education records. The study employs MLM methods to address some of the problems suffered by past studies of aggregation bias, heterogeneity of regression, and increased errors in parameter estimation ( Bryk and Raudenbush, 1988 ; Tabachnick and Fidell, 2014 ).

Core covariates are also included in the analysis. Students' gender is included due to its known effect on academic achievement ( Marsh et al., 2005 ; Hinnant et al., 2009 ). Male students have an advantage on numeracy tasks whereas females may have an advantage in verbal information tasks ( Halpern and LaMay, 2000 ; Ma and Klinger, 2000 ). This gender difference has been reflected in NAPLAN data for 2008–2013, where males have performed consistently better in numeracy tests ( Australian Curriculum Assessment Reporting Authority, 2008-2015 ). The level of education of students' parents is also included, as it is also known to affect student achievement ( Davis-Kean, 2005 ; Senler and Sungur, 2009 ).

School covariates are included, namely, the SES of the whole school ( Caldas and Bankston, 1997 , 1999 ; Johnson et al., 2001 ; Perry and McConney, 2010 ) and school size ( Lee and Loeb, 2000 ; Ma and Klinger, 2000 ). These individual factors (students' gender and the educational level of their parents) and school factors (SES of the school and school size) are controlled in order to measure the impacts of school climate perception and identification on NAPLAN results more clearly.

While a similar study measured the impact of students' school climate perception and school identification on NAPLAN results ( Reynolds et al., 2017 ), the present study uses a significantly larger sample size (2,257 students in 17 secondary schools) compared to their study (340 students in 2 schools). Compared with their study, a more fully developed version of School Climate and School Identification Measurement Scale ( Lee et al., 2017 ) is used. Furthermore, multilevel modeling is employed and staff perceptions are additionally investigated.

The current study also explores the role of school identification in the climate-achievement relationship. Students' school identification is modeled as a mediator of the link between students' perceptions and their achievement. Mediation models ( MacKinnon, 2008 ; Hayes, 2009 ) were tested using the following paths; (1) from school climate to school identification, (2) from school identification to achievement scores, and (3) the indirect path from school climate to achievement scores via school identification. In contrast, staff's school identification is modeled as a moderator. It is hypothesized to interact with the relationship between staff school climate perception and student achievement, such that the level of staff school identification changes the strength and nature of the potential relationship between staff perceptions and student achievement.

Accordingly, the current research proposes that after controlling for demographic factors, students' school identification will mediate the impact of student's school climate perception on academic achievement. More specifically, corresponding to Figure 1 , positive school climate perception will predict stronger school identification among students ( a ) that in turn predict higher achievement scores ( b ). The indirect path from school climate to student achievement scores via school identification will be positive and significant ( d ).

Figure 1 . Conceptual model for Hypothesis 1: c = the direct path from students' perceptions of school climate to students' NAPLAN results will be positive and significant. Conceptual model for Hypothesis 2: a = students' positive school climate will predict stronger school identification among students, b = the path from students' school identification to students' NAPLAN results will be significant and d = the indirect path from school climate to student achievement scores via school identification will be positive and significant.

We also hypothesize that Staff perceptions of school climate will predict students' higher levels of academic achievement. Furthermore, staff's school identification will moderate the impact of their school climate perception on students' academic achievement. A high level of school identification amongst staff will explain a stronger impact of staff perception of school climate on students' academic achievement, whereas a low level of school identification will explain a weaker impact of staff school climate perception on students' academic achievement (Refer to Figure 2 ).

Figure 2 . Model 5: Multilevel SEM of numeracy scores with student and staff school climate perception predictors, a mediator of student school identification, and demographic covariates at the student and school level. Error terms, correlations, and related coefficients are ommitted for simplicity. Gender: Male = 0, Female = 1; Parental education: below university degree = 0, university degree or higher = 1. Coefficients are unstandardized. * p < 0.05; ** p < 0.01.

Multi-informant Data Sets and Procedure

This research uses data collected as part of an ongoing longitudinal project between the Australian National University (ANU) and the Australian Capital Territory (ACT) Education and Training Directorate (ETD) ( Reynolds et al., 2012 ). 1 The project aimed to measure and enrich the health of school climates in the district in order to improve student and staff outcomes. The project involved all 86 public schools in the district, a city region with a population of ~367,752 ( Australian Bureau of Statistics, 2014 ).

The present study also uses educational register data from 2,257 students' achievement scores on a robust, standardized, and nation-wide test, the Australian National Assessment Program—Literacy and Numeracy (NAPLAN). Every 2 years, all Australian students from Grade 3 up to Grade 9 sit NAPLAN tests. The current study sampled Grades 7–10 (high school attendees in the district) students' scores, which were provided by the education department.

Specifically, the following three data sets were merged to a single main data set.

(1) Data from education district records. This included demographic information, such as levels of parental education, school level SES, and student achievement scores. Student achievement scores are a sample of students' results on 2014 NAPLAN tests.

(2) Student survey responses. An online survey was administered to all Grade 7 and 9 students at all schools in the ACT during a 2-week period (during June 2014). Students provided their consent if they chose to participate. Then they completed the online survey (through Qualtrics software) in their classrooms with teachers' assistance. Parents' consent was waved by the relevant authority due to the low risk nature of the survey and students being able to provide own consent. Survey responses were on a Likert scale from 1 (“disagree strongly”) to 7 (“agree strongly”). Data sets 1 and 2 were merged to include students who both participated in the SCASIM-St survey, and completed NAPLAN tests. Accordingly, each student's survey response was matched with their NAPLAN scores and demographic information.

(3) Staff responses from the SCASIM-Sf survey. Staff provided consent and completed an online survey during the survey period at a time convenient to them.

Participants

The sample included 2,257 Grade 7 and 9 students and 760 staff from 17 public schools, 89% of all the 19 public high schools in the district.

One thousand and one hundred fifteen male students (49.4%) and one thousand and one hundred forty-two female students took part in the survey ( M = 13.3 years old, SD = 1.2). 51.5% were in Grade 7 and 48.5% were in Grade 9. 80.3% of the sample spoke English at home, compared to the overall Australian average of 82% ( Australian Bureau of Statistics, 2014 ). 0.7–1% participants who did not indicate their their age, spoken language at home, or gender, were excluded from the main analysis. 65.3% of the students' parents had education levels below a university level. The survey response rate was between 23.7 and 79% ( M = 61.47%). This percentage can be attributed to some students being absent, some deciding not to participate, and there being some technological issues with online participation. The response rate was included as a covariate in the statistical models to control for possible sampling issues, and was placed at the school level in the MLM.

The staff sample consisted of 497 females (68.6%) and 228 males (31.4%), which is representative of the female majority of educators in Australia ( Australian Bureau of Statistics, 2014 ). The average age was 41.03 years old ( SD = 11.5, range = 18–70). 15.2% were administrative staff and 84.3% were teaching staff. Administrative staff members were included because they play a role in setting and reflecting the climate of the schools. 4.6% participants did not report their gender and 7.5% did not report their age, so they were excluded from the main analysis.

Among the 17 schools, the average school size was 676.54 students ( SD = 274.12, range = 205–1154). 58.82% were Kindergarten to Grade 10 schools and 41.18% were high schools containing Grades 7–10. An average of 25.42% of students in each school had a language background other than English (range = 13–65%, one school was a bilingual school, with 65% of students with a language background other than English). The SES of the schools was measured by the Index of Community Socio-Educational Advantage (ICSEA, described later in detail). On a possible scale from 500 to 1,300, ICSEA values for the schools ranged from 971.68 to 1177.91 ( M = 1075.21, SD = 57.24).

Student Measures

Students' perceptions of school climate and level of school identification.

School Climate and School Identification Measurement Scales-Student (SCASIM-St, Lee et al., 2017 ) with 38 items was used to measure school climate and school identification. The four subscales for school climate are academic emphasis (8 items, α = 0.929), staff-student relations (9 items, α = 0.964), student-student relations (7 items, α = 0.959), and shared values and approach (8 items, α = 0.927). The school identification factor consists of 6 items (α = 0.944). The subscales were highly reliable with the current data (αs > 0.7). The SCASIM-St has also shown criterion validity associated with academic achievement, attendance, aggressive behavior at school, and a well-being factor of depression ( Lee et al., 2017 ).

Demographic variables

Students' age, gender, spoken language at home, and parents' level of education was collected by the survey or matched from education records.

Students' academic achievement

Grade 7 and 9 students' performance on NAPLAN tests was used to measure academic achievement in numeracy, reading and writing ability. Students' scores are standardized and range from 0 to 1,000 ( Australian Curriculum Assessment Reporting Authority, 2014 ).

Staff Measures

Staff perceptions of school climate and school identification.

These were measured by a staff measure, the School Climate and School Identification Measurement Scales-staff (SCASIM-Sf, the scale's factor structure was validated in a supplementary analysis, and available as supplementary Material). It was used as a paired and mirrored scale of the student version, The confirmatory factor analysis (CFA) on the SCASIM-Sf 2 revealed that the 36 items represent four sub-factors of school climate and school identification, as parallel with the student survey. The factors were reliable with the data: academic emphasis (8 items, α = 0.94), staff-student relations (9 items, α = 0.95), staff-staff relations (5 items, α = 0.94), shared values and approach (8 items, α = 0.94), and a correlated school identification factor (6 items, α = 0.95).

School Level Measures

School-wide ses schools'.

SES levels were measured by the Index of Community Socio-Educational Advantage (ICSEA) and included as a covariate. ICSEA values are nationally standardized to reflect educational advantages and disadvantages at the school level, based on student family and school background variables such as parents' occupation and school location. Higher values indicate more advantages for the school students and the values are on a scale from 500 to 1,300 (median = 1,000, The Australian Curriculum, Assessment and Reporting Authority, 2014).

School size

School size was measured by the number of students enrolled in the school and was included as a covariate. This information was from the 2014 district school census.

Analytical Plan

The variance in students' achievement scores was analyzed at both the within (individual) level and between (school) level, due to substantial intra-class correlations (ICCs) and subsequent design effects above two 3 . As detailed in the following section, the results suggested that responses within schools were not independent ( Hox, 2010 ). To handle this dependency, two-level multilevel Structural Equation Modeling (SEM) procedures were employed using MPlus version 7 ( Muthén and Muthén, 1998–2015 ). Hierarchical models were tested to assess the impacts of student and staff perceptions of school climate and school identification on students' NAPLAN results. The impact of covariates on NAPLAN results was also examined in all models from the base model.

Variables on the first-level of the model (“within-level” or “individual level”) were students' grade, gender, parents' educational level, and students' perceptions of school climate and school identification. Staff members' school climate perception and school identification were also placed on the within-level. Staff ratings may well-serve as school-level variables, however the current data exhausted all the school-level variance once the school-level demographics were controlled for. Therefore, staff variables were modeled to further explain individual student-level variance. Specifically staff perceptions of school climate were averaged as school means and disaggregated on to individual student data. Thus, students from the same school had the same staff school climate mean scores in their data. This practice (disaggregating the school means to the individual level to explain the variance in individual students' scores) has been applied before in educational research to explain achievement (e.g., Thomas and Collier, 2002 ).

Second-level (“between-level” or “school level”) variables were covariates, including schools' ICSEA value and size, as well as the student response rate. School-level variables were entered as random effects (as they were expected to differ between schools) and individual-level variables were declared fixed (it was presumed that there would be no random differences in the relationships between the variables and NAPLAN results).

First, the “null models” (“Model 0”) were run to get the ICCs, which determined the proportion of variance accounted for by the clustering ( Goldstein et al., 2002 ), and confirmed whether MLM procedures were required. Models were then built hierarchically, increasing in complexity and explanatory potential as more predictor variables were added.

Model 1 was then tested, adding covariate demographic variables at the student and school levels. These models operated as a baseline for models 2–7, to compute the increased proportion of explained variance (Δ R 2 ) as other variables were added to the models. Student perceptions were then added in Model 2 to test the impact on academic achievement. Social identity mediation was then tested with two subsequent models, first by adding student school identification to Model 3 and then by modeling school identification as a mediator (Model 4).

As the next step, staff perceptions of school climate were added to test the impact) in Model 5 with all other variables controlled. Staff's school identification was added to Model 6, and then an interaction term was added to test if staff social identity significantly moderated the impact of staff school climate perception on student achievement (Model 7). Model 7 was the most complex model run, and is visually depicted in Figure 2 .

Domain specificity was anticipated to occur ( Marsh et al., 2005 ; Hinnant et al., 2009 ), wherein the nature and extent of the impact of the variables on NAPLAN results may have varied according to subject domain. Correspondingly, Models 0–7 were run for each of the three different dependent variables (numeracy, reading, and writing results) 4 .

Descriptive Statistics

Data screening showed that both the staff and student data sets were not normally distributed. The means, standard deviations, skew, kurtosis, and reliability statistics for the staff and student school climate sub-scales are reported in Tables 1 , 2 , respectively. For staff responses, all means were reasonably high on the 7-point Likert scale, with a small range (5.22–5.98), as were the student responses (4.01–5.02). The dependent variables (students' scores on numeracy, reading, and writing NAPLAN tests) were also not normally distributed. Out of a possible score of 1,000, students' overall total means were 565.43 for numeracy ( SD = 83.38), 572.70 for reading ( SD = 87.72), and 525.09 for writing ( SD = 106.43). Therefore, non-normality was dealt with the MPlus MLR estimator (maximum likelihood estimation with robust standard errors) that are robust to non-normality ( Muthén and Muthén, 1998–2015 ). The data missing rate was trivial with a maximum of 2.8–0.7% at average. Accordingly a multiple imputations method was employed using Mplus.

Table 1 . Means, standard deviations, skewness and kurtosis scores, and cronbach's alpha for the scale scores in the student and staff samples.

Table 2 . Correlations among study variables, means, and standard deviations.

After screening and cleaning the data, the staff data was merged with the student data set by disaggregating staff responses as school means. The final data set included students' demographic variables, students' ratings of school climate and school identification, staff ratings of school climate and school identification, school-level demographic variables and NAPLAN scores. This merged data set was then used for analyzing correlations (Table 2 ) 5 and for the main multilevel SEM analysis.

Multilevel SEM Analysis

It was expected that students' perceptions of school climate would be positively related to students' NAPLAN results (H1) and that this relationship would occur through students' school identification (H2). It was also expected that staff perceptions of school climate would be positively related to student achievement (H3) and that staff's school identification would moderate this relationship (H4).

Multilevel Modeling

The ICCs and design effects for the numeracy, reading, and writing models were high enough to require multilevel modeling (ICCs: numeracy: 0.08, reading: 0.05, writing: 0.04, all design effects >2, (Satorra and Muthen, 1995; Muthén and Muthén, 2009; Hox, 2010 ; Muthen and Muthen, 2007). The maximum likelihood parameter estimation with standard errors (MLR) was used because it is robust to non-normality, enabling the analysis of the substantially skewed and kurtosed data ( Muthén and Muthén, 1998–2015 ). Tables 3 – 8 summarize the results of the hierarchical stepwise multilevel SEMs. Models 0–6 were run separately, with writing, reading, and numeracy scores as dependent variables.

Table 3 . Multilevel SEM results for models 0–3 explaining NAPLAN numeracy.

Table 4 . Multilevel SEM results for models 4–6 explaining NAPLAN numeracy.

Table 5 . Multilevel SEM results for models 0–3 explaining NAPLAN reading.

Table 6 . Multilevel SEM results for models 4–6 explaining NAPLAN reading.

Table 7 . Multilevel SEM results for models 0–3 explaining NAPLAN writing.

Table 8 . Multilevel SEM results for models 4–6 explaining NAPLAN writing.

Demographic Covariates

The demographic covariate-only model (Model 1) showed that 8.4, 5.2, and 6.7% of variance in numeracy, writing, and reading performance, respectively, was explained by the three covariates of grade, gender, and parental education (Tables 3 , 5 , 7 ). However, the school-level variances (20.18~165.99, p = 0.19~0.76) were completely explained by the three school-level covariates of SES, school size, and response rate, before including staff perception variables on school climate or identification. These results forced analyzing the staff variables at the student level, as the exhausted variance at the school-level meant no additional explanatory variable could be added at the school level of the model.

When all other variables were included (Model 6), the impact of most covariates on achievement persisted. For example, NAPLAN scores were significantly higher for students in higher grades (writing b = 17.48, p < 0.01; reading b = 13.89; numeracy b = 20.88) and students who had parents with higher educational levels (writing b = 30.13, p < 0.01; reading b = 33.02; numeracy b = 28.26). Boys performed better than girls on numeracy tests ( b = −9.30, p < 0.05) and girls performed better on literacy tests, particularly writing tests (reading b = 9.59, p < 0.10; writing b = 42.69, p < 0.01).

School-level variables showed mixed effects. Response rate did not significantly predict student achievement at the school level in any domain. However, larger schools (reading: b = 0.02, p < 0.05; numeracy: b = 0.03, p < 0.05) and schools with higher SES had significantly higher achievement at the school level in numeracy (b = 0.22, p < 0.01) and reading (b = 0.20, p < 0.05). There was no effects of school covariates on writing achievement at the school level, when all the variables including school climate perception of both student and staff groups and student school identification.

Social Identity Mediation: Students' School Climate Perception Impacted on Numeracy, Writing, and Reading through Their School Identification

As shown in the Model 4 results in Table 2 , students' school identification ( b = 10.03, p < 0.05) completely mediated the impact of their school climate perception (indirect effect, b = 10.90, p < 0.05) upon Numeracy. For Writing (Model 4 in Table 3 ), partial mediation was observed with the significant impact of students' school identification ( b = 10.89, p < 0.05) as well as their school climate perception ( b = 1.09, p < 0.05; indirect effect, b = 11.83, p < 0.05). Yet, only marginally significant mediation effects were examined for Reading (Model 4 in Table 4 ) with students' school identification ( b = 7.15, p < 0.10) and their perceptions of school climate (indirect effect, b = 7.87, p < 0.10). In all models, students' perception of school climate impacted their school identification ( b = 1.08–1.1, p < 0.01). All these results were so when all the individual and school level covariates were taken into account.

Staff School Climate Perception Impacted on Student's Numeracy, Writing, and Reading Achievement

As presented in Model 5 in Tables 3 – 5 , staff perceptions of school climate were significant predictors of students' academic achievement (writing b = 21.21; reading b = 16.80; numeracy b = 7.57, all p < 0.05). However, staff's school identification was not a significant predictor of students' academic achievement (Model 6, Tables 3 – 5 ). Because staff school identification was not found to be a significant predictor of students' numeracy, reading, or writing results, the seventh proposed model (suggesting staff's school identification as a moderator) could not be investigated.

Overall, Model 5 was the most complicated model run, as it had better model fit than Model 6 and others. Model 5 included demographic covariates, students' perceptions of school climate (with students' school identification modeled as a mediator) and staff perceptions of school climate to explain NAPLAN results. This model is visually depicted in Figure 2 for numeracy achievement scores. The fifth model uniquely explained 8, 6, and 9% of variance at the student level in students' writing, reading, and numeracy scores, respectively. In total, 39% for both variances in writing and reading, and 42% of variance in numeracy scores were explained by Model 5 with the student and school level variables.

This study used a multilevel framework to examine the influence of individual (student and staff) factors and school level factors on students' academic achievement. Three out of the four hypotheses were supported. Positive student and staff perceptions of school climate positively and significantly impacted students' NAPLAN results, as expected. Students' school identification mediated the impact of their perception of school climate on their performance in two learning domains. However, staff's school identification did not moderate the impact of staff's perceptions on student achievement.

Academic Achievement Explained by Student- and School-Level Variables

Students' individual factors (gender, grade, and education level of their parents) and school factors (school size and school SES) significantly impacted students' academic achievement. Collectively, these factors accounted for 8.4, 5.2, and 6.7% of within-school variance in students' numeracy, writing, and reading performance respectively and ~40% of the whole variance in the achievement scores. As expected, consistent with the literature, boys tended to score better on numeracy and girls tended to score better on literacy ( Halpern and LaMay, 2000 ; Marsh et al., 2005 ; Hinnant et al., 2009 ). The results also showed that the three most significant demographic predictors of student achievement were school SES, parental education and grade, replicating well-confirmed findings ( Davis-Kean, 2005 ; Perry and McConney, 2010 ; Reynolds et al., 2017 ). However, student and staff perceptions of school climate also emerged as significant predictors in all three learning domains.

In line with the first hypothesis, the more positively students perceived school climate, the better their achievement scores were in the numeracy and writing domains. These results were evident even after known covariates of student achievement (gender, SES, and parental education) were controlled. Using a more complex model and a national standardized measure of achievement, this relationship between student school climate perception and achievement is largely consistent with the literature ( Brookover et al., 1978 ; Sweetland and Hoy, 2000 ; Tschannen-Moran et al., 2006 ; Brand et al., 2008 ). School climate perception did not significantly impact reading performance, which reflects previous research demonstrating that the reading domain is less affected by school climate ( Ma and Klinger, 2000 ; Reynolds et al., 2017 ).

The results showed substantial support for the second hypothesis, such that students' positive school climate perception predicted stronger school identification among students in all the three models of reading, writing, and numeracy, which in turn, predicted higher achievement scores in numeracy and writing. In other words, students' perceptions of school climate psychologically flowed through school identification to influence students' numeracy and writing scores (the indirect mediation effect was only marginally significant for reading performance). The current results demonstrate the impact of school climate may only operate indirectly, as a function of students' identification with the school. Students merely perceiving the school climate as positive might not be sufficient to trigger the influence of school climate on their achievement. Rather, school identification is a vital psychological mechanism to activate the influence of school climate on students' numeracy and writing performance.

The only other study to have directly tested the viability of school identification as a mechanism underpinning the climate-achievement link was conducted by Reynolds et al. (2017) , using a much smaller sample (340 students in 2 schools). The present study replicated their findings that school identification mediated the impact of school climate on achievement in numeracy and writing, yet, with a much larger sample and MLM procedures. The findings are also consistent with Bizumic et al. (2009) and Turner et al. (2014) , who provide evidence that school identification mediates the impact of school climate on non-academic outcomes, such as well-being and bullying behavior.

There are some important caveats on this interpretation. First, most students in this sample identified relatively strongly with their school ( M = 4.71 on a 7-point Likert scale) so this mediation relationship may be generalizable to school populations in which students moderately to highly identify with the school. As noted, an effect of domain specificity was also apparent, so the mediation results should only be interpreted as applying to specific domains of students' numeracy and writing achievement.

Academic Achievement Explained by Staff Variables

There was mixed support for the hypotheses for staff. Specifically, the results showed staff's perceptions of school climate significantly predicted students' academic achievement, confirming the third hypothesis. This finding is consistent with the literature (e.g., Johnson and Stevens, 2006 ; Brand et al., 2008 ; MacNeil et al., 2009 ; Yang, 2014 ). Given that student perceptions of school climate were controlled, the current research also gives more confidence in this school climate-achievement relationship from the staff perspective.

The fourth hypothesis was not supported. Staff's social identification did not significantly relate to, or moderate, the relationship between staff perceptions of school climate and students' academic achievement. It was expected that stronger identification would be associated with staff spending more time and effort on achieving the school's vision and norms, leading to better academic outcomes for students. Methodological limitations may have contributed to these non-significant findings. There may not have been enough statistical power for the multilevel model to detect a real effect. Tabachnick and Fidell (2014) advise that adequate power is generated when “sample sizes at the first level are not too small and the number of groups is 20 or larger” (p. 793). In this case, there were less than 20 groups (17 schools). The nature of the sample and variables may have also precluded a significant finding. The sample was quite homogenous and there was little variability in staff responses to school identification ( M = 5.88 on a 1–7 Likert scale, SD = 1.12). Future research could overcome these limitations by including more schools and more diversity in respondents' levels of school identification.

In light of these methodological shortcomings, it is premature to extract theoretical meaning from the non-significant result for H4. However, it is plausible that there were other factors affecting staff members' job performance (measured by students' NAPLAN results) that were not included in the model, such as salary, leadership, training, and administrative support. Moreover, the model may not have captured the right type of identification. Staff could identify with other levels of identity that may affect their job performance (and hence, students' academic achievement). For example, staff's identification with the profession or teaching discipline (more broadly) or the classroom unit (more narrowly), may have impacted students' academic achievement. Clearly, this is fertile ground for further research.

Implications for Theory and Research

This research has contributed to social-psychological and educational research concerning school climate and highlighted the importance of psychological factors for students' academic success. It replicated established findings that student and staff perceptions of school climate impact student achievement, and extended the research further by proposing school identification as an explanatory psychological mechanism for students. Various studies have explored the link between staff perceptions of school climate and student achievement, but none have controlled for student perceptions. Hence, for the first time in a single statistical model, the present study revealed the unique contribution of staff perceptions in explaining in student achievement. The study also contributed to the social identity body of work. The finding that school identification mediated the student-climate-achievement link is a marked contribution since schools are a relatively novel context to apply the theory ( Reynolds and Branscombe, 2015 ).

The use of MLM procedures, national standardized academic achievement tests, a large sample size and the inclusion of covariates increased the reliability and validity of these findings. Importantly, no other study has used MLM procedures and national standardized academic achievement tests to explore the climate-achievement link. These findings are also strengthened by the multi-informant design of the study. As the introduction revealed, studies integrating multiple school climate perspectives are relatively rare in the school climate field ( Thapa et al., 2013 ; Wang and Degol, 2015 ). Using multiple informants is considered “best practice” when measuring educational and psychological constructs ( Konold and Cornell, 2015 ). Hence, this study has enriched the school climate field by including student and staff perspectives, answering research calls for measuring school climate from different perspectives ( Thapa et al., 2013 ; Liu et al., 2014 ).

Implications for Designing School Initiatives

By disentangling school-level factors from the student level factors affecting student achievement and illuminating core psychological processes within schools, the present study has also uncovered potential targets for intervention. Rather than standardized reforms that are insensitive to psychological elements of school functioning (e.g., economic incentives for teachers, increasing school resources), initiatives informed by this analysis could be more innovative by engaging with the psychological intricacies of school processes.

The following example initiatives are proposed as efficient strategies to affect change, since top-down change to the system level can capture more members than if every individual group member were to receive an individual intervention. It is presumably easier to change the health of the school climate and school members' school identification than to influence other factors, such as the SES of a school and other non-school factors that are beyond schools' control ( Heck, 2000 ; Hoy et al., 2002 ). Two areas for targeted intervention are proposed; school climate perception and school identification.

Initiatives Facilitating School Climate and the Perceptions by Staff and Students

Since school climate is malleable ( Wang and Degol, 2015 ), interventions could modify and improve school members' perceptions of school climate in order to impact student achievement. For example, the Comer School Development Program ( Cook et al., 2000 ) is an initiative that seeks to improve interpersonal relations and build shared academic and social goals among school members. After 2 years of the program's implementation, teachers' and students' ratings of schools' academic climate improved, as did students' results on mathematics and reading tests compared to controls. Another example is the Child Development Project, a school-wide intervention that seeks to foster healthy interpersonal relations (collaboration among and between staff, students, and parents) and a sense of common purpose (two sub-factors incidentally measured by the SCASIM). Results have shown that students who received this intervention felt more connected to the school and had significantly higher levels of academic achievement (measured by GPA and achievement test scores). The outcomes of the Child Development Project take on new importance when considering the mediation effect found in the present study. Hence, by strengthening school connectedness (identification) and increasing positive perceptions of school climate, the Child Development Project achieved two outcomes that this study has found to be critically related to student achievement.

Fostering School Identification

Because students' psychological identification with a positive school climate emerged as a powerful variable influencing students' academic performance, interventions could foster and support students' feeling of closeness to the school. Turner et al. (2014) provide some guidance to this end, as the authors advocated for the implementation of the ASPIRe model 6 ( Haslam et al., 2003 ). The ASPIRe model operationalizes the core aspects of the social identity approach into a four-phase sequence of group tasks, which seek to foster increased organizational identification (school identification). In light of current results, activities which emphasize a shared mission of the school and remove barriers to psychological school membership might have positive implications for students' academic achievement.

Limitations and Future Directions

First and foremost, this study would have benefitted from the inclusion of data from additional schools. Moreover, analyzing staff perceptions for the school level achievement would have made more statistical and theoretical sense, if staff perception ratings had not to be aggregated as means by school in the current study. However, this design was not possible as there was not enough variance left to explain at the school level, after school level variables such as school SES (ICSEA) and school size were accounted for. This situation may be explained by the fact that schools in the participating district are fairly homogenous in terms of student achievement due to the regional SES characteristics. This is in contrast to American schools (where much of the research has taken place), which are more diverse and for which school-level analysis was available (e.g., Brand et al., 2008 ). The inclusion of additional schools would have increased the power of the statistical model and may have enabled the analysis of staff perceptions on the school level. Hence, future studies should employ data from a larger number of schools to cross-validate the current findings.

Another statistical issue with the present study concerns high correlations between staff variables. The supplementary CFA analysis revealed staff school identification and the latent school climate factor were highly correlated (Online material A: r = 0.76). This already high correlation may have been further inflated when the staff data were disaggregated to the student data, as the correlation between shared values and approach and school identification increased from r = 0.71 in the CFA to r = 0.91 in the present study. Multicollinearity is a problem because multicollinear variables inflate error terms and weaken the analysis ( Tabachnick and Fidell, 2014 ).

Similar to most of the school climate research, this study was neither longitudinal nor experimental. This is a problem for the research because causal inferences are not possible ( Wang and Holcombe, 2010 ). Future studies examining causal relationships with interventions or a longitudinal design are clearly warranted ( Brand et al., 2003 ). For example, differences in academic achievement could be measured after students receive an intervention that increases their school identification. An idea for investigating the climate-achievement link with a longitudinal design was put forward by Johnson and Stevens (2006, p. 119); “rather than relying on student achievement at one point in time, growth in student achievement could be used as an outcome construct.” This would be possible under the larger longitudinal project from which the current data set originated. For example, differences in the same cohort's level of academic achievement could be analyzed (the difference between NAPLAN data at time 1 [Grade 7] and time 2 [Grade 9]). A longitudinal design would also account for the fact that school climate perception is not static ( Wang and Degol, 2015 ). It potentially changes and evolves during different points in the school year (for example, proximity to holiday periods or exam periods) and corresponding with different events at the school (for example, changing administration or exposure to a new initiative, Johnson and Stevens, 2006 ). Hence, longitudinal designs should be adopted in future research, as they would account for the impermanency of school climate perception ( Wang et al., 2010 ).

Future studies should control for other known critical predictors of achievement. Even though many critical variables were included in this analysis, the most complicated models explained ~40% of the whole variance and only 7.6, 5.6, and 9.7% of variance at the student level in writing, reading, and numeracy scores, respectively. This reflects that teaching and learning is a complex process and numerous factors affect students' academic achievement. Future studies could include more covariates that have been known to influence academic achievement, such as students' individual SES, parental involvement, leadership, teacher credentials, students' IQ, students' motivation and attendance ( Keith and Cool, 1992 ; Ma and Klinger, 2000 ; Perry and McConney, 2010 ). Students learning disabilities and attribution styles may also be important considerations as they can affect the student-teacher relationship ( Pasta et al., 2013 ). In a comprehensive meta-analysis as a synthesis of more than 800 studies (over 50,000 studies) relating to academic achievement, Hattie (2009) found that among the most significant factors are feedback, metacognitive strategies and reciprocal teaching. Future studies might find that the additive role of such variables changes the strength of the impact of school climate and school identification on academic achievement, because school climate and school identification may also have significant predictors and determinants.

A strength of the present study was the inclusion of school climate as a latent construct in the models. It would also be interesting for future studies to test the impact of discrete sub-factors of school climate. Testing their respective roles on achievement may expose more precise areas for improvement (e.g., increasing academic emphasis for achievement). This means that interventions could be crafted to pinpoint those factors more directly ( Wang and Degol, 2015 ). As noted previously, other types of social identification could also be tested, in order to further test the theoretical model. For example, classroom identification and peer-group identification could be tested for students, and workgroup identification and professional identification could be tested for staff.

The present study aimed to deepen our understanding of the contributions of student and staff perceptions of school climate to student achievement. The findings have consolidated the importance of school climate and school identification for student achievement. The present study also aimed to uncover the psychological mechanisms underlying the climate-achievement link. This aim was partly achieved, as students' school identification emerged as a mediator in two out of three learning domains. This has illuminated potential targets for interventions and fertile ground for future research. Furthermore, through the use of multilevel modeling and measurement of multiple perspectives of school climate, the study addressed important methodological concerns identified in the literature. Overall, this study provided empirical support demonstrating that school climate and social identification are core variables that have the power to augment student achievement.

Author Contributions

EL and KR: contributed to the conception and design of the work; the acquisition, analysis, and interpretation of data for the work; and revising the work. SM: contributed to the conception and design of the work; the analysis and interpretation of data for the work; and drafting the work. ES: contributed to the conception and design of the work. DB: contributed to the conception and design of the work; and the acquisition of data for the work.

This research was supported by the Australian Capital Territory Education and Training Directorate.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2017.02069/full#supplementary-material

1. ^ The research project was approved by the research ethics committee at ANU (No. 2009/293).

2. ^ A series of CFA were conducted for the SCASIM-Sf used in the present study (see Supplementary Material A for a full analysis). Five plausible models were competitively tested in order to identify factor structures underlying and causing the 36 item responses. The most parsimonious model was selected, based on conventional model fit information and theoretical considerations. The final model for the SCASIM-Sf mirrored the SCASIM-St scale, and showed good model fit (χ 2 = 2907.226 [p < .05], df = 580, CFI = 0.949, TLI = 0.945, SRMR = 0.051, RMSEA = 0.046).

3. ^ Formula for design effect ( Satorra and Muthen, 1995 ; Muthén and Muthén, 2009 ): D E F F =   V C V S R S = 1   ( s - 1 ) ρ

VC = correct variance under cluster sampling; VSRS = variance assuming simple random sampling; s = common cluster size and ρ = intra-class correlation.

4. ^ MULTILEVEL MODELING EQUATIONS

The following structural equations describe Model 7 (with numeracy as a dependent variable). This is an example of the last step of the hierarchical SEMs:

Level 1: student level (‘within school level’)

In the equations, the subscript j denotes the schools ( j = 1, 2, 3 …17) and i is for individual students ( i = 1, 2, 3….n) within the school. βs are regression coefficients. β 0 j represents the school intercept (latent factor) for the student's school which is estimated at the school level by Equation (3) in the following. β 1 j is the regression coefficient of grade that represents the predicted increase in achievement scores by one unit in grade. β 8 j is the regression coefficients of the interaction term of staff school climate perception and staff school identification. e ij is an estimated error at the student level, i.e., random error of deviation in the student score that is unexplained by the equation. Therefore, an individual student's NAPLAN score, Y ( Numeracy Score ) ij is the sum of the school intercept, β 0 j , for that student's school, all the serial predictor variables effect, and the individual level error, e ij . The mediator variable of students' school identification was also regressed on their school climate perception at the student level.

Level 2: school level (“between school level”)

In equation 3, γ denotes regression coefficients at the school level. An intercept for a school, β 0 j , is predicted by the average intercept over groups when all predictors are zero, γ 00 (a fixed effect); regression coefficient of socio-economic status γ 01 , of school size γ 02 , of response rate γ 03 , and finally the school level error, u 0 j (a random effect: deviation from average intercept for group j).

Multilevel Mode

If we integrate Model 7 into two equations by substituting Equation (3) into Equation (1), an individual student score would be estimated by the following Equation (4). In summary, a reading score of a student is the sum of the effects of school level predictors, student level predictors, and random errors (unexplained deviances) at both school and school level.

5. ^ As expected large intercorrelations between the four sub-factors of staff school climate perceptions ( r > 0.5) were observed. These correlations suggested a latent general factor of school climate and they were analyzed as a measurement model (CFA) in the main SEM. One instance of multicollinearity was detected, between staff's perceptions of shared values and approach and staff's school identification ( r = 0.91 > 0.9; Tabachnick and Fidell, 2014 ). Shared values and approach (‘SVA’) was a sub-factor of school climate, and analyzed in the factor structure of the higher order general school climate construct. Therefore, the multicollinearity between SVA and school identification did not directly affect the estimation of the parameters in the model. This multicollinearity issue may have arisen due to the disaggregation of staff data to the student data, which exaggerated the correlations that were identified in the CFA (supplementary analysis, Supplementary Material).

6. ^ ASPIRe is an acronym for Actualizing Social and Personal Identity Resources model.

Anderson, C. S. (1982). The search for school climate: a review of the research. Rev. Educ. Res. 52, 368–420. doi: 10.3102/00346543052003368

CrossRef Full Text | Google Scholar

Australian Bureau of Statistics, P. (2014). National Regional Profile: Australian Capital Territory . Available online at: http://www.abs.gov.au/AUSSTATS/[email protected]/Previousproducts/LGA8Population/People12007-2011opendocument&tabname=Summary&prodno=LGA8&issue=2007-2011

Australian Curriculum Assessment and Reporting Authority, ACARA (2008-2015). National Assessment Program: Literacy and Numeracy. Available online at: https://www.nap.edu.au/ .

Australian Curriculum Assessment and Reporting Authority, ACARA (2014). What Does The ICSEA Value Mean? . Available online at: https://acaraweb.blob.core.windows.net/resources/About_icsea_2014.pdf

Bandura, A. (1993). Perceived self-efficacy in cognitive development and functioning. Educ. Psychol. 28, 117–148. doi: 10.1207/s15326985ep2802_3

Bandura, A. (1997). Self-Efficacy: The Exercise of Control . New York, NY: Macmillan.

Bear, G. G., Yang, C., Pell, M., and Gaskins, C. (2014). Validation of a brief measure of teachers' perceptions of school climate: relations to student achievement and suspensions. Learn. Environ. Res. 17, 339–354. doi: 10.1007/s10984-014-9162-1

Bizumic, B., Reynolds, K. J., Turner, J. C., Bromhead, D., and Subasic, E. (2009). The role of the group in individual functioning: school identification and the psychological well-being of staff and students. Appl. Psychol. 58, 171–192. doi: 10.1111/j.1464-0597.2008.00387.x

Blum, R. W. (2005). A case for school connectedness. Educ. Leader. 62, 16–20. Available online at: https://eric.ed.gov/?id=EJ725962

Google Scholar

Boyd, D., Lankford, H., Loeb, S., and Wyckoff, J. (2005). Explaining the short careers of high-achieving teachers in schools with low-performing students. Am. Econ. Rev. 95, 166–171. doi: 10.1257/000282805774669628

Brand, S., Felner, R. D., Seitsinger, A., Burns, A., and Bolton, N. (2008). A large scale study of the assessment of the social environment of middle and secondary schools: the validity and utility of teachers' ratings of school climate, cultural pluralism, and safety problems for understanding school effects and school improvement. J. Sch. Psychol. 46, 507–535. doi: 10.1016/j.jsp.2007.12.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Brand, S., Felner, R., Shim, M., Seitsinger, A., and Dumas, T. (2003). Middle school improvement and reform: development and validation of a school-level assessment of climate, cultural pluralism, and school safety. J. Educ. Psychol. 95:570. doi: 10.1037/0022-0663.95.3.570

Bronfenbrenner, U. (1979). Contexts of child rearing: problems and prospects. Am. Psychol. 34:844. doi: 10.1037/0003-066X.34.10.844

Bronfenbrenner, U. (1986). Ecology of the family as a context for human development: research perspectives. Dev. Psychol. 22:723. doi: 10.1037/0012-1649.22.6.723

Brookover, W. B., Schweitzer, J. H., Schneider, J. M., Beady, C. H., Flood, P. K., and Wisenbaker, J. M. (1978). Elementary school social climate and school achievement. Am. Educ. Res. J. 15, 301–318. doi: 10.3102/00028312015002301

Bryk, A. S., and Raudenbush, S. W. (1988). Toward a more appropriate conceptualization of research on school effects: a three-level hierarchical linear model. Am. J. Educ. 97, 65–108. doi: 10.1086/443913

Burke, R., and Kao, G. (2013). Bearing the burden of whiteness: the implications of racial self-identification for multiracial adolescents' school belonging and academic achievement. Ethn. Racial Stud. 36, 747–773 doi: 10.1080/01419870.2011.628998

Caldas, S., and Bankston, C. (1997). Effect of school population socioeconomic status on individual academic achievement. J. Educ. Res. 90, 269–277. doi: 10.1080/00220671.1997.10544583

Caldas, S., and Bankston, C. (1999). Multilevel examination of student, school, and district-level effects on academic achievement. J. Educ. Res. 93, 91–100. doi: 10.1080/00220679909597633

Caprara, G. V., Barbaranelli, C., Steca, P., and Malone, P. S. (2006). Teachers' self-efficacy beliefs as determinants of job satisfaction and students' academic achievement: a study at the school level. J. Sch. Psychol. 44, 473–490. doi: 10.1016/j.jsp.2006.09.001

Chen, G., and Weikart, L. A. (2008). Student background, school climate, school disorder, and student achievement: an empirical study of New York City's middle schools. J. Sch. Violence 7, 3–20. doi: 10.1080/15388220801973813

Cohen, J., Pickeral, T., and McCloskey, M. (2009). Assessing school climate. Educ. Digest. 74, 45.

Collie, R. J., Shapka, J. D., and Perry, N. E. (2012). School climate and social-emotional learning: predicting teacher stress, job satisfaction, and teaching efficacy. J. Educ. Psychol. 104, 1189–1204. doi: 10.1037/a0029356

Collins, T. N., and Parson, K. A. (2010). School climate and student outcomes. J. Cross Discipl. Perspect. Educ. 3, 34–39.

Connell, J. P., and Wellborn, J. G. (1991). “Competence, autonomy, and relatedness: a motivational analysis of self-system processes,” in Self-Processes and Development: Minnesota Symposium on Child Psychology, Vol. 23 , eds M. R. Gunmar and L. A. Sroufe (Chicago, IL: University of Chicago Press), 43–77.

Cook, T. D., Murphy, R. F., and Hunt, H. D. (2000). Comer's school development program in Chicago: a theory-based evaluation. Am. Educ. Res. J. 37, 535–597. doi: 10.3102/00028312037002535

Crosnoe, R., Johnson, M. K., and Elder, G. H. (2004). Intergenerational bonding in school: the behavioral and contextual correlates of student-teacher relationships. Sociol. Educ. 77, 60–81. doi: 10.1177/003804070407700103

D'haenens, E., Van Damme, J., and Onghena, P. (2010). Multilevel exploratory factor analysis: illustrating its surplus value in educational effectiveness research. Sch. Effect. Sch. Improve. 21, 209–235. doi: 10.1080/09243450903581218

CrossRef Full Text

Davis-Kean, P. E. (2005). The influence of parent education and family income on child achievement: the indirect role of parental expectations and the home environment. J. Fam. Psychol. 19, 294. doi: 10.1037/0893-3200.19.2.294

Deci, E. L., and Ryan, R. M. (1985). Intrinsic Motivation and Self-Determination in Human Behavior . Berlin; Heidelberg: Springer Science & Business Media.

Espelage, D. L., Polanin, J. R., and Low, S. K. (2014). Teacher and staff perceptions of school environment as predictors of student aggression, victimization, and willingness to intervene in bullying situations. Sch. Psychol. Q. 29, 287. doi: 10.1037/spq0000072

Esposito, C. (1999). Learning in urban blights: school climate and its effect on the school performance of urban, minority, low-income children. Sch. Psychol. Rev. 28, 365.

Fan, W., Williams, C. M., and Corkin, D. M. (2011). A multilevel analysis of student perceptions of school climate: the effect of social and academic risk factors. Psychol. Sch. 48, 632–647. doi: 10.1002/pits.20579

Fisher, D. L., and Fraser, B. J. (1983). A comparison of actual and preferred classroom environments as perceived by science teachers and students. J. Res. Sci. Teach. 20, 55–61. doi: 10.1002/tea.3660200106

Frederickson, N. (1968). “Administrative performance in relation to organizational climate,” in Paper Presented at the American Psychological Association Convention (San Francisco, CA).

Garson, G. D. (ed.). (2013). “Fundamentals of hierarchical linear and multilevel modeling,” in Hierarchical Linear Modeling: Guide and Applications , (Beverly Hills, CA: Sage), 3–25. doi: 10.4135/9781483384450

Goddard, R. D., Hoy, W. K., and Hoy, A. W. (2000a). Collective teacher efficacy: its meaning, measure, and impact on student achievement. Am. Educ. Res. J. 37, 479–507. doi: 10.3102/00028312037002479

Goddard, R. D., Sweetland, S. R., and Hoy, W. K. (2000b). Academic emphasis of urban elementary schools and student achievement in reading and mathematics: a multilevel analysis. Educ. Administrat. Q. 36, 683–702. doi: 10.1177/00131610021969164

Goldstein, H., Browne, W., and Rasbash, J. (2002). Partitioning variation in multilevel models. Understand. Stat. 1, 223–231. doi: 10.1207/S15328031US0104_02

Gottfredson, G. D., Gottfredson, D. C., Payne, A. A., and Gottfredson, N. C. (2005). School climate predictors of school disorder: results from a national study of delinquency prevention in schools. J. Res. Crime Delinquency 42, 412–444. doi: 10.1177/0022427804271931

Grayson, J. L., and Alvarez, H. K. (2008). School climate factors relating to teacher burnout: a mediator model. Teach. Teach. Educ. 24, 1349–1363. doi: 10.1016/j.tate.2007.06.005

Gregory, A., Henry, D. B., and Schoeny, M. E. (2007). School climate and implementation of a preventive intervention. Am. J. Community Psychol. 40, 250–260. doi: 10.1007/s10464-007-9142-z

Gummadam, P., Pittman, L. D., and Ioffe, M. (2016). School belonging, ethnic identity, and psychological adjustment among ethnic minority college students. J. Exp. Educ. 84, 289–306. doi: 10.1080/00220973.2015.1048844

Halpern, D. F., and LaMay, M. L. (2000). The smarter sex: a critical review of sex differences in intelligence. Educ. Psychol. Rev. 12, 229–246. doi: 10.1023/A:1009027516424

Hampden-Thompson, G., and Galindo, C. (2017). School–family relationships, school satisfaction and the academic achievement of young people. Educ. Rev. 69, 248–265. doi: 10.1080/00131911.2016.1207613

Haslam, S. A., Eggins, R. A., and Reynolds, K. J. (2003). The ASPIRe model: actualizing social and personal identity resources to enhance organizational outcomes. J. Occup. Organ. Psychol. 76, 83–114. doi: 10.1348/096317903321208907

Haslam, S. A., Powell, C., and Turner, J. (2000). Social identity, self-categorization, and work motivation: rethinking the contribution of the group to positive and sustainable organizational outcomes. Appl. Psychol. 49, 319–339. doi: 10.1111/1464-0597.00018

Hattie, J. (2009). Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement . Abingdon, UK: Routledge.

Hayes, A. F. (2009). Beyond Baron and Kenny: statistical mediation analysis in the new millennium. Commun. Monogr. 76, 408–420. doi: 10.1080/03637750903310360

Haynes, N. M., Emmons, C., and Ben-Avie, M. (1997). School climate as a factor in student adjustment and achievement. J. Educ. Psychol. Consult. 8, 321–329. doi: 10.1207/s1532768xjepc0803_4

Heck, R. H. (2000). Examining the impact of school quality on school outcomes and improvement: a value-added approach. Educ. Administrat. Q. 36, 513–552. doi: 10.1177/00131610021969092

Hernández, M. M., Robins, R. W., Widaman, K. F., and Conger, R. D. (2014). School belonging, generational status, and socioeconomic effects on mexican-origin children's later academic competence and expectations. J. Res. Adolesc . 26, 241–256. doi: 10.1111/jora.12188

PubMed Abstract | CrossRef Full Text

Hinnant, J. B., O'Brien, M., and Ghazarian, S. R. (2009). The longitudinal relations of teacher expectations to achievement in the early school years. J. Educ. Psychol. 101, 662–670. doi: 10.1037/a0014306

Hox, J. (2010). Multilevel Analysis: Techniques and Applications . Abingdon, UK: Routledge.

Hoy, W. K., and Hannum, J. W. (1997). Middle school climate: an empirical assessment of organizational health and student achievement. Educ. Administrat. Q. 33, 290–311. doi: 10.1177/0013161X97033003003

Hoy, W. K., and Sabo, D. J. (1998). Quality Middle Schools: Open and Healthy. Thousand Oaks, CA: Corwin Press.

Hoy, W. K., and Woolfolk, A. E. (1993). Teachers' sense of efficacy and the organizational health of schools. Elem. Sch. J. 93, 355–372. doi: 10.1086/461729

Hoy, W. K., Hannum, J., and Tschannen-Moran, M. (1998). Organizational climate and student achievement: a parsimonious and longitudinal view. J. Sch. Leadership 8, 336–359.

Hoy, W. K., Sweetland, S. R., and Smith, P. A. (2002). Toward an organizational model of achievement in high schools: the significance of collective efficacy. Educ. Administrat. Q. 38, 77–93. doi: 10.1177/0013161X02381004

Hoy, W. K., Tarter, C. J., and Kottkamp, R. B. (1991). Open Schools/Healthy Schools: Measuring Organizational Climate. Beverly Hills, CA: Sage.

Jia, Y., Way, N., Ling, G., Yoshikawa, H., Chen, X., Hughes, D., et al. (2009). The influence of student perceptions of school climate on socioemotional and academic adjustment: a comparison of Chinese and American adolescents. Child Dev. 80, 1514–1530. doi: 10.1111/j.1467-8624.2009.01348.x

Johnson, B., and Stevens, J. J. (2006). Student achievement and elementary teachers' perceptions of school climate. Learn. Environ. Res. 9, 111–122. doi: 10.1007/s10984-006-9007-7

Johnson, B., Stevens, J. J., and Zvoch, K. (2007). Teachers' perceptions of school climate. Educ. Psychol. Meas. 67, 833. doi: 10.1177/0013164406299102

Johnson, M. K., Crosnoe, R., and Elder, G. H. Jr. (2001). Students' attachment and academic engagement: the role of race and ethnicity. Sociol. Educ. 74, 318–340. doi: 10.2307/2673138

Keith, T. Z., and Cool, V. A. (1992). Testing models of school learning: effects of quality of instruction, motivation, academic coursework, and homework on academic achievement. Sch. Psychol. Q. 7, 207. doi: 10.1037/h0088260

Konold, T., and Cornell, D. (2015). Multilevel multitrait–multimethod latent analysis of structurally different and interchangeable raters of school climate. Psychol. Assess. 27, 1097–1109. doi: 10.1037/pas0000098

Lee, E., Reynolds, K. J., Subasic, E., Bromhead, D., Lin, H., Marinov, V., et al. (2017). Development of a dual school climate and school identification measure–student (SCASIM-St). Contemp. Educ. Psychol. 49, 91–106. doi: 10.1016/j.cedpsych.2017.01.003

Lee, V. E., and Loeb, S. (2000). School size in Chicago elementary schools: effects on teachers' attitudes and students' achievement. Am. Educ. Res. J. 37, 3–31. doi: 10.3102/00028312037001003

Libbey, H. P. (2004). Measuring student relationships to school: attachment, bonding, connectedness, and engagement. J. Sch. Health 74, 274. doi: 10.1111/j.1746-1561.2004.tb08284.x

Lindjord, D. (2003). The start of a new school year: research-based classroom practices for improving student achievement in children from high poverty and minority families. Family review. J. Early Educ. Fam. Rev. 11, 4–5. Available online at: https://eric.ed.gov/?id=EJ679713

Liu, Y., Ding, C., Berkowitz, M. W., and Bier, M. C. (2014). A psychometric evaluation of a revised school climate teacher survey. Can. J. Sch. Psychol. 29, 54–67. doi: 10.1177/0829573514521777

Longobardi, C., Prino, L. E., Marengo, D., and Settanni, M. (2016). Student-teacher relationships as a protective factor for school adjustment during the transition from middle to high school. Front. Psychol. 7:1988. doi: 10.3389/fpsyg.2016.01988

Loukas, A., Suzuki, R., and Horton, K. D. (2006). Examining school connectedness as a mediator of school climate effects. J. Res. Adolesc. 16, 491–502. doi: 10.1111/j.1532-7795.2006.00504.x

Lubienski, S. T., Lubienski, C., and Crane, C. C. (2008). Achievement differences and school type: the role of school climate, teacher certification, and instruction. Am. J. Educ. 115, 97–138. doi: 10.1086/590677

Ma, X., and Klinger, D. A. (2000). Hierarchical linear modelling of student and school effects on academic achievement. Can. J. Educ. 25, 41–55. doi: 10.2307/1585867

Ma, X., and MacMillan, R. B. (1999). Influences of workplace conditions on teachers' job satisfaction. J. Educ. Res. 93, 39–47. doi: 10.1080/00220679909597627

MacKinnon, D. P. (2008). Introduction to Statistical Mediation Analysis . New York, NY: Lawrence Erlbaum Associates.

MacNeil, A. J., Prater, D. L., and Busch, S. (2009). The effects of school culture and climate on student achievement. Int. J. Leader. Educ. 12, 73–84. doi: 10.1080/13603120701576241

Marsh, H. W., Trautwein, U., Lüdtke, O., Köller, O., and Baumert, J. (2005). Academic self-concept, interest, grades, and standardized test scores: reciprocal effects models of causal ordering. Child Dev. 76, 397–416. doi: 10.1111/j.1467-8624.2005.00853.x

Mitchell, M. M., Bradshaw, C. P., and Leaf, P. J. (2010). Student and teacher perceptions of school climate: a multilevel exploration of patterns of discrepancy. J. Sch. Health 80, 271–279. doi: 10.1111/j.1746-1561.2010.00501.x

Mohammadpour, E. (2012). A multilevel study on trends in Malaysian secondary school students' science achievement and associated school and student predictors. Sci. Educ. 96, 1013–1046. doi: 10.1002/sce.21028

Moos, R. H. (1987). Person-environment congruence in work, school, and health care settings. J. Vocat. Behav. 31, 231–247. doi: 10.1016/0001-8791(87)90041-8

Moos, R. H., and Moos, B. S. (1978). Classroom social climate and student absences and grades. J. Educ. Psychol. 70, 263–269. doi: 10.1037/0022-0663.70.2.263

Muthén, L. K., and Muthén, B. (2009). Multilevel Modeling With Latent Variables Using Mplus: Cross-Sectional Analysis . Available online at: https://www.statmodel.com/download/Topic%207-v25.pdf

Muthén, L. K., and Muthén, B. O. (1998–2015). Mplus User's Guide, 7th Edn . Los Angeles, CA: Muthén & Muthén.

Osterman, K. F. (2000). Students' need for belonging in the school community. Rev. Educ. Res. 70, 323–367. doi: 10.3102/00346543070003323

Pasta, T., Mendola, M., Longobardi, C., Pino, L. E., and Gastaldi, F. G. M. (2013). Attributional style of children with and without Specific Learning Disability. Electron. J. Res. Educ. Psychol. 11, 649–664. doi: 10.14204/ejrep.31.13064

Perry, L., and McConney, A. (2010). School socio-economic composition and student outcomes in Australia: implications for educational policy. Aust. J. Educ. 54, 72–85. doi: 10.1177/000494411005400106

Petrie, K. (2014). The relationship between school climate and student bullying. TEACH J. Christ. Educ. 8, 26–35. Available online at: https://research.avondale.edu.au/cgi/viewcontent.cgi?referer=https://scholar.google.com.au/&https%20redir=1&article=1237&context=teach

Ramelow, D., Currie, D., and Felder-Puig, R. (2015). The assessment of school climate review and appraisal of published student-report measures. J. Psychoeduc. Assess. 33, 731–743. doi: 10.1177/0734282915584852

Raviv, A., Raviv, A., and Reisel, E. (1990). Teachers and students: Two different perspectives?! Measuring social climate in the classroom. Am. Educ. Res. J. 27, 141–157. doi: 10.3102/00028312027001141

Reeve, J. (2012). “A self-determination theory perspective on student engagement,” in Handbook of Research on Student Engagement , eds S. L. Christenson, A. L. Reschly, and C. Wylie (Berlin; Heidelberg: Springer), 149–172.

Reyes, M. R., Brackett, M. A., Rivers, S. E., White, M., and Salovey, P. (2012). Classroom emotional climate, student engagement, and academic achievement. J. Educ. Psychol. 104, 700. doi: 10.1037/a0027268

Reynolds, K. J., and Branscombe, N. (2015). Psychology of Change: Life Contexts, Experiences, and Identities, Vol. 2 . Beaverton, OR: Ringgold Inc.

Reynolds, K. J., Lee, E., Turner, I., Bromhead, D., and Subasic, E. (2017). How does school climate impact academic achievement? An examination of social identity processes. Sch. Psychol. Int. 38, 78–97. doi: 10.1177/0143034316682295

Reynolds, K. J., Subasic, E., and Bromhead, D. (2012). School Identification, School Climate and School Outcomes: Evidence from ACT Schools . Grant funded by Education and Training Directorate: The Australian National University.

Roeser, R. W., Eccles, J. S., and Sameroff, A. J. (1998). Academic and emotional functioning in early adolescence: longitudinal relations, patterns, and prediction by experience in middle school. Dev. Psychopathol. 10, 321–352. doi: 10.1017/S0954579498001631

Roeser, R. W., Eccles, J. S., and Sameroff, A. J. (2000). School as a context of early adolescents' academic and social-emotional development: a summary of research findings. Elem. Sch. J. 100, 443–471. doi: 10.1086/499650

Rosenfeld, L. B., Richman, J. M., and Bowen, G. L. (2000). Social support networks and school outcomes: the centrality of the teacher. Child Adolesc. Soc. Work J. 17, 205–226. doi: 10.1023/A:1007535930286

Satorra, A., and Muthen, B. (1995). Complex sample data in structural equation modeling. Sociol. Methodol. 25, 267–316.

Schacter, J., and Thum, Y. M. (2004). Paying for high-and low-quality teaching. Econ. Educ. Rev. 23, 411–430. doi: 10.1016/j.econedurev.2003.08.002

Senler, B., and Sungur, S. (2009). Parental influences on students' self-concept, task value beliefs, and achievement in science. Span. J. Psychol. 12, 106–117. doi: 10.1017/S1138741600001529

Smith, P. A., and Hoy, W. K. (2007). Academic optimism and student achievement in urban elementary schools. J. Educ. Administrat. 45, 556–568. doi: 10.1108/09578230710778196

Stewart, E. B. (2007). Individual and school structural effects on African American high school students' academic achievement. High Sch. J. 91, 16–34. doi: 10.1353/hsj.2008.0002

Sweetland, S. R., and Hoy, W. K. (2000). School characteristics and educational outcomes: toward an organizational model of student achievement in middle schools. Educ. Administrat. Q. 36, 703–729. doi: 10.1177/00131610021969173

Tabachnick, B. G., and Fidell, L. S. (2014). Using Multivariate Statistics, 6th Edn . New York, NY: Pearson Education.

Tajfel, H., and Turner, J. (1979). “An integrative theory of intergroup conflict,” in The Social Psychology of Intergroup Relations , eds W. G. Austin and S. Worchel (Pacific Grove, CA: Brooks & Cole), 33–47.

Taylor, G., Jungert, T., Mageau, G. A., Schattke, K., Dedic, H., Rosenfield, S., et al. (2014). A self-determination theory approach to predicting school achievement over time: the unique role of intrinsic motivation. Contemp. Educ. Psychol. 39, 342–358. doi: 10.1016/j.cedpsych.2014.08.002

Thapa, A., Cohen, J., Guffey, S., and Higgins-D'Alessandro, A. (2013). A review of school climate research. Rev. Educ. Res. 83, 357–385. doi: 10.3102/0034654313483907

Thomas, W. P., and Collier, V. P. (2002). A National Study of School Effectiveness for Language Minority Students' Long-Term Academic Achievement . Available online at: http://files.eric.ed.gov/fulltext/ED475048.pdf

Tschannen-Moran, M., Parish, J., and DiPaola, M. (2006). School climate: the interplay between interpersonal relationships and student achievement. J. Sch. Leadership 16, 386.

Turner, I., Reynolds, K. J., Lee, E., Subasic, E., and Bromhead, D. (2014). Well-being, school climate, and the social identity process: a latent growth model study of bullying perpetration and peer victimization. Sch. Psychol. Q. 29, 320–335. doi: 10.1037/spq0000074

Turner, J. C. (1985). “Social categorization and the self-concept: A social cognitive theory of group behavior,” in Advances in Group Processes: Theory and Research, Vol. 2 , ed E. J. Lawler (Greenwich, CT: JAI Press), 72–121.

Turner, J. C., Oakes, P. J., Haslam, S. A., and McGarty, C. (1994). Self and collective: cognition and social context. Pers. Soc. Psychol. Bull. 20, 454–454. doi: 10.1177/0146167294205002

Turner, J., Hogg, M., Oakes, P., Reicher, S., and Wetherell, M. (1987). Rediscovering the Social Group: A Self-Categorization Theory . Cambridge: Basil Blackwell.

Turner, J. C., and Reynolds, K. J. (2011). Self-categorization theory. Handbook of Theories in Soc. Psychol. 2, 399–417.

Vieno, A., Perkins, D. D., Smith, T. M., and Santinello, M. (2005). Democratic school climate and sense of community in school: a multilevel analysis. Am. J. Community Psychol. 36, 327–341. doi: 10.1007/s10464-005-8629-8

Wang, M. T., and Degol, J. L. (2015). School climate: a review of the construct, measurement, and impact on student outcomes. J. Educ. Psychol. Rev. 28, 1–38. doi: 10.1007/s10648-015-9319-1

Wang, M. T., and Eccles, J. S. (2014). Multilevel predictors of math classroom climate: a comparison study of student and teacher perceptions. J. Res. Adolesc . 26, 617–634. doi: 10.1111/jora.12153

Wang, M.-T., and Holcombe, R. (2010). Adolescents' perceptions of school environment, engagement, and academic achievement in middle school. Am. Educ. Res. J. 47, 633–662. doi: 10.3102/0002831209361209

Wang, M.-T., Selman, R. L., Dishion, T. J., and Stormshak, E. A. (2010). A tobit regression analysis of the covariation between middle school students' perceived school climate and behavioral problems. J. Res. Adoles. 20, 274–286. doi: 10.1111/j.1532-7795.2010.00648.x

Waters, S. K., Cross, D. S., and Runions, K. (2009). Social and ecological structures supporting adolescent connectedness to school: a theoretical model. J. Sch. Health 79, 516–524. doi: 10.1111/j.1746-1561.2009.00443.x

Way, N., Reddy, R., and Rhodes, J. (2007). Students' perceptions of school climate during the middle school years: associations with trajectories of psychological and behavioral adjustment. Am. J. Community Psychol. 40, 194–213. doi: 10.1007/s10464-007-9143-y

Yang, Y. (2014). Principals' transformational leadership in school improvement. Int. J. Educ. Manage. 28, 279–288. doi: 10.1108/IJEM-04-2013-0063

Keywords: academic achievement, school climate, school identification, social identity, student and staff/teacher perceptions, multilevel analysis

Citation: Maxwell S, Reynolds KJ, Lee E, Subasic E and Bromhead D (2017) The Impact of School Climate and School Identification on Academic Achievement: Multilevel Modeling with Student and Teacher Data. Front. Psychol . 8:2069. doi: 10.3389/fpsyg.2017.02069

Received: 30 April 2017; Accepted: 14 November 2017; Published: 05 December 2017.

Reviewed by:

Copyright © 2017 Maxwell, Reynolds, Lee, Subasic and Bromhead. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Eunro Lee, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Nearly Half of Educators Say Climate Change Is Affecting Their Schools—or Will Soon

• Share article

One in 4 teachers, principals, and district leaders say that climate change is impacting their school or district to some extent. And an additional 18 percent say that while their district has not yet been affected by climate change, they believe it poses an imminent threat, according to a nationally representative survey of educators by the EdWeek Research Center.

The survey responses, taken in February, give a rare look into educator attitudes toward climate change and its impact on their school communities. School buildings across the country have been destroyed or forced to close in response to wildfires, extreme heat, and flooding due to hurricanes. These more severe and frequent natural disasters, which have been linked to climate change, affect students’ learning and physical and mental health.

Even so, 8 percent of educators in EdWeek’s nationally-representative online survey of 960 respondents said they do not believe climate change is real. (In comparison, 14 percent of Americans don’t believe global warming is happening, according to a 2021 summary of public opinion surveys from the Yale Program on Climate Change Communication.) The vast majority of climate scientists and peer-reviewed scientific studies on climate change agree that humans are the driving cause of rising global temperatures, which are altering weather patterns and causing sea levels to rise.

But most school districts have not taken any action in the past five years to prepare for more severe weather related to climate change, according to 84 percent of principals and district leaders surveyed by the EdWeek Research Center.

The reasons? The most cited, by 36 percent of school and district leaders, was that their campuses are located in areas that they don’t expect to be severely impacted by climate change in the near future.

Many educators also pointed to concerns that senior district leaders, school board members, and the broader community would be resistant to taking action—either because people don’t believe climate change is real or that it is not an immediate threat. There are also many more immediate crises competing for school and district leaders’ attention: the ongoing pandemic, student mental health, catching students up academically, and charged debates over how race and LGBTQ issues should be taught in schools—to name just a few. Not all district leaders or other stakeholders are convinced climate change is an area districts should be spending their limited time and resources on.

“One of the things that jumped out at me, a lot of people indicate that they are scared of working on these issues because of a fear of people being dismissive of them,” said Laura Schifter, a senior fellow at the Aspen Institute, leading the organization’s K-12 climate action initiative, who was not involved in EdWeek’s survey. But, she said, she’s encouraged by the fact that educators themselves are not dismissive of the effects climate change will have on their school communities.

When asked for their personal views on the impact of climate change on their district or school, 16 percent of teachers, principals, and district leaders said that climate change has already had a mild effect, while 7 percent said they have seen a moderate effect. Two percent said climate change has already had a severe effect on their district or school.

Larger shares of respondents said although they believe climate change is real, they didn’t think it would impact their district in the foreseeable future (15 percent) or that climate change was unlikely to impact their district because of where it was located (25 percent).

That perception, that climate change will only affect some geographic areas and not others, hints at a misunderstanding of how changing weather patterns will affect our interconnected world, said Schifter.

“Thinking about how climate change is going to impact schools is broader than just the impact of extreme weather on that school,” she said.

For example, extreme weather in other parts of the country could lead to students being displaced, which will affect the schools that take them in, Schifter said. It will also change the economy and what kinds of jobs will be in demand in the future.

“As we start to think about the jobs that will be needed—whether that’s jobs around clean energy or jobs around what’s needed for adaptation or, frankly, emergency management—our school systems need to keep up to ensure that they’re providing students the skills they need to be successful in those jobs,” she said.

What schools are doing to prep for climate change

Overall, schools and districts are putting more of their energy toward reacting to the effects of climate change than in efforts to reduce their carbon footprints.

Nearly half of principals and district leaders said their campuses had invested in infrastructure to support remote instruction when, or if, severe weather does not allow for in-person classes, while 43 percent say they have upgraded school buildings to better withstand severe weather.

“The investment in infrastructure to promote remote instruction—that seems it’s totally reactive to the pandemic. Ultimately, what the pandemic has highlighted is that we need to build more resilience to prepare for disruption,” said Schifter. “I think it’s encouraging that in [educators’] response to climate change, there was an acknowledgement of the fact that what they have done with COVID is helping them prepare for extreme weather related to climate change.”

Large shares of principals and district leaders said their campuses had taken climate change into account when developing emergency response plans (22 percent) and facilities plans (30 percent).

Among other preparations:

• Thirty-nine percent of principals and district leaders said their campuses had started using energy-efficient appliances, and 17 percent said their school or district has invested in sustainable energy sources such as solar or wind power.
• Thirteen percent said their district had developed a strategic plan related to climate change.
• Another 13 percent said their school or district had taken steps to reduce their carbon footprint through efforts like composting, and 4 percent said they had set targets for reducing their carbon footprints.
• Eleven percent of school and district leaders said they had eliminated single-use plastics in their schools.
• Six percent said they had converted or planned to convert gasoline- or diesel-powered vehicles, such as school buses, to electric.
• Four percent said they had purchased new or different insurance for severe weather, and 2 percent of principals and district leaders said they had gone so far as to close or relocate buildings in locations that are most likely to be impacted by severe weather.

Schools can play an outsized role in reducing carbon emissions, according to the Aspen Institute. Schools are one of the largest public sector energy consumers in the country , they operate what equates to the nation’s largest mass transit fleet, and they generate over 530,000 tons of food waste a year.

In terms of what teachers, principals, and district leaders say is needed to improve their school or district’s ability to prepare for the effects of climate change, money—perhaps not surprisingly—was one of the two most cited supports.

The other: 36 percent said that better efforts to educate stakeholders about the need to prepare for climate change was also necessary.

Nearly a third said they felt they needed support from the broader community to improve their school or district’s ability to confront climate change.

Eleven percent said that their school or district needed nothing to help prepare for the effects of climate change because their campuses were already well-prepared, and 9 percent said that climate change does not exist or is not a threat to their school or district.

About This Series

This article is part of an ongoing Education Week series, The Climate Crisis and Schools , about how climate change and schools intersect. We aim to illuminate how schools contribute to climate change; highlight challenges districts face in dealing with the effects of climate change; and offer solutions to the feelings of helplessness and anxiety that often accompany this subject. If you have a related story idea for us, please email staff writer Madeline Will at [email protected] .

Data analysis for this article was provided by the EdWeek Research Center. Learn more about the center’s work.

Sign Up for The Savvy Principal

Edweek top school jobs.

Sign Up & Sign In