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• Published: 02 August 2022

The influence of ICT use and related attitudes on students’ math and science performance: multilevel analyses of the last decade’s PISA surveys

• Matthew Courtney   ORCID: orcid.org/0000-0002-3253-8353 1 ,
• Mehmet Karakus   ORCID: orcid.org/0000-0002-3628-9809 2 ,
• Zara Ersozlu   ORCID: orcid.org/0000-0002-9120-2921 3 &
• Kaidar Nurumov 4  

Large-scale Assessments in Education volume  10 , Article number:  8 ( 2022 ) Cite this article

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This study analyzed the latest four PISA surveys, 2009, 2012, 2015, and 2018, to explore the association between students’ ICT-related use and math and science performance. Using ICT Engagement Theory as a theoretical framework and a three-level hierarchical linear modeling approach, while controlling for confounding effects, ICT-related independent variables of interest were added to the models at the student, school, and country levels. The series of models revealed that, in general, an increase in ICT availability and ICT use both inside and outside school had a negative association with learning outcomes, while students’ positive attitude toward ICT demonstrated a strong positive relationship. However, students’ perceived autonomy related to ICT use had the strongest association with academic performance, which is consistent with the changing nature of the modern learning environments. Findings revealed that virtually all forms of student ICT use, both inside and outside of school and whether subject related or not, had no substantive positive relationship with student performance in math or science. Conversely, higher student attitude toward, confidence in, belief in utility of, and autonomous use of ICT was associated with higher math and science performance for each of the four years of the study. Incidentally, we also found that while country GDP per capita had no consistent association with student performance, a school’s provision of extra-curricula activities did. Recommendations for educational leaders, teachers, and parents are offered.

Introduction

The use of Information and Communication Technology (ICT) have been a hot topic in education research since the beginning of the 1990s. ICT usage in vocational training, primary and secondary education is rapidly growing all around the world, but it remains unequally distributed across countries (OECD, 34 ). Schools are looking for new ways to integrate ICT skills into their policies and curriculum to foster the teaching and learning process in the context of “twenty-first-century skills” (Anderson, 1 ).

There is a rich research collection investigating the vital role that school ICT infrastructure and student ICT-related behaviour plays in students’ academic development, with much of this research country-specific, based on Programme for International Student Assessment (PISA) data, and focusing on only one cycle of PISA with some selection of variables (Biagi & Loi, 7 ; Bulut & Cutumisu, 9 ; Carrasco & Torrecilla, 9 ; Erdogdu & Erdogdu, 9 ; Hu et al., 21 ; Luu & Freeman, 24 ; Petko et al., 34 ; Wittwer et al., 51 ). We believe that researching the trends throughout the last decade of PISA cycles and making use of all key ICT-related variables can provide a more holistic picture of how school ICT infrastructure, ICT use and availability, and attitudes toward ICT is associated with academic performance over time. Therefore, the current study aims to explore the relationship between of ICT infrastructure, ICT use and availability inside and outside school, and students’ attitudes toward ICT and students’ math and science abilities measured in all the PISA surveys within the last decade (2009, 2012, 2015, and 2018).

Theoretical framework

This study uses Self-Determination Theory (SDT) to explain the associations between ICT-related variables and students’ academic performance. We bring together a set of environmental factors, individual differences, ICT use and availability inside and outside school, and attitudes toward ICT to explain the differences in students’ math and science performance. SDT asserts that self-motivation and determination are the main drivers of an individual’s learning (Deci & Ryan, 9 ). Competence (mastery and control over outcomes), relatedness (the drive to communicate with others), and autonomy (the desire to make their own choices) are the three basic facets in SDT used to explain mastery in learning (Deci & Ryan, 9 ). Based on SDT, Goldhammer et al. ( 9 ) introduced the ICT engagement concept with the dimensions of perceived autonomy related to ICT use, perceived ICT competence, ICT interest, and ICT as a topic in social interaction. Goldhammer et al. ( 9 ) assert that it is not only the use and availability of ICT inside and outside school but the underlying attitudes toward ICT that predict students’ academic achievement. Based on SDT, ICT Engagement Theory suggests that students’ interest, positive social interactions, autonomy, and competence related to ICT increase their intrinsic motivation, enabling them to challenge themselves with self-driven technology use, which can generate conditions conducive to optimal academic performance (Goldhammer et al., 9 ). Based on ICT Engagement Theory (Cristoph et al., 9 ; Kunina-Habenicht & Goldhammer, 24 ), student attitudes toward ICT were partially covered in the 2009 and 2012 cycles of PISA, while they were more closely reflected in the 2015 and 2018 cycles in the “ICT Familiarity Questionnaire” (OECD, 24 ).

In addition to our focus on student-related ICT variables, we also explore the role of background and ICT-related variables on student science and math performance. Though researched rarely (see Hu et al, 21 ), we explore the association between GDP per capita and Math and Science performance for each of the four cycles. Under SDT, it is important to consider the role of such contextual effects (Deci & Ryan, 9 ) and report on the results to educational stakeholders (Skyrabin et al., 34 ).

Considering that some schools can be considered digital frontrunners (Novak et al., 24 ) we are also interested in the role of school ICT infrastructure for the study period. Specifically, while controlling for important covariates (Zhang & Liu, 21 ) we look at the role of the number of available computers per student and the proportion of available computers connected to the internet in schools on the math and science performance of schools.

Students’ attitudes toward ICT

The empirical evidence suggests that students’ positive attitudes toward ICT are positively associated with their mathematics and science performance (Petko et al., 34 ; Tourón et al., 34 ). Areepattamannil and Santos ( 2 ) found that students who perceived themselves as autonomous and competent in ICT use develop positive views and feelings towards science, such as self-efficacy, enjoyment, and interest in science. Numerous studies have supported the notion that students’ mathematics and science achievement is associated with autonomous use of ICT (Hu et al., 21 ; Juhaňák et al., 9 ; Kunina-Habenicht & Goldhammer, 24 ; Meng et al., 21 ), interest in ICT use (Christoph et al., 9 ; Hu et al., 21 ; Koğar, 21 ; Kunina-Habenicht & Goldhammer, 24 ; Meng et al., 21 ), perceived self-confidence in ICT use (Guzeller & Akin, 9 ; Luu & Freeman, 24 ), and perceived self- competence in ICT use (Hu et al., 21 ; Koğar, 21 ; Kunina-Habenicht & Goldhammer, 24 ; Luu & Freeman, 24 ; Papanastasiou et al., 21 ; Srijamdee & Pholphirul, 44 ).

Although most of the studies reported positive relations between those attitudes and mathematics and science performance, Meng et al. ( 21 ) and Juhaňák et al. ( 9 ) reported controversial results for some of those attitudes. Meng et al. ( 21 ) found that the association between interest in ICT and mathematics and science performance was positive for the Chinese students while negative for the German students. Meng et al. ( 21 ) also reported a negative relationship between perceived self-competence and mathematics and science performance for the Chinese students, while there is no relation for the German students. In addition, Juhaňák et al. ( 9 ) found no associations between mathematics and science achievement with either interest in ICT or perceived self-competence (Czech students). On the other hand, most of the studies found negative relations between ICT use in social interaction and mathematics and science performance (Hu et al., 21 ; Juhaňák et al., 9 ; Meng et al., 21 ). Conversely, Martínez-Abad, Gamazo, and Rodríguez-Conde ( 9 ) reported positive associations between ICT use in social interaction and mathematics and science achievement on a sample of Spanish students. Given the conflicting results pertaining to students’ attitude toward ICT and academic performance, more substantial research in this area is in order.

ICT use and availability inside and outside of school

Research has suggested that ICT can add value to the learning process (UNESCO, 44 ). ICT use in educational settings with academic purposes has been shown to be useful in improving students’ performance in science (Erdogdu & Erdogdu, 9 ; Luu & Freeman, 24 ; Skryabin et al., 34 ) and mathematics (Carrasco & Torrecilla, 9 ; Erdogdu & Erdogdu, 9 ; Skryabin et al., 34 ).

The research on the impact of technology on learning outcomes, especially in mathematics and science, revealed the importance of technology use in education (Luu & Freeman, 24 ; Rutten et al., 24 ; Tamim et al., 21 ; Wittwer & Senkbeil, 51 ). Further, several meta-analysis studies suggested that ICT use in education has a small but positive impact on student performance (Bayraktar, 5 ; Cheung & Slavin, 9 ; Torgerson & Zhu, 51 ). However, a substantive number of research studies using large-scale international databases investigated how forms of ICT availability, use, and engagement has a positive association with student performance in mathematics and science (i.e., databases such as PISA, the Trends in International Mathematics and Science Study, TIMSS; and the Progress in International Reading Literacy Study, PIRLS). Importantly, the majority of these studies suggested that increased use of ICT at school had a negative association with mathematics and science performance (Bulut & Cutumisu, 9 ; Erdogdu & Erdogdu, 9 ; Hu et al., 21 ; Petko et al., 34 ; Skryabin et al., 34 ; Wittwer & Senkbeil, 51 ). The summary of the findings of a number of these key studies is now provided.

Using the PISA 2012 data, Petko et al. ( 34 ) investigated the role of the frequency of educational technology use on student achievement. They found that while ICT use at home for school purposes had a positive relationship with achievement, ICT use for entertainment purposes and the magnitude of use at school had a negative relationship with achievement. They also found that students’ positive attitudes towards educational technology were associated with higher test scores in most countries. They concluded that the moderate use of educational technology could be related to higher achievement, though both low and intensive use of educational technology in school appears to have a negative association. To explain this finding, the authors inferred that students’ lower academic achievement could be the result of ineffective pedagogy while they used technology and low-quality educational software that is used in the teaching process. However, these results of the study were not conclusive and there were a limited number of control variables used in the analysis.

Skryabin et al. ( 34 ) investigated how country-level ICT development and individual ICT usage was related to 4th- and 8th-grade student achievement in reading, mathematics, and science based on the data from TIMSS 2011, PIRLS 2011, and PISA 2012. The analysis revealed that country-level ICT development was a significant positive predictor for individual academic performance in all three subjects for both 4th- and 8th-grade students. After controlling for students’ gender and socioeconomic status, they found that country-level ICT development and student ICT use at home had a positive relationship with students’ academic performance; however, the ICT rate of change (measured by country’s recent shift in the ICT development index; International Telecomunications Union, 2012) had a negative association with students’ academic performance, but this link was not always significant for all subjects.

Early research by Wittwer and Senkbeil ( 51 ) investigated the role of using computers at home and school on student academic performance (based on PISA 2003 data). Their results suggested that, for the majority of students, the use of the computer at home or at school had no substantial influence on their academic achievement. However, more recently, Hu et al. ( 21 ) conducted research on how national ICT skills affected students’ academic performance (using PISA 2015 data). They found that ICT skills had a positive relationship with student academic performance and that ICT availability at school also had a positive relationship with students’ academic performance. In addition, the researchers found that student use of ICT for academic purposes had a positive relationship with student performance, whereas student use of ICT for entertainment purposes had a negative relationship. However, the study did not control for school SES and only focused on one year so could not draw conclusions across multiple cycles.

Wainer et al. ( 56 ) analysed the 2001 Brazilian Basic Education Evaluation System (SAEB) achievement exam for 4th-, 8th-, and 11th-grade students in mathematics and reading (Portuguese). The results suggested that the frequency of computer use had (1) a negative association with test results, and (2) a particularly high negative association with the test results of younger and lower-ability students. The researchers also identified that having internet access had a negative relationship with the academic performance of younger students, whilst this relationship was positive for older students. More recent research has explored the association between internet availability at school and home and student academic performance. Erdogdu and Erdogdu ( 9 ) explored the associations between access to ICT, student background, and school/home environment and students’ academic performance based on PISA 2012 data. While controlling for parental education level and socio-economic conditions (e.g., students’ having their own room), findings suggested that internet availability at home and at school was positively associated with students’ academic performance. Though, the specific relationships between availability and types of ICT use across the last decade have yet to be explored. Moreover, the nuanced associations between outside-of-school ICT use for leisure and social interaction for all countries has yet to be examined comprehensively in the literature.

Carrasco and Torrecilla ( 9 ), drawing upon PISA 2006 data, researched how computer access and use affected students’ academic performance. They found that computer access and use had a positive association with student performance. The researchers found that having a computer at home had a significant positive association with students’ reading and mathematics performance. Furthermore, Bulut, and Cutumisu ( 9 ) examined whether the use and availability of ICT at home and school was related to students’ academic success in the PISA 2012 mathematics and science-based assessments in Finland and Turkey. In both countries, they found that the use of ICT for mathematics lessons had a negative association with mathematics success; however, the general use of ICT at school had no substantive relationship with student performance in both mathematics and science. Finally, findings suggested that the use of ICT for entertainment had a positive association with students’ academic successes in Turkey while at the same time a negative association with students’ academic performances in Finland. Though, nuanced relationships for outside-of-school ICT use for leisure and social interaction for all countries has yet to be examined comprehensively. In another related study, Luu and Freeman ( 24 ) analysed the relationship between ICT use and scientific literacy across Canada and Australia based on PISA 2006 data. Their results suggested that students who browse the Internet more frequently and those who were more confident with basic ICT tasks earned higher scientific literacy scores. Though, more recent work in this area appears to be lacking.

Controversial findings in the associations between ICT related variables and students’ academic performance may have stemmed from the variety of PISA results across different nationalities, cycles, subjects, the combination of the variables chosen by the researchers, or the statistical approaches adopted by the researchers. For instance, ICT availability and use had a positive relationship with mathematics and science performance of Turkish students in PISA 2012 data, while it has either negative or no association with the performance of the Finnish student sample in the same study Bulut & Cutumisu 10 ). Similarly, using PISA 2015 data, Meng et al. ( 21 ) observed negative associations between mathematics and science performance of the Chinese and German students and their self-competence and interest in ICT, as opposed to the PISA results of the other countries. Juhaňák et al. ( 9 ) and Luu and Freeman ( 24 ) took into account the moderation effect of the frequency of ICT use on academic performance and found divergent results regarding the subgroups of students who used ICT at low, moderate, and high levels. They found that very low and very high usage of ICT had a negative association with academic performance. Biagi and Loi ( 7 ) found a positive association between ICT use for gaming and students’ academic performance. Petko et al. ( 34 ) argued that the controversial positive relationship could have resulted from an artefact of the method of analysis that Biagi and Loi ( 7 ) used. Rodrigues and Biagi’s ( 21 ) findings varied substantially by the combinations of type of school, frequency of ICT use, and ESCS (student economic, social, and cultural status) regarding the subgroups of the chosen variables. Through the econometric specification method they adopted, they regressed the students' performance on the different frequencies of ICT uses, while controlling for other variables that could be simultaneously associated with the dependent and independent variables. They found that low-frequency ICT users with mid to high ESCS benefit the most from an increased ICT use at school. They also reported that the positive association between ICT use at home for schoolwork and students’ science performance is stronger than those with low ESCS in private schools. In the current study, rather than comparing specific countries or testing any moderation or mediation effects, we use all the ICT related predictors of students’ mathematics and science performance using the complete data sets from the latest four PISA cycles to provide a comprehensive view of the subject matter.

The rationale for the current study

There is an increasing trend in the amount of research based on PISA data with interest in ICT skills and how these skills affect our students’ performances and other related constructs. Based on the rich research evidence, it was evident that ICT use can have a positive (small to moderate generally) association with students’ academic successes whilst it does depend on students’ purpose of using ICT, attitudes toward ICT, and the availability of ICT at both home and school.

Of the PISA studies reviewed, common independent variables pertained to ICT availability and use at school and home, with the strength of relationship between these variables and student academic performance sometimes dependent on the student sample and year of study. To date, little research has focused on the role of student competence in, attitude towards, interest in, and autonomous use of ICT. Moreover, to date, many studies have focussed on examining the role of ICT using a single PISA (and other single cycle large-scale assessment datasets such as TIMSS) and by taking a limited number of covariates into account (Luu & Freeman, 24 ; Erdogdu & Erdogdu, 9 ; Meng et al., 21 ; Odell, Galovan, & Cutumisu, 34 ; for TIMSS and PIRLS, see, for example, Grilli et al., 9 ). It should be noted that the cross-sectional nature of the PISA surveys makes longitudinal research impossible: i.e., the same cohort of students are not tracked longitudinally across time. However, for each cycle, attempts are made to ensure that samples are representative of the student group of interest, 15-year-olds, and questions pertaining to ICT are repeated opening the possibility for reasonable comparisons to be made across administrations.

We could only identify one example of research that focussed on five cycles of PISA. Zhang and Liu ( 21 ) investigated the role of ICT use on student performances for PISA cycles spanning 2000 to 2012. Research based on multiple PISA cycles over time provides a more holistic approach to highlighting and identifying the general situation of ICT use and attitude and its role in student learning. Therefore, the current research focuses on the last decade on PISA administrations and makes use of all ICT-related variables. Therefore, this study aims to explore the relationship between (1) ICT use and ICT related attitudes and (2) students’ math and science abilities measured in all the PISA surveys within the last decade. Besides, this study accounts for a wide range of covariates while undertaking the analyses at the student, school, and country levels. This was done to adjust for the confounding of associations of variables possibly related to both ICT-related use and students’ math and science performance. To note, Zhang and Liu ( 21 ) analysed the PISA surveys between 2000 and 2012 with a similar research question. However, in the 2015 and 2018 PISA cycles, several essential variables were added to the ICT surveys. To this point, in their scoping review, Odell, Galovan, and Cutumisu ( 34 ) noted that ICT as a topic in social interactions, interest in ICT, and autonomy in using ICT—variables added to the ICT survey in the latest two cycles—have been less studied concepts in the relevant literature. The current study makes further use of data from these two more recent cycles with the intention to provide updated and more comprehensive insights into the role of ICT use on student academic performance. Accordingly, the following three research questions are proposed for the current study:

RQ1: Can reasonable comparisons between ICT-related variables and control variables be made year-to-year for PISA 2009, 2012, 2015, and 2018? If not, what type of variable transformations might be usefully be applied to ensure this?

RQ2: What proportion of the variance in Math and Science can be attributed to within-school, between-school, and between-country effects?

RQ3: While controlling for student-, school-, and country-level confounding factors, what forms of student ICT-related attitude, accessibility, and school ICT-related infrastructure are associated with student performance in PISA Science and Math across PISA cycles?

Methodology

Participants.

The data for the current study was compiled from the previous four PISA cycles, which were made available from the OECD website. PISA is an international survey that has been conducted every three years since 2000. PISA aims to assess 15-year-old students’ science, math, and reading achievement scores, their various attitudes, behaviors, demographics, and other relevant contextual data from their parents and schools. For each of the four cycles, 2009, 2012, 2015, and 2018, both student and school data were merged. Each country had the option to have students and schools complete questions that measured the student- and school-level utility of, familiarity with, and attitude toward ICT. Because this survey was not obligatory, different numbers of countries opted to be involved in the ICT survey year-to-year. Accounting for this missing data, and after removing schools with fewer than ten students (Lai & Kwok, 9 ), total student sample sizes across the four cycles amounted to 247,352, 243.060, 194,399, and 212,652, respectively. The total number of schools was 9,123, 9,923, 7,726, and 8,261, respectively, while the total number of countries was 44, 43, 45, and 49, respectively. On average, there were 27.1, 24.5, 25.2, and 25.7 students in each school, respectively; and an average of 207.3, 230.8, 171.7, and 168.6 schools were sampled from each country, respectively.

In this study, a series of three-level models were used to examine the relationship between ICT-related variables and students’ academic performance. The plausible values of students’ math and science achievement scores were used as dependent variables in the models. The control and independent variables used at the country, school, and student levels are described below.

Country-level variables

There are inequalities in computer and internet use between countries, and this has been found to be related to countries’ socio-economic characteristics (Montagnier & Wirthmann, 24 ). As a prominent indicator of a country’s socio-economic level, each country’s GDP per capita score was taken from World Bank ( 34 ) and included in the model as an independent variable at the country-level. Therefore, in the current study, GDP per capita was considered an important independent variable of interest.

School-level variables

School-level ICT development indices were used as independent variables and several educational variables related to school infrastructure were also included as control variables at the between-school level.

For school-level ICT development, we included the ratio of available computers per student at modal grade (RATCMP1 in 2015 and 2018; RATCMP15 in 2012; IRATCMP in 2009), and the proportion of available computers that are connected to the Internet (RATCMP2 in 2015 and 2018; COMPWEB in 2009 and 2012; 0 = no computers in school online, 1 = all computers in school online).

We included the following six control variables: (1) “Shortage of educational material” (EDUSHORT in 2018 and 2015), (2) “Quality of educational resources” (SCMATEDU in 2012 and 2009), (3) School-level economic, social, and cultural status (ESCS) (aggregated from students’ ESCS scores), (4) School type (SCHLTYPE; 1 = Private; 2 = Public), (5) Creative extra-curricular activities (EXCURACT in 2009; CREACTIV in 2012, 2015, 2018, and (6) Shortage of educational staff (STAFFSHORT in 2015 and 2018; TCSHORT in 2009 and 2012).

Student-level variables

Like at the school-level, multiple independent and control variables of interest were included in all models.

For control variables, students’ economic, social, and cultural status (ESCS) and gender (1 = female; 2 = male) were used. ESCS is a composite score computed by three indices (OECD, 24 ): home possessions including books at home (HOMEPOS), highest parental education (PARED), and highest parental occupation (HISEI).

The independent variables related to ICT use can be classified into three categories: ICT use outside school , ICT use in school , and students’ attitudes toward ICT .

ICT availability at home (ICTHOME in all cycles), ICT use outside of school [leisure] (ENTUSE in all cycles), use of ICT outside of school [for schoolwork activities] (HOMESCH in all cycles), subject-related ICT use outside of lessons (ICTOUTSIDE in only 2018 PISA), and ICT as a topic in social interaction (SOIAICT in only 2015 and 2018 cycles) were the variables related to “ ICT use outside school .”

ICT availability at school (ICTSCH in all cycles), use of ICT at school in general (USESCH in all cycles), and subject-related ICT use during lessons (ICTCLASS only in 2018 PISA) were the variables related to “ ICT use in school .”

Self-confidence in ICT high-level tasks (HIGHCONF only in 2009 PISA), attitude towards computers (ATTCOMP only in 2009 PISA), limitations of a computer as a tool for school learning (ICTATTNEG only in 2012 PISA), attitudes towards computer as a tool for school learning (ICTATTPOS only in 2012 PISA), interest in ICT (INTICT only in 2015 and 2018 cycles), perceived ICT competence (COMPICT only in 2015 and 2018 cycles), and perceived autonomy related to ICT use (AUTICT only in 2015 and 2018 cycles) were the variables pertaining to “ studies attitudes toward ICI ” Footnote 1

Data adjustments

Dichotomous variables were dummy coded as follows: school type (SCHLTYPE: private = 1, public = 2) and student gender (GENDER: female = 1, male = 2). The variance for (1) GDP per capita, (2) the ratio of computers to students (RATCMP1), ICT available at home (ICTHOME), and ICT available in school (ICTSCH) was not consistent across the four cycles. For this reason, these variables were each also standardized prior to MLM analyses (see Table 1 ). In addition, the variable specifying the proportion of computers connected to the Internet (COMPWEB: none = 0, all = 1) was highly negatively skewed each cycle, so normalization procedures were undertaken in accordance with Courtney and Chang ( 9 ) (see Table 1 for selected descriptive statistics) prior to analysis. Decisions concerning the centering of predictor variables were made in accordance with Brincks et al. ( 8 ) and Lüdtke et al., 21 ). Specifically, we group mean center variables at the individual or school level when (1) student perception of the school environment was measured (e.g., perceived ICT use and availability inside schools, and (2) in the special case when the predictor has been computed by averaging the responses for all cases in each group (herein, ESCS). Further, because the school-level variables, STAFFSHORT, SCMATEDU, and EDUSHORT pertain to school principal perception (likely bound by comparative in-country perceptions), country-mean centering was applied to these variables.

To note, it was decided that the coefficients reported in the final linear mixed-effects models would be unstandardized. This decision was made so that the size of the coefficients would reflect the commonly understood metric in PISA, i.e., with the mean of approximately 500 and SDs of 100. While this is not exactly the case (see Table 1 , means of all PVs), means and standard deviations are approximately the same. It should also be noted that the Supplementary Materials (Additional file 1 : Table A1) provide definitions for each of the variables included in the study.

Use of sample weights

To ensure that each of the participating countries made an equal contribution to the study and to make the results of the study more generalizable internationally, we decided to make use of “senate weights” for all models. Because of missing data, the resultant sum of all student senate weights did not reach 5000. Therefore, the student senate weights for each country were multiplied by a constant such that the resultant sum of all student senate weight for the respective country came to 5000. The constant for each country was estimated in accordance with Eq.  1 :

where N is the total number of students included in the final analyses for each country after accounting for missing data.

The analysis was undertaken with the assistance of the open-source software, R (R Core Team, 44 ). The means and standard deviations for all variables are reported based on the observed sample data. The null and linear mixed effects modes made use of the lme4 (linear mixed-effects) package (Bates et al., 4 ) and lmer function. Analyses accounted for the three-level hierarchical structure of the data with students nested schools and schools nested in countries. All multilevel modeling analyses incorporated normalized weights so that the contribution from each of the countries in the analysis could be considered equal, regardless of their population or sample size (for PISA 2009, W_FSTUWT; for 2012, SENWGT_STU; for 2015 and 2018, SENWT were used). This way results of the study could be considered applicable to all participating countries. For each cycle, an initial exploration of the intra-class correlations (ICCs) for students’ Math and Science was followed by analyses of the aforementioned country-, school-, and student-level variables as fixed effects.

In accordance with Wu ( 44 ), analyses for each year and associated subjects were run with all available plausible values (PV1-5 for 2009–2012, and PV1-10 for 2015–2018). After implementing optimization algorithms in accordance with Nash and Ravi ( 34 ) and Bates et al. ( 3 ), all models converged successfully. All models used the maximum likelihood (ML) estimation.

Based on these results, mean coefficients, t values, and p values for each year-subject combination were then calculated for the models for all four years. With the trend toward more strict assessments of statistical significance (Benjamin et al., 6 ), and the large sample sizes associated with the PISA studies, a threshold of p  < 0.001 and b  = 2.00 (unstandardized shift in achievement/scale scores) was deemed as substantive at the student and school levels, while a threshold of p  < 0.05 was deemed of interest at the country level. Given the inclusion of multiple control variables in the models, we set the minimum association at 2 scale score points, though recognize that other researcher may propose different substantive limits depending on their study.

RQ1 asks whether or not reasonable comparisons between ICT-related variables and control variables can be made year-to-year. Results suggest that, after standardizing the three variables, namely RATCMP1, ICTHOME, and ICTSCH, the variance in each variable does not change substantially year-to-year. Therefore, it is argued that reasonable comparisons can be made across the four administrations (see Table 1 ).

RQ2 asks what proportion of the variance in Math and Science can be attributed to within-school, between-school, and between-country effects. The null models were run for both math and science achievement scores, using the available plausible values for each analysis. This was done to examine the extent to which student achievement differed significantly between schools and countries. Table 2 shows the intercepts, residuals, and intraclass correlation coefficients (ICCs) at school and country levels. For Math, country-level ICCs were quite stable across all cycles ranging from 0.199 to 0.214, while school-level ICCs dropped from levels slightly higher than 0.300 to slightly higher than 0.200 for the latter two cycles. Similarly, for science, country-level ICCs were quite stable with values ranging from 0.155 to 0.183 across all cycles while school-level ICCs dropped from approximately 0.300 and 0.310 in the first two cycles to approximately 0.220 and 0.210 for the last two cycles.

RQ3 asks, what forms of student ICT-related attitude, accessibility, and school ICT-related infrastructure are associated with student performance in PISA Science and Math across PISA cycles. After establishing substantive school- and country-level effects in RQ2, a series of three-level linear mixed-effect models, inclusive of the independent variables at the student-, school-, and country-levels, were run. A review of the final models in Tables 3 and 4 reveal that the independent variables explained up to 9.5% of the residual variance at the student-level, 61.8% of the residual variance at the school-level, and 34.8% of the residual variance at the country-level variance.

At the country-level, the GDP per capita of the countries involved in the ICT survey only had a statistically significant relationship with math and science achievement in 2012 ( b  = 13.21, p  < 0.01; b  = 10.98, respectively, p  < 0.05).

For both mathematics and science performance, results revealed that the overall variance explained at the school level tended to increase in the last two cycles (2015 and 2018). For math, variance explained at the school level grew from 50.7% in 2009 to 60.9% in 2018. Similarly, for science, variance explained grew from 50.5% in 2009 to 61.8% in 2018, with the variance explained due to school type appearing to become more substantive for both subjects.

Results revealed that the overall variance explained in science performance, at the within-school (student) level, increased from 6.5% in 2009 to 9.5% in 2018. It appears that the inclusion of variables in 2015 and 2018 pertaining to students’ perceived interest, competence, and autonomy in ICT provided substantive explanatory power for science performance. However, in comparison, the level of variance explained for math performance remained more constant across PISA cycles.

Tables 3 and 4 reveal that the direction of the relationships between the variables and the coefficients at all three levels appear to be quite consistent over the four cycles for both math and science ability. At the student level, two covariates, ESCS, and gender have strong positive association with both math and science achievement across all cycles. The ESCS effect indicates that the economic, social, and cultural advantages have a substantial relationship with students’ math and science achievement levels. The results for the gender variable means that, with females as the reference group, males have higher academic performance consistently across all the cycles.

ICT availability both at home and at school, and ICT use both inside and outside school—no matter the purpose of the students; for general, leisure, for schoolwork activities, or social interaction—was virtually always associated with either neutral or lower math and science performance for all cycles (with the single exception being Science, 2018, “ICT use outside of school, leisure”). For student use of ICT outside of school, substantive associations ( b  > 2.00; p  < 0.001) were quite consistently negative across all cycles with no instances of substantive positive associations. Similarly, for ICT use inside school, relationships were generally negative or neutral with no substantive positive relationships for either students’ math or science performance.

Students’ positive attitudes and beliefs toward ICT use have a substantive positive relationship with both their math and science performance for all cycles. The findings indicate that the more successful students have higher self-confidence in ICT high-level tasks, have more positive attitudes towards computers, more strongly believe in the usefulness of computers as a tool for school learning, are more interested in ICT, and perceive themselves more competent and autonomous in ICT use. In 2009 PISA, self-confidence in ICT had the highest relationship ( b math  = 5.94, b science  = 6.44, p  < 0.001) followed by positive attitudes toward computers ( b math  = 5.35, b science  = 5.25, p  < 0.001).

In the 2012 cycle, “attitudes towards computers: limitations of the computer as a tool for school learning” had the largest ICT-related relationship ( b math  = -10.30, b science  = -11.82, p  < 0.001). The scale measured the degree to which students “think that using computers for learning is troublesome and using the internet resources as a learning tool is not useful and suitable”, and this variable appeared to be associated with lower math and science performance. Conversely, this result also somewhat suggested that those “who believe that computers and Internet are useful tools for school learning” have higher achievement scores (2012; b math  = 2.07, b science  = 4.26, p  < 0.001).

In the 2015 and 2018 cycles, students’ perceived autonomy had the strongest association with academic performance ( b math(2015)  = 9.43, b math(2018)  = 8.93, b science(2015)  = 11.90, b science(2018)  = 10.20, p  < 0.001), reflecting the changing nature of the current educational settings in the way that students are more inclined to exert influence over their learning environments in order to increase their knowledge and abilities (Pellegrino, 24 ). Autonomy was followed by students’ interest in ICT ( b math(2015)  = 2.82, b math(2018)  = 3.65, b science(2015)  = 3.72, b science(2018)  = 5.06, p  < 0.001) and their perceived ICT competence ( b math(2015)  = 2.30, b math(2018)  = 2.63, p  < 0.01; and b science(2015)  = 2.71, b science(2018)  = 3.86, p  < 0.001).

In terms of ICT infrastructure, the number of available computers per student in the school appeared to have no substantive association with math and science performance for any cycle. However, the proportion of available computers connected to the net appeared to have generally positive associations (see exception for 2015, Math) with math and science performance ( b math(2009)  = 3.85, p  < 0.001; b math(2015)  = − 1.22, p  < 0.05; b math(2018)  = 2.96, p  < 0.05; b science(2009)  = 5.39, b science(2018)  = 4.26, p  < 0.001).

Incidentally, and as expected, at the school level, ESCS maintained the most substantive confounding association with student math and science performance for all cycles with coefficients for math between 63.67 to 74.61 ( p  < 0.001) and coefficients for science ranging between 66.80 to 71.98 ( p  < 0.001). Also incidentally, it is noted that a schools’ level of provision of extra-curricular activities appears to have an substantive, consistent, and positive associations with student math and science performance for all cycles. Finally, parenthetically, after accounting for the role of school socio-economic advantage and provision of extra-curricular activities, counterintuitively, school designation as a public institution appears to afford an advantage.

This study aimed to explore the role of student engagement with ICT technologies and the role of school ICT infrastructure on students’ math and science abilities for the last four PISA cycles (2009, 2012, 2015, and 2018). The results of this study drew upon multiple ICT-related PISA variables to provide insights into the changing role of ICT infrastructure and behavior on students’ academic performances in mathematics and science. Although studies using the PISA data from different countries revealed different patterns of relationships between ICT related variables and students’ math and science performance (Odell, Galovan, & Cutumisu, 34 ), the current study provides an overall view, taking all the participating countries into account across all PISA cycles spanning the last decade.

Country- and school- level effects

At the country-level, GDP per capita of the countries involved in the ICT survey only had an association with math and science achievement scores in 2012. Although the country-level ICCs suggested substantial differences in science and math achievement in the current study, GDP could not consistently explain the achievement gap between countries. Another variable, such as the “national ICT development level” that was not included in this study, could have provided some explanatory power for the achievement gap between countries, as explored by Skryabin et al. ( 34 ). As the number of participating countries increase in international large-scale assessment studies, more extensive work could be undertaken in this area.

At the school levels, counterintuitively, the number of available computers per student appeared to have no substantive association with school-level math and science performance for any cycle. This result concurs with early PISA studies on the topic. For example, Fuch and Ludger ( 9 ) found that, after controlling for family background and general school infrastructure, the availability of computers at schools had no statistically significant association with student academic performance. The authors posit that the relationship between school access to computers and performance may be more U-shaped. Therefore, more specific research into possible non-linear relationship is certainly in order here. However, the proportion of available school computers connected to the internet did have an expected positive relationship for both math and science in 2009 and 2018. Therefore school connectivity may be important, though this is not conclusive. Certainly, further international research into the role of school internet speed and student accessibility to websites (not necessarily used for learning) beyond simple proportion of computers connected should be explored in the future so to provide more pertinent insight of the digital divide in schools internationally (for a discussion, see Valadez & Duran, 21 ).

Within-school effects of ICT use and availability

In this section the current findings associated with the student-level effects of ICT use both (1) outside of school lessons, and (2) in school are discussed in contrast with the research literature. For convenience, the discussion is provided in the order of fixed effects presented in the Tables 3 and 4 .

In terms of within-school effects, there is a negative association between ICT availability at home and students’ math and science performance, as supported by previous studies (Hu et al., 21 ; Juhaňák et al., 9 ; Tan & Hew, 24 ). While some studies found a positive association between these two variables (Delen & Bulut, 9 ; Papanastasiou et al., 21 ; Srijamdee & Pholphirul, 44 ), others such as Juhaňák et al. ( 9 ) suggested no association. Also to note is that Bulut and Cutumisu ( 10 ) found positive relations for Turkish students but no relations for Finnish students. Considered broadly, the results here call into question the utility of unlimited availability of ICT materials at home and the possibility of distractive effects. It appears that unrestrained home access may have substantive detrimental relationship with adolescent academic learning.

ICT use outside of school for entertainment is associated with lower math and science performance in the current study, which is in line with the previous research findings (Bulut & Cutumisu, 10 , for Finnish students; Petko et al., 34 ; Skryabin et al., 34 , for math only; Juhaňák et al., 9 ; Luu & Freeman, 24 ; Rodrigues & Biagi, 21 , high-intensity users; Kunina-Habenicht & Goldhammer, 24 ). Therefore, these findings in the current study support the idea that, the frequency of use of ICT for entertainment, though outside of school, can place students at a disadvantage academically when student performance is contrasted with counterparts inside schools.

ICT use outside of school for schoolwork activities is negatively associated with math and science achievement in the current study, corroborating the findings of the previous studies (Carrasco & Torrecilla, 9 ; Skryabin et al., 34 ; Rodrigues & Biagi, 21 , medium and high users; Kunina-Habenicht & Goldhammer, 24 ; Hu et al., 21 ; Petko et al., 34 , only for science; Juhaňák et al., 9 , only for science). The findings here are somewhat troublesome given that the focus here is students’ frequency of computer use at home for school-related purposes. While counter-intuitive, it may be that use of such devices may involve a higher potential for distraction for the study period—the potential for distraction for which adolescent students may not manage well. However, we note that these effects are generally quite small ( b math(2012)  = − 0.40, p  < 0.01; b sci(2009)  = − 5.69, p  < 0.001) so further research is needed on this topic.

ICT as a topic in social interaction is also negatively associated with student math and science performance, further confirming previous findings (Carrasco & Torrecilla, 9 ; Rodrigues & Biagi, 21 ; Skryabin et al., 34 ). This finding comes as no surprise given that the index reflects the level of ICT use for interpersonal communication.

Finally, in terms of ICT-use outside of school lessons, students subject-related ICT use outside of lessons, defined as the extent to which students use UCT for specific subject-related tasks was also negatively associated with academic performance. This pattern is also revealing and confronting as even student ICT use focused on school work appears to also have a detrimental association with academic performance.

At this juncture, we turn to the role of ICT use in school itself for the four PISA cycles.

Findings in this study also reveal a negative association between ICT availability at school and students’ math and science performance, as supported by research by Koğar ( 21 ). Therefore, overall, and for the age-group of interest, ICT availability at school, akin to that at home, may also have a prominent distracting effect. Therefore, consistent negative associations for home and school use for both math and science across all PISA cycles may reveal the need to manage and constrain adolescent engagement with ICT devices and content.

ICT use at school, both in general and subject-related use during lessons , was associated with lower math and science performance for all cycles, confirming the results of previous studies (Erdogdu & Erdogdu, 9 ; Hu et al., 21 ; Juhaňák et al., 9 ; Luu & Freeman, 24 ; Petko et al., 34 ; Skryabin et al., 34 ; Bulut & Cutumisu, 10 ; Kunina-Habenicht et al., 24 ). Given the results above pertaining to ICT use and availability at school, it is understandable that involvement in ICT tasks at school might also be disruptive to student learning and development. However, here, year-by-year confirmation that student subject-related use is also associated with poor academic performance is quite confronting. This suggests that the integration of ICT for classroom activities may be associated with more damage than good.

Odell, Cutumisu, & Gierl ( 21 ) concluded in their scoping review of the secondary analyses of the PISA data that moderate use of ICT, rather than high or no use of it, may be positively associated with students’ math and science performance. However, our research here points to the consistent finding that ICT availability both at home and at school and ICT use both inside and outside school may be distractive for most students, decreasing their achievement levels. Even if they make use of ICT at school for subject-related purposes, it might be distractive and reduce their academic performance in science and math, subjects requiring focus and concentration to improve (Hu et al., 21 ). One explanation for this may be provided by Kunina-Habenicht and Goldhammer ( 24 ) who argue that more frequent use of ICT at school can be linked with remedial purposes for lower-performing students. Rodrigues and Biagi’s ( 21 ) findings are supportive of this contention by pointing out that high performers in math and science are the ones who use ICT at lower levels inside and outside school while the low performers are the ones who use ICT from medium to high levels.

Within-school effects of attitudes toward ICT

The most significant finding of this study is related to the role of the more recently fielded attitudinal variables in 2015 and 2016. Students’ positive attitudes and beliefs toward ICT use have a substantive positive influence on both their math and science performance for all cycles. More specifically, self-confidence in ICT high-level tasks, positive attitudes toward computers, belief in the usefulness of computers and the Internet as a tool for school learning, interest in ICT, perceived ICT competence, and perceived autonomy in ICT use appear to have a positive influence on students’ math and science performance. Previous studies have also found that successful students in math and science have more positive attitudes toward computers (Petko et al., 34 ; Tourón et al., 34 ), are more confident in ICT use (Guzeller & Akin, 9 ; Luu & Freeman, 24 ), are more interested in ICT use (Christoph et al., 9 ; Hu et al., 21 ; Meng et al., 21 ; Koğar, 21 ; Kunina-Habenicht & Goldhammer, 24 ), and feel more competent (Hu et al., 21 ; Koğar, 21 ; Kunina-Habenicht & Goldhammer, 24 ; Luu & Freeman, 24 ; Papanastasiou et al., 21 ; Srijamdee & Pholphirul, 44 ) and autonomous in using ICT (Hu et al., 21 ; Juhaňák et al., 9 ; Kunina-Habenicht & Goldhammer, 24 ; Meng et al., 21 ).

The findings of this study corroborate the assumptions of the self-determination theory and the ICT engagement concept (Deci & Ryan, 9 ; Goldhammer et al., 9 ), suggesting that academically successful students have a higher content-specific inner motivation related to ICT (ICT interest), more positive beliefs about their ICT knowledge and skills (ICT competence), and a feeling of self-directedness and control in ICT-related activities (autonomy). Given these relationships, it may be that there exists a cluster of student attributes associated with positive beliefs and attitudes around learning in ICT and in general. More work could be done to explore this. It should also be noted that the current findings posit that student enjoyment of social interaction around ICT has a negative influence on students’ math and science performance, confirming the findings of the previous studies (Hu et al., 21 ; Juhaňák et al., 9 ; Kunina-Habenicht & Goldhammer, 24 ; Meng et al., 21 ). In addition, it may be that lower performing students use ICT more often for social interaction to solve their school-related problems, such as requesting help from others instead of searching for written information, as was proposed by Kunina-Habenicht and Goldhammer ( 24 ).

Incidental findings

While students’ math and science performance in public schools was found to be lower than that in private schools, after controlling for the role of ESCS and the provision of extracurricular activities, school designation as a public institution appears to offer an advantage (Tourón et al., 34 ). This was a surprising result as it appears to be counter-intuitive. However, Zhang and Liu ( 21 ) findings also confirm the same pattern after controlling for ESCS. This finding appears to extend previous research that found no statistically significant relationship between private schooling and student performance in Australia (Nghiem et al., 21 ). Evidence in the current study of a consistent and growing reverse relationship (i.e., public school advantage, ceteris paribus ) for the past decade in PISA. On a speculative note, this pattern may be associated with the general, and perhaps inefficient, trend toward school privatization and socio-economic segregation (Lam et al., 21 ; Valenzuela et al., 24 ; Willms, 57 ). Finally, the provision of extra-curricular activities can be a critical complement to science and math performance that appears to consistently raise the learning bar and possibly ameliorate the role of socio-economic disadvantages (Willms, 57 ). Finally, our study adds to the growing body of literature on the role of gender for math and science performance. We note that boys tend to have a moderate advantage for math and more slight advantage for science, ceteris paribus.

The results of this study imply that the most substantial ICT-related predictor of students’ are an appropriate set of positive attitudes, competencies, and skills. In other words, the intensity or the quantity of ICT use itself may not make a difference, and the students may not realize the expected benefits if they do not use the ICT purposefully and consciously. These results are in line with the previous research findings suggesting that the quality of the ICT use is more predictive of students’ academic outcomes than the quantity (Lee & Wu, 24 ; Lei, 9 ; Petko et al., 34 ). Since ICT availability and ICT use have varying influences on students’ academic performance, educators and parents are recommended to be extra cautious in using ICT both inside and outside school. It can be helpful for educational leaders, teachers, and parents to invest more time in developing strategies for the students to effectively use educational technologies as a learning tool and to refrain from their distractive effects. The results also imply the importance of students’ positive attitudes and beliefs toward ICT and their interest in ICT for their math and science performance. Based on these results, teachers and parents are advised to nurture students’ positive attitudes and beliefs toward ICT to supplement learning and empower them to be self-competent and autonomous learners in order to improve their learning.

There are several limitations concerning the data used in this study, and they have implications for future studies. The cross-sectional nature of the PISA data set does not allow us to make direct causal inferences from the findings; instead, we intended to explore the associations between the selected variables. Other researchers can use experimental or longitudinal designs to better explore cause-and-effect relationships between those variables. The self-reported nature of the PISA data used in this study poses a methodological limitation that might provide an exaggerated or biased approximation of the ICT related attitudes and perceptions, and this might not give an accurate estimation of the ICT use. Other researchers can use different research designs and datasets that provide a more precise delineation of the ICT use and other ICT-related variables. Another limitation is that the complete set of items in the ICT questionnaire varied between different PISA cycles. It maybe that the items do not represent ICT-related behaviour in a comprehensive way in order to cover all aspects of the ICT related perceptions and attitudes or ICT use. In addition, all the results need to be interpreted in the context of the current research design (i.e., inclusion of specific country, school, and student-related variables). Therefore, researchers can use other data sets covering other ICT related variables such as teachers’ and parents’ perceptions and attitudes towards educational technologies, teacher support, or parental support in ICT use. Finally, future research that explores student accessibility to and attitude toward ICT during and after the recent schooling restrictions (associated with the pandemic) will also shed light on this field.

Availability of data and materials

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

All wording and meaning for all student- and school-level variables were equivalent across cycles. Table 1 provides further details.

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Dr. Matthew Courtney is an Associate Professor of Educational Assessment and Student Achievement. He has a broad interest in student and teacher development and enjoys applying quantitative methods to answer questions about education and learning. Dr Courtney has publications in peer-reviewed journals in the fields of assessment, higher education, cyber behavior and psychology, youth academic engagement, and quantitative research methods. He has developed extensive skills and experience in the application of IRT, multilevel modelling, VAM, and SEM models.

Dr. Mehmet Karakus is currently working as an Assistant Professor at the Research Centre for Global Learning, Coventry University, UK. Prior to that he worked at the Department of Higher Education, Graduate School of Education, Nazarbayev University, Kazakhstan. His main research interests are emotions in educational leadership, teacher psychology, equity and equality in education, quantitative methodology, multivariate analyses, and structural equation modeling in educational research.

Dr Ersozlu is a lecturer in Education (Maths Education) in the Faculty of Arts and Education. Dr Ersozlu’s research focuses on self-regulated learning (SRL), metacognition, reflective thinking, practice and teacher education. Her published papers have examined student’s metacognitive and cognitive strategy usages, how student’s metacognitive skills relate to their reflective thinking levels, the student teachers’ SRL behaviours, the problems in teacher education training, and self-regulated studying strategy used in instrumental learning.

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Courtney, M., Karakus, M., Ersozlu, Z. et al. The influence of ICT use and related attitudes on students’ math and science performance: multilevel analyses of the last decade’s PISA surveys. Large-scale Assess Educ 10 , 8 (2022). https://doi.org/10.1186/s40536-022-00128-6

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Enhancing the roles of information and communication technologies in doctoral research processes

• Sarah J. Stein   ORCID: orcid.org/0000-0003-0024-1675 1 &
• Kwong Nui Sim 2  

International Journal of Educational Technology in Higher Education volume  17 , Article number:  34 ( 2020 ) Cite this article

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While information and communication technologies (ICT) are prominent in educational practices at most levels of formal learning, there is relatively little known about the skills and understandings that underlie their effective and efficient use in research higher degree settings. This project aimed to identify doctoral supervisors’ and students’ perceptions of their roles in using ICT. Data were gathered through participative drawing and individual discussion sessions. Participants included 11 students and two supervisors from two New Zealand universities. Focus of the thematic analysis was on the views expressed by students about their ideas, practices and beliefs, in relation to their drawings. The major finding was that individuals hold assumptions and expectations about ICT and their use; they make judgements and take action based on those expectations and assumptions. Knowing about ICT and knowing about research processes separately form only part of the work of doctoral study. Just as supervision cannot be considered independently of the research project and the student involved, ICT skills and the use of ICT cannot be considered in the absence of the people and the project. What is more important in terms of facilitating the doctoral research process is students getting their “flow” right. This indicates a need to provide explicit support to enable students to embed ICT within their own research processes.

Background/context

Information and communication technologies (ICT) can bring either joy or challenge to well-versed academic practices, and either create barriers to learning and development or be the answer to needs. While some grasp and pursue opportunities to make use of various ICT for study, research and teaching, others struggle. Despite documented and anecdotal positive urges to adopt ICT to increase and improve efficiency and effectiveness, staff and students struggle experience ICT as needless and difficult-to-use interruptions. There is often little need seen to change practices by introducing ICT into ways of working. Exploring these views and experiences was the focus of this project. Being empathetic to views such as those expressed by Castañeda and Selwyn ( 2018 ), we did not approach this investigation from a position that assumes that ICT are natural and needed solutions to problems related to improving and facilitating effective learning, teaching and research. Rather, we took a more neutral stance, wishing to explore the experiences of those involved, namely, students and staff, through discussion with them about their ICT practices and views, and with a specific focus on doctoral study and supervision.

Doctoral supervision and the role, place and nature of the doctorate are receiving increasing attention in higher education research literature. A wide range of topics have been covered from, for example, the importance and types of support for students throughout candidature (e.g., Zhou & Okahana, 2019 ); to the teaching and supervision aspects of doctoral supervision (e.g., Åkerlind & McAlpine, 2017 ; Cotterall, 2011 ; Lee, 2008 ).

With advancements in, accessibility to, and development of, ICT within education settings has come a plethora of research into online and blended learning. These studies often highlight the capacity of ICT for facilitating teaching, learning and administrative activity within educational institutions and systems (e.g., Marshall & Shepherd, 2016 ). They cover numerous areas of importance from theoretical, practical, and philosophical angles and include the perspectives and needs of learners, educators and institutions (e.g., Nichols, Anderson, Campbell, & Thompson, 2014 ).

There are also studies on student use of ICT, though not necessarily doctoral students, and these cover a wide range of topics including specific ICT skills (e.g., Stensaker, Maassen, Borgan, Oftebro, & Karseth, 2007 ). Where postgraduate research students are concerned, some studies on ICT skill development and support provide some insights about students (e.g., Dowling & Wilson, 2017 ), and institutional ICT systems (Aghaee et al., 2016 ).

Notable about the many of these studies cited above is the use of self-reporting tools as mechanisms for gathering data about student use and views about ICT. While self-reports are valuable ways to collect such data about self-efficacy, they do have limits. In online learning environments, the role of self-efficacy, for example, is still being contested. It has been argued that learners from a variety of disciplines and learning settings will tend to overestimate claims about their performance and/or knowledge and skills (e.g., Mahmood, 2016 ).

All these studies help to ‘map the territory’ of ICT, their use at individual and institutional levels and related practices. Much advice and guidance can be gleaned from the literature as well, although relatively little for the specific integration of ICT within the doctoral research and supervision environment. Based on the literature that is available though, all indications are that (doctoral) students adopt educational practices incorporating limited ICT use, even though the use of ICT has grown enormously in the last 10 to 20 years. With the current interest in ensuring success of students and completion of doctoral degrees being closely related to high quality supervision, there is a need to improve supervision practices and within that, advance understandings about how to support students in their use of ICT for their doctoral research.

This project

This project aimed to explore doctoral student and supervisor views and use of ICT within the doctoral process. The intention was to bring to light perceptions that could give clues as to how to make practical modifications to the content and scope of professional development support for supervisors and students, in order to help them to make best use of ICT. In addition, consideration was given to the way data would be collected to ensure that more than just the self-reported perspectives of the participants were included.

An interpretivist research approach (Erickson, 2012 ) framed this study to support a focus on understanding the world from the perspectives of those who live it. Thus, the approach was well-suited to exploring perceptions about the use of ICT in our context.

Thus, this study did not commence with any hypotheses related to the influence of ICT in doctoral research in mind. Instead, as the interpretive frame of the research implies, this study investigated ways in which participants expressed their experiences of engaging and integrating ICT in support of their doctoral research processes. The data tapped into the participants’ (PhD students and doctoral supervisors) perspectives, as they expressed them. The research approach thus defined and shaped all aspects of the data gathering, analyses and presentation. In this way, alignment was ensured among the ontological, epistemological and practical implementation of the research project.

The study took place in two New Zealand universities where participants were either employees or students. Both universities are research-intensive, with histories of producing high-level research across many disciplines. Both institutions have clear and well-formulated policies and practices governing doctoral study - PhD and professional doctorate - and these include supporting that study through supervision. A specialised unit in each institution manages the administration of the doctoral degree. Couching “supervision” as essentially a (specialised) teaching activity, each unit also provides or coordinates professional development for staff in the art of supervision, and for students in the skills and processes of undertaking doctoral degree study.

Participants

Participants included doctoral students and supervisors from the two universities. As a result of an invitation to all students and supervisors, in total, 11 students and two supervisors responded. The students were PhD students at varying levels of completion. There was a mix of part time and full-time students from a variety of discipline backgrounds including health sciences, sciences, commerce and humanities. The supervisors were experienced and were from humanities and sciences.

Data sources

Data were collected using a 3-tier participative drawing process (Wetton & McWhirter, 1998 ). This strategy involved a series of two or three interview/discussions, along with participant-made drawings, which formed the focus of the interview/discussions.

This strategy generated two sources of data - interview transcripts and participant drawings – and involved the following (3-tier) phases:

Initial semi-structured interview/discussion to ascertain information about participants’ backgrounds and other details they saw relevant to share. In addition, they were asked about their use of ICT generally as well as within the doctoral process. It was a chance for the researchers to gain some understanding of participants’ views and practices in relation to ICT and their doctoral/supervision journeys.

Participant drawing . The participants were asked to make a drawing in their own time and before the second interview/discussion. Guidelines for the drawing suggested that they think of a way to illustrate their research process first, then to add onto the drawing any ICT (such as devices, websites, programmes, applications) that they make use of in the process.

Follow-up interview/discussion . During this phase, each participant was asked to explain the drawing’s features and how it made sense in terms of the project he or she was undertaking. This included discussion about how their supervision was working, how they worked with supervisors, and how the ICT they had included in the drawing worked within the process. They were also asked about elements that were not in the drawing, for example, certain ICT or activities that might have appeared in a typical account of a doctoral research process but were not included.

All interview/discussions were audio recorded and transcriptions of the recordings were returned to the participants for checking. The drawings were scanned and stored electronically.

In line with the interpretive approach that framed and governed our study, the data were analysed shortly after being gathered. Analysis of the data contributed to the development of ideas about participants’ perceptions, and these were refined progressively across the instances that researchers met with participants. Perceptions were thus checked, rechecked and refined against each data set.

This iterative and inductive approach (Thomas, 2006 ) involved thematic analysis (Silverman, 2001 ) and the capture of major and common ideas (Mayring, 2000 ) expressed by participants about how ICT are perceived and used in doctoral research processes. This approach helped to operationalise a process of co-construction between researchers and participants. Through checking, rechecking, refining and confirming, the researchers were able to articulate their understanding of participant perceptions that matched participants’ expressed thoughts.

The outcome of the analysis process was four assertions concerning ways the students perceived and understood ICT within doctoral study. Because there were only two supervisor participants, the data from the supervisors served to support the assertions we were more confidently able to make about student perceptions.

Research approach, quality assurance conditions and context

Despite the (what might be argued, small) number of volunteer participants who showed interest in, and committed themselves to, this study (i.e., no drop-outs or selection being made from a pool), it is worth noting that the researchers worked with each participant over an extended period of time (prolonged engagement), focused on investigating and gathering identifiable, as well as documentable, aspects of the participants’ ICT understandings and practices (persistent observation), and employed analysis techniques that incorporated peer debriefing, member checking, and fair presentation of assertions (Guba & Lincoln, 1989 ).

The aim was to unlock and identify views of reality held by the participants. The empirical evidence was used to help develop commentary and critique of the phenomenon which was the focus of the study (i.e., ICT use), including what the phenomenon is and how it occurs/is enacted/revealed in a particular context (viz., in doctoral research). This was, therefore, a different kind of study from one that might commence with a hypothesis, which would be concerned more with objectivity, explanation and testable propositions. In short, the methods employed in the current study fitted the intention to solve a “puzzle” about a phenomenon in relation to a particular context.

As this study involved human participants, ethical approval was gained through the institutional processes. This approval (University of Otago Human Ethics Committee reference number D17/414 and Victoria University of Wellington, Ethics Committee reference number 0000023415) enabled data collection methods described in the previous section to be carried out for any doctoral students and supervisors who volunteered to participate in this study. Ethical consent, use and care of the data as well as the ethical treatment of students and staff as participants were integral to the research design, planning and implementation of the whole study.

Findings and discussion

The four assertions are now presented. Each assertion is described and quotations from the interview/discussions along with examples of drawings from the student participants are used to illustrate aspects of each assertion.

Assertion 1: ICT are impartial tools; it does not matter how ICT are used, because the endpoint, that is, thesis completion, is the justification. ICT and people are separate and separated entities.

Students talked about how they worked on their thesis document and on the process of the study they were undertaking. Comments focused on various ICT being used and often on skills needed in order to use them. Some students expressed the view that ICT were tools, separate from the project and the person involved, to be used to achieve an endpoint. For example,

So long as it's formatted – it shouldn't matter - that's their [editors’] responsibility, not mine.

There’s probably a bit more about Zoom [web conferencing application] I could learn but again for me unless it’s a problem, I’m not going to go looking for it… not just for the sake of it at the moment.

Motivation to achieve an outcome was a focus of comments that support this assertion. For many participants, the aim to complete the study and write a thesis was, naturally, a large driver for how they were managing their study. Time was precious, and they would do what they had to do to reach their goal. To be motivated to learn about a new ICT, there needed to be a purpose that sharply focussed on achieving that end.

If the technologies are suddenly not available] I’m happy to sit down with a typewriter and learn it… If I’m not driven, I won’t bother.

This focus is illustrated in Fig.  1 . The drawing shows clearly identified components that make up major elements within the stages of producing the research for the thesis. ICT are listed in relation to those components.

ICT and people are separate and separated entities

Supervisors too, tended to focus on thesis production rather than on the process of producing a thesis that includes the use of ICT (i.e., as opposed to their very clear and explicit focus on the research process). An example illustrating this is:

Generally, people think the standard of the people getting or earning a PhD is that this person should be an independent researcher. [But no] After all, we only examine a particular thesis [and] there are lots of inputs from supports and supervision from supervisors.

In summary, this assertion focusses strongly on the experience of doctoral study being about getting the project done within a research journey that gives minimal regard to the affordances of ICT. ICT are framed as necessary but also fraught, especially due to the effort and time that draw attention away from the primary goal.

Assertion 2: ICT are tools or mechanisms that prompt active thought on practices with respect to planning and managing thesis writing and project execution. ICT and individuals work alongside each other.

Views that expressed notions of there being a close interactive relationship between students and ICT came through in several of the discussions with the participants. The focus on achieving goals and endpoints was strong, but the expression of how to achieve those goals, capitalising upon the affordances that ICT present, was different from the way views were expressed in relation to Assertion 1.

On a simple level, this student describes the checking he did when weighing up the merits of a piece of software to meet his needs.

I normally do a trial version… have a play with it. And if I think they are useful then I might try it on a project. And if then I feel it’s definitely worth investing… then I’ll go buy it.

Others simply liked to explore, to see whether there was potential in any ICT they encountered, as in,

Sometimes I just like playing with stuff to see what they can do and then if they tick my boxes then I keep them and if they don't, I move on. So it's more kind of ‘search and discover’ than kind of looking for something, you know.

Describing a deeper level of activity, a degree of critique and active reflection were indicated by another student when he said,

…we tried an electronic version of putting together a programme for a New Zealand conference and I was surprised how long it took us. Whereas in the past I’ve worked with [colleagues] and we’ve just moved pieces of paper around on the floor for abstracts and we were done really quickly.

These sentiments are well-captured in Fig.  2 . Here, the focus is on experimenting with ICT rather than the research process. The process of working things out to suit the individual is foregrounded.

ICT and individuals work alongside each other

Whereas Assertion 1-type expressions presented effort in a generally negative light, Assertion 2-type expressions couched effort as an assumed part of learning something new. There was a sense expressed in comments that there will be a way to manage the “problem” to be solved, which then generated the necessary motivation to engage effort. For example,

You just know what you know when you start off; when you're unsure about what you need to do. There's a bit of a barrier in front of you. It feels a bit intimidating and overwhelming, and then you get into it and it just works. And you just kind of put all the pieces together and get something out at the end.

There was a sense that supervisors’ perspectives of ICT might support this assertion too. For instance,

[ICT are] integral to everything now – there's no such thing as doing it without [them] anymore – these are the tools with which we do all the things we do.

In summary, this assertion captures the views of students who engage actively in making decisions about which, how and why they incorporate ICT into doctoral research practices.

Assertion 3: Knowing about ICT is only part of the thinking; what is more important is getting the “flow” right. ICT and the individual are in a complementary partnership.

Perhaps prompted by the nature of the drawing task, which was to illustrate how ICT fitted within the whole process of doctoral study, several students described the challenges to bringing everything together into one process made up of many parts, sections and subsections. One participant focussed on her “workflow” in order to manage the multiple documents, tasks and schedule involved in her doctoral research journey.

What systems do I use, what's my workflow? So, I actually spent some weeks looking at … ideas from other PhD students about their workflows and how they manage it.

Similar to Assertion 2-type comments, ‘getting one’s flow right’ involved exploration and an amount of reflective decision-making. For example,

So I did a play around with that [ICT] and found it was quite useful … So I’m trying to be quite disciplined about when I’ve got a document, entering it at the time, reading an article, throw in heaps of tags rather than not …And I simply keep a note, cross referencing to the actual articles. I like to have the articles and for some key ones I like to make a note. So, if it’s a seminal paper that I know I’ll be referring back to.

Thus, students talked about how hard they worked to set up routines and processes to enable them to manage time and their research projects. As in the above excerpts, they referred to categorising documents, searching for resources, undertaking analysis, managing data, and producing the thesis itself.

In working out one’s system or flow, this student highlighted the need to know about the affordances of ICT and how others had made use of them.

…you do need to know a bit about each of the individual … capabilities of the different systems to know what's even possible… but alongside that you're kind of reading other people's ideas of how they did it, and you think that bit might work for me oh, but that bit won't… so then you can kind of mix and match a bit.

The drawing in Fig.  3 highlights the “flow”. Absent of all words, this illustration draws attention to the movement of ideas, thoughts, processes and actions, from a number of different points but all ultimately converging or contributing to the one path.

ICT and the individual are in a complementary partnership

There was a hint that at least one of the supervisors saw the need for a workflow in this same vein: “So long as [the students are] happy with what they’re using – they should use ‘a’ system,”

In summary, this assertion highlights that what is important with respect to ICT and the doctoral process is how it all comes together within one’s flow. That flow incorporates active effort on the part of the individual in finding ICT and practices that suit the individual’s approaches as well as their project demands.

Assertion 4: ICT are not neutral; there is a two-way interaction between technologies as artefacts and the use of them to achieve ends. ICT and the person are intricately linked through multiple active, practical, goal-oriented connections.

This assertion draws attention to the nature of technology as a phenomenon; that technology is not an impartial tool that has no influence on the way humans act and react. This assertion presents ICT as an artefact of technological design activity; as a source of improving efforts to achieve an endpoint; but also as an influencer and even determiner of the thinking and practices of the person interacting with the ICT (e.g., Baird, 2002 ).

On what could be argued a superficial level, this student noted some active connection between the person and the software application, beyond simple use, when he commented:

I think it goes both ways, the product has to be intuitive and you’ve got to have a little bit of inclination to try out different things.

Others went beyond the superficial to describe more in-depth relationships between themselves and the ICT they were using. When discussing her use of software to help her manage her project and her time, this student talked about how the ICT she was using supported and enhanced her thinking.

Using the application] really changed the way I started to think about [my research]. I started to be less worried about the big overwhelming long term stuff that was out there and just think, okay, this week, what am I going to do this week, how am I going to be really efficient and targeted, and I think that really helped me.

Following is another example of how ICT helped solve a problem while simultaneously having an influence on behaviour; in this instance with organising notes, ideas and documents.

“… and it's the same with my note-taking because [the programme] that I use has a similar sort of functionality that it can search text that you've written but also search notes and PDF docs and those kind of things, so it means that when you've had a random thought and put it somewhere you can find it again. Which is huge for me, so I guess that … the power of the search engine is probably the thing that drove me to become paperless, so it helps me to organize myself much better. … filing paper is a skill that I have not mastered whereas filing digital stuff is not as important because you can always just find it again.

Figure  4 illustrates this intricately intertwined interactivity among person, purpose, project, ICT and outcomes.

ICT and the person are intricately linked through multiple active, practical, goal-oriented connections

While we did not find strong evidence for supervisors’ thoughts about this integrated and embedded notion of ICT, one supervisor did note “I could probably build them into my system, but I just never have”.

In summary, Assertion 4 highlights the integral role that ICT can be perceived to play in doctoral research processes. This is more than the working-alongside connection illustrated by Assertion 2 and the complementary partnership characterised by Assertion 3.

Assertions 1 and 2 highlight that individuals hold assumptions about, and have expectations of, ICT use; and those expectations and assumptions influence and determine their judgements about ICT and their use of ICT. The assertions point to connections between perceptions and practices. Assertion 1 describes a perception that ICT are separate from the person and the task-at-hand, while Assertion 2 presents a perception in which the person and the ICT are working alongside each other in harmony or at least in a loose partnership. Both assertions focus on endpoints, but the endpoints vary according to the perception of where ICT fit into the journey towards their achievement. For Assertion 1-type expressions, there is one major endpoint. For Assertion 2-type expressions, there are multiple, shorter-term endpoints that build towards achieving the major goal of completing the thesis.

Building on Assertions 1 and 2 are Assertions 3 and 4, which highlight what may be argued as more complex levels of perceiving and working with ICT. Both assertions give some focus to inter-connections, where people and ICT partner or collaborate. Assertion 3 depICT a perception that is about complementarity; where ICT affordances are seen as worthwhile when they support and enhance the work of the individual in ways that make sense to that individual. Assertion 4 builds on Assertion 3 by bringing to light the relationship in which the person alters and changes thinking or practices because of the influence that ICT affordances can have. No evidence was found to support a possible additional claim that as well as ICT causing individuals to alter and modify thinking and behaviours due to their existence, ICT, in turn, are perceived to be able to alter their ways of responding to the people who use them. This is not out of the realms of possibility of course, with ICT increasingly being designed and built to be able to respond to users’ needs.

It is also worth mentioning that the ‘types’ of ICT and the extent of their use by the participants was not the focus of this study. However, the findings suggested that the participants’ ICT use, regardless of their PhD phase and broad discipline background, might have reflected their inability to realise the advantages of learning how to use current ICT-related devices, tools, and applications to enhance the process of undertaking their doctoral research. The evidence that emerged in this study indicated that participants’ perspectives of ICT determined their adoption practices in general (i.e., as illustrated through the four assertions). The boarder higher education context including the specific institution and supervisors, might have neglected the explicit support of PhD students’ ICT capability development in this process.

In addition, while there is no similar study being found thus far, the insights gained from this study are actually similar to the findings in the research studies into the role of ICT in undergraduate education (Butson & Sim, 2013 ; Sim & Butson, 2013 , 2014 ). Results in those studies, demonstrated students’ low levels of ICT use, may be an indication that digital devices and digital tools do not play a significant role in daily study practices. Researchers such as Esposito, Sangrà & Maina ( 2013 ) also show that the PhD students’ learning to become researchers in the digital age is much more complex than is often suggested (e.g., the skills of Prenksy ( 2001 ) “digital natives”). Becoming a researcher involves developing a complex set of knowledge, intellectual abilities, techniques and professional standards. The Researcher Development Framework (Careers Research and Advisory Centre (CRAC), 2010 ) illustrates one useful attempt at mapping out that complexity. It could be that both students’ and supervisors’ adoption of ICT for academic purposes has been overshadowed or taken for granted as a consequence of their advanced academic level.

Implications

The four assertions can be used to provide some guidance to those supporting and participating in doctoral research processes. Students and supervisors do possess a vast array of skills, knowledge and abilities. They have a variety of experiences as well as varying reasons and levels of motivation. Their skills and capacity to make use of ICT to support their roles in the research process vary as well. The assertions that have emerged from this study will inform the planning for support activities to enhance supervisors’ and students’ professional development, whatever their background and needs.

Depending on the perceptions held about ICT and the relationship between ICT and the person in the context of the task and its goals (i.e., the doctoral study) within the doctoral research process as depicted in the four assertions, ICT tend to be seen as a challenge, a change or an opportunity. In the context of ICT use, doctoral students and supervisors may:

assume that if they do not already know how to use something it is not worth learning or exploring as that learning brings with it risk to quality, efficiency and effectiveness of the doctoral research process; and/or.

assume that students will work out the place that ICT play within the research process for themselves.

The findings of this study suggest the need to.

challenge existing ICT knowledge and skill, and to support acceptance of the need to change practices;

teach technological thinking, to enable choice and decision making about ICT;

embed ICT into practices in meaningful ways to suit individual and project needs;

highlight (explicit) responsibilities about thinking and planning skills with respect to making the best use of ICT, to ensure efficiency and effectiveness;

realise that the research process is as much about how it happens as what happens;

recast assumptions about the doctoral research process to embed ICT within it;

reflect on the meaning of effectiveness and efficiency in the context of doctoral research; and the effects of ICT in supporting and facilitating them;

understand that there is a link among ICT thinking and practice: using ICT can enhance or raise ideas that were never thought of before.

This study explored perceptions of doctoral supervisors and students of the role and place of ICT in supervision and study. It generated four assertions characterising those perceptions the relationships among people, ICT and the task-at-hand, that is, the supervised research process. As Castañeda and Selwyn ( 2018 ) argue, it is important that we have an active commitment to ‘think otherwise’ about how ICT might be better implemented across higher education settings” (p. 8). We should not assume that ICT are not important enough to let them fade into the background as they become normalised, without questioning the interrelationships that are happening between the person and the ICT. In the doctoral research setting, as one example of a higher education context, ICT do have a role to play. They cannot and should not be ignored. But seeing ICT in relationship to the person and to the setting is essential.

This project has provided insights into the doctoral students and supervisors’ perceptions of the roles played by ICT during doctoral research process. There are complex human factors, including assumptions, attitudes and conceptions about academic practices, influencing and determining perspectives as well as how ICT are incorporated into doctoral research process, behaviours and practices. Just as Kandiko and Kinchin ( 2012 ) argue that supervision cannot be looked at in the absence of the research work in which it occurs, we argue that doctoral students’ understanding and use of ICT cannot be considered independently of their research work; and that work includes relationships with their project, their supervisors, within the context of the institution, and with the ICT they do and could engage with.

Directly associated with the outcomes of this study, future studies and further exploration could focus on:

ICT use by larger and more diverse groups of doctoral students from a range of fields within discipline areas at institutions outside New Zealand;

building on the findings in order to determine how intensity of ICT use might change for students across the course of their candidature, and in relation to the nature of their research projects;

the role of supervisors, academic departments, and institutions in supporting and enhancing students’ practices and beliefs about ICT in research processes;

the ways in which supervisors engage ICT in their daily academic practices, with a view to exploring how, or if, their ICT use is an influence on PhD students’ beliefs and behaviours in using ICT.

Studying ICT in these directions could offer fresh perspectives and opportunities to think differently and reveal an active way of understanding the role of ICT in doctoral education.

Availability of data and materials

These are not available for open access as their access is bound by the ethical agreement approved by the two institutions and made with the participants in the study.

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Acknowledgements

We thank the students and supervisors who shared their reflections and willingly engaged with us in this project.

We acknowledge the support of Ako Aotearoa, The National Centre for Tertiary Teaching Excellence, New Zealand through its Regional Hub Project Fund (RHPF), and the support of our institutions, University of Otago and Victoria University of Wellington.

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Stein, S.J., Sim, K.N. Enhancing the roles of information and communication technologies in doctoral research processes. Int J Educ Technol High Educ 17 , 34 (2020). https://doi.org/10.1186/s41239-020-00212-3

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Digital competence is considered to be a crucial aspect of education, and the discourse around digital technologies has been full of promises of more and better learning. It has also been quite general and not taking into account subject specific differences in what digital competence may be across disciplines and grades. In this chapter, I will discuss why access to technology it not enough to digitalize education, and what kind of knowledge specific educational research we need to address digitalization in the classroom. I will draw on empirical data from two different projects to shed light on these questions: the large scale video study Linking Instruction and Student Achievement (LISA) and a national survey to parents in Norway about what characterized teaching when schools went 100% digital overnight in March 2020, due to the global outbreak of COVID-19. I will point to important implications of these studies for the field of didactical research and underscore the need for more studies looking systematically into what digital competence means within specific didactical contexts.

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Ict use, digital skills and students’ academic performance: exploring the digital divide.

1. Introduction

2. the impact of ict on student performance, 2.1. the effects of ict equipment on student performance, 2.1.1. the beneficial effects of university ict equipment on the average performance of students, 2.1.2. personal equipment as an explanatory factor for performance differentials and the digital divide, 2.2. students’ innovative and collaborative uses of ict improve their results, 2.3. impact of digital skills on student performance, 2.4. strategies for acquiring digital skills limited to the implementation of ict-specific training by universities, 3. research methodology, 3.1. sample and data collection, 3.2. defining the selected variables, 3.2.1. the dependent variable, 3.2.2. the variables of interest.

• The amount ICT equipment made available to students by universities;
• Students’ computer skills;
• Students’ Internet skills, i.e., level of skills to search, select and analyze large amounts of information in a meaningful way;
• The perceived usefulness of ICT-specific tools. Items positively correlated to this component reflect students’ beliefs about the performance and efficiency gains resulting from use of these tools;
• Innovative educational uses resulting from ICTs and developed by the student;
• The educational benefits of using remote working tools, including collaborative work enabled by the co-presence of students via asynchronous and synchronous collaborative communication tools;
• Creative uses enabled by ICT;
• The impact of using ICT-related tools on flexible working.

3.2.3. Control Variables

3.3. model specification.

• P i Y i = j is the probability that student i will achieve grade j ;
• φ ⋅ is the cumulative standard normal distribution function;
• μ j and μ j − 1 are the upper and lower threshold values for category j .

4.1. ICT Investments Have a Small Impact on Students’ Academic Success

4.2. innovative and collaborative uses of ict improve students’ results, 4.3. impact of digital skill levels on student performance, 4.4. ict-specific training does not improve student performance, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Ben Youssef, A.; Dahmani, M.; Ragni, L. ICT Use, Digital Skills and Students’ Academic Performance: Exploring the Digital Divide. Information 2022 , 13 , 129. https://doi.org/10.3390/info13030129

Ben Youssef A, Dahmani M, Ragni L. ICT Use, Digital Skills and Students’ Academic Performance: Exploring the Digital Divide. Information . 2022; 13(3):129. https://doi.org/10.3390/info13030129

Ben Youssef, Adel, Mounir Dahmani, and Ludovic Ragni. 2022. "ICT Use, Digital Skills and Students’ Academic Performance: Exploring the Digital Divide" Information 13, no. 3: 129. https://doi.org/10.3390/info13030129

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A Study of the Impact of the ICT Strand As Part of Tech-VocStrand of Senior High School Students of Southeast Asian College Inc. In partial fulfillment for the requirements in Practical Research II Researchers

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