A meta-analysis on the effect of technology on the achievement of less advantaged students

A meta-analysis on the effect of technology on the achievement of less advantaged students ☆,☆☆ Giorgio Di Pietro a,b,1,* , Jonatan Casta˜ no Mu˜ noz c a European CommissionJoint Research Centre, Edificio Expo, Calle Inca Garcilaso, 3, 41092, Seville, Spain b Institute of Labour Economics (IZA), Schaumburg-Lippe-Straße 5-9, 53113, Bonn, Germany c University of Seville, Department of Teaching and Educational Organization, Faculty of Education, C/ Pirotecnia, S/N, 41013, Seville, Spain ARTICLE INFO Keywords: Less advantaged students Student achievement Educational technology Meta-analysis ABSTRACT This paper presents a meta-analysis that investigates the impact that the educational use of digital technologies has on less advantaged students’ achievement. We use a comprehensive definition for this group of students that includes all students in less developed countries as well as more disadvantaged students in more developed countries. 740 estimates from 72 studies employing experimental and quasi-experimental research designs are collected. Overall, educational technology initiatives are found to have a small, positive, statistically significant effect that remains even after correcting for publication bias. Additionally, our results indicate that computerassisted learning and behavioural interventions are more effective in raising the achievement of less advantaged students than simple access to technology. Interestingly, the effect of these two interventions appears to be of a similar magnitude. Finally, the use of digital technologies is associated with slightly greater achievements in math and science than humanities. 1. Introduction The expansion of technology has affected many areas of our life, including education. Digital learning tools such as tablets, smartboards and online applications have become increasingly important elements of teaching and course delivery. There is a lot of evidence showing that the introduction of these tools can improve children’s teaching and learning experiences. McEwan (2015) argues that technology-based interventions may be as effective in raising student achievement as well-known and popular policies such as smaller class size, teacher training and performance incentives. Discussions exist in the literature concerning the relationship between digital technologies and equity in educational outcomes (Warschauer & Xu, 2018). On the one hand, there are concerns that more vulnerable students can miss out on the benefits that these technologies bring. The use of digital technologies may be less effective for low socio-economic status children as they tend to have ☆ The authors would like to thank three anonymous referees for their helpful and constructive comments. The usual disclaimer applies. ☆☆ Jonatan Casta˜ no Mu˜ noz acknowledges the support of the ‘Ram´ on y Cajal’ grant RYC 2020-030157 funded by MCIN (Ministerio de Ciencia, Innovaci´ on y Univesidades)/AEI (Ministerio de Ciencia, Innovaci´ on y Univesidades)/10.13039/501100011033 and by “ESF Investing in your future”, and the University of Seville "VI University research plan" (VI plan propio de investigaci´ on) for this work. * Corresponding author. European CommissionJoint Research Centre, Edificio Expo, Calle Inca Garcilaso, 3, 41092, Seville, Spain. E-mail addresses: [email protected] (G. Di Pietro), [email protected] (J. Casta˜ no Mu˜ noz). 1 The views expressed are purely those of the author and may not in any circumstances be regarded as stating an official position of the European Commission. Contents lists available at ScienceDirect Computers & Education journal homepage: www.elsevier.com/locate/compedu https://doi.org/10.1016/j.compedu.2024.105197 Received 1 May 2024; Received in revised form 2 November 2024; Accepted 11 November 2024 Computers & Education 226 (2025) 105197 Available online 13 November 2024 0360-1315/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). limited access to technological infrastructure (Hohlfeld, Ritzhaupt, Barron, & Kemkerhon, 2008), exhibit moderate levels of digital competence (Fraillon, Ainley, Schulz, Friedman, & Duckworth, 2019), can count on little external support for the use of these technologies (Reich, 2020), and are less likely to adopt a self-regulated learning strategy (Yang, Cheng, & Chen, 2018). On the other hand, however, other arguments suggest that the application of technology to the education sector has the potential to be very beneficial to more disadvantaged students. First, technology may enable personalised learning by tailoring educational experiences to individual students’ needs and abilities. This may be advantageous especially for students who need extra help (e.g., students with learning difficulties), ensuring that they receive the support that is right to them and empowering them to choose when and how they learn (Ogan et al., 2012; Wagner, 2016). Second, the incorporation of technologies into the learning process may increase student engagement. Interactive and multimedia-rich digital resources (that include, for instance, games, simulation, quizzes) may make learning enjoyable and exciting, doing a better job in capturing students’ attention than traditional classroom settings. Students lacking motivations, who are more likely to come from less advantaged backgrounds (Fejes, 2012), may particularly benefit from this learning approach. Third, technology has broken down physical barriers, enabling learners to have access to high-quality educational resources irrespective of their geographical location (e.g., through online learning platforms, video conferencing tools). This is especially relevant for individuals from remote and rural areas who tend to face significant more challenges in obtaining learning materials than those from urban areas. Given the growing importance of technologies in education, one needs to gain a better understanding of their role in enhancing the achievement of all students. It is important to know that the development and diffusion of digital technologies is not leaving behind those from more vulnerable groups. The purpose of this review is to analyse evidence from rigorous evaluations on the effect of educational technology (ed-tech) interventions on the academic performance of less advantaged students. 1.1. Prior reviews on technologies and the achievement of less advantaged students Earlier reviews attempting to summarise existing evidence on the impact of digital technologies on the achievement of less advantaged students have followed two different approaches. First, a few meta-analyses and systematic reviews have investigated how ed-tech interventions affect the learning outcomes of students in disadvantaged contexts such as less developed countries. Some of these studies have focused their attention on the impact of a specific set of technologies. For instance, Major, Francis, and Tsapali (2021) have looked at the effect of technology-supported personalised learning on academic outcomes for school-aged learners in lowand middle-income countries. They found that these interventions have a statistically significant positive effect on students’ learning, reporting an overall effect size of 0.18. Other studies have adopted a broader approach, considering all types of technologies. For example, Rodriguez-Segura (2022) has attempted to synthetise the results of studies analysing the effect of any type of ed-tech intervention on student performance in less developed countries. He observes that, while access to technology interventions alone are not sufficient to enhance learning, interventions centred around self-led learning and improvements in instruction are the most promising ones. The second line of research consists in meta-analyses on the association between ed-tech and student achievement in which socioeconomic status is used as a moderator variable. For instance, Cheung and Slavin (2012 & 2013) consider K-12 students in the US and examine whether there any differences in the impact of technology on student performance in reading and math between students from high and low socio-economic status. No statistically significant differences across socio-economic status were found. 1.2. The current study The present meta-analysis investigates the impact of ed-tech interventions on academic outcomes among less advantaged students. We extend previous relevant work by employing a comprehensive definition for this group of students that comprises all students in less developed countries as well as more disadvantaged students in more developed countries. To the best of our knowledge, there is no meta-analysis examining the impact of digital technologies on the achievement of more disadvantaged students in more developed countries. This is important because, in contrast to earlier meta-analyses including socioeconomic status as a moderator variable and thereby employing more privileged students as a control group, we use similar students (i. e., students from more disadvantaged backgrounds not being exposed to the ed-tech intervention) as a comparison group. In addition to studies where the majority of the sample or the whole sample consist of more disadvantaged students in more developed countries, we also consider studies analysing students in less developed countries. This allows us to take a holistic approach for the more disadvantaged students. Analysing and comparing results from the aforementioned two groups of studies is indeed an important value added of our research as this may provide information on differential returns to investment in ed-tech in different parts of the world. In less developed countries ed-tech may help to address issues such as low supply of qualified teachers, teachers’ absenteeism, 2 scarce quality learning materials, and large student-toteacher ratios, 3 but it may also play an important role in more developed countries, for instance by enhancing the quality of education for students from rural areas, boosting students’ motivation, and personalising teaching practices (Escueta, Nickow, Oreopoulos, & Quan, 2020). 2 Chaudhury, Hammer, Kremer, Muralidharan, and Rogers (2006) observe that in less developed countries the proportion of absent teachers during unannounced visits is 19%. 3 For instance, according to the UNESCO database, while in 2018 the pupil-teacher ratio in primary education was 15.3 in OECD countries, the similar figure in the least developed countries (following the United Nations’ definition) was 37.2. G. Di Pietro and J. Casta˜ no Mu˜ noz Computers & Education 226 (2025) 105197 2 Since the terms “more developed” and “less developed” countries have been used loosely in the literature, it is important to provide a working definition of these terms. In this review, “more developed” countries refer to high-income or upper middle-income countries as defined by the World Bank (WB)’s classification of countries by income levels. “Less developed” countries refer to low-income or lower middle-income countries, again as defined by the WB’s classification of countries by income levels. Similarly, it is also important to provide a working definition of the expression “educational technology”. This refers to those digital tools and resources designed to deliver learning materials, support or enhance student achievement that complement, and not replace, in-person teaching (e.g., computer games, learning software, apps, text messages). Fully online courses as well as teacher-focused tools (e.g., learning analytics, AI for resource generation) are excluded. This meta-analysis seeks to address the following three research questions (RQ). RQ1. What is the overall impact of ed-tech interventions on the academic performance of this broader group of less advantaged students? RQ2. Is the impact of ed-tech interventions on achievement different between all students in less developed countries and more disadvantaged students in more developed countries? RQ3. What type/s of ed-tech interventions is/are more successful in raising the achievement of this broader group of less advantaged students? 2. Data and methods 2.1. Inclusion and exclusion criteria Table 1 shows the ten predefined inclusion and exclusion criteria developed and applied in the screening process. We chose to consider the period from 2000 onwards because digital education gained momentum at the start of the millennium. According to a KPMG report (Wildi-Yune & Cordero, 2015), the global e-learning market has massively grown since 2000. Following the recommendations by the Cochrane Statistical Methods Group, 4 studies have not been excluded purely based on the sample size 5 (Grainge, 2015). In addition to peer-reviewed journal articles and scholarly book chapters, we decided to consider also conference papers, reports and working papers. The rationale behind this is to have a balanced picture of available evidence given that the grey literature represents an important vehicle for disseminating studies with null or negative results that might not otherwise be disseminated (Paez, 2017). We only included studies presenting evidence from experimental or quasi-experimental research designs. These techniques, which test causal hypotheses (White & Sabarwal, 2014), provide the more rigorous evidence for the evaluation of ed-tech on student academic outcomes. We restricted our attention to studies focusing on primary (including kindergarten), lower and upper secondary education and using objective indicators to measure student academic outcomes. Achievement in all subjects (except for digital literacy 6 ) is considered. 2.2. Literature search Studies included in our meta-analysis are identified through three main steps: 1) electronic database search, 2) ancestry search across the studies selected at the end of the first step, and 3) ancestry search across previous relevant systematic reviews and metaanalyses. In the first step, following the recommendation that meta-analyses and systematic reviews should employ multiple bibliographic databases to search for relevant literature (Harari, Parola, Hartwell, & Riegelman, 2020), we used four of them (i.e., Web of Science, Scopus, Education Information Research Center (ERIC) and Google Scholar). Ewald et al. (2022) find that searching two or more databases reduces the risk of missing eligible studies and Hernandez, Marti, and Roman (2020) suggest that at least three search engines should be utilised. While ERIC is an education-focused database, Web of Science and Scopus are comprehensive general databases covering high-quality publications (Fan & Beh, 2023). Borrego and Froyd (2014) stress the importance of using general databases in addition to subject-specific ones when carrying out systematic reviews. The rationale for this is that research on a given topic of interest is often not only published in specialist journals or report series, but also in generalist journals or report series. Additionally, it is important to use Google Scholar as this database can be useful for finding grey literature (Haddaway, Collins, Coughlin, & Kirk, 2015). Appendix A sets out the clusters of keywords used to identify the studies to be included in this meta-analysis. These studies were found using keywords covering five different concepts: (1) the setting of interest (kindergarten, primary and secondary education), (2) 4 Its members were polled about whether it is appropriate for a Cochrane systematic review to exclude small studies. 26 out of 26 representatives voted against this proposal. 5 While some previously published meta-analyses (e.g., Zhang et al., 2020) do not consider studies with a sample size of less than 5, this exclusion criterion is also met in our case (in our meta-analysis the study by Aunio and Mononen (2018) has the lowest sample size, i.e., 22). 6 This is because digital competences are not always included among the basic skills expected to be learned by students. For instance, in PISA (Programme for International Student Assessment) basic skills are reading, mathematics and science. While reading in primary grades refers especially to vocabulary acquisition and text comprehension, in the intermediate and upper grade levels factual knowledge and understanding are progressively expanded and increasingly applied to operational use. G. Di Pietro and J. Casta˜ no Mu˜ noz Computers & Education 226 (2025) 105197 3 the methodology (experimental and quasi-experimental research designs), (3) the exposure (ed-tech interventions), (4) the outcome (student achievement), and (5) the socio-economic condition (more disadvantaged status). We employed Boolean terms ‘OR’ and ‘AND’ to combine searches within and between concepts, respectively. In ERIC, the search terms were searched in the ‘abstract’ and ‘descriptor’. As regards Google Scholar, we searched ‘with all of the keywords, anywhere in the article’ and focused our attention on the first 100 results of the search (Romanelli et al., 2021). In Scopus, the search terms were sought in the ‘title’, ‘abstract’ and ‘keywords’. In Web of Science, the search terms were sought only in the ‘abstract’. Our search, which ended in December 2023, delivered 141 hits. After removing duplicates (i.e., 5), the two authors, working independently, screened the titles and the abstracts of the studies. Following this, 105 items were excluded. Next, the full text of the remaining 31 studies was retrieved and carefully examined. This exercise was again conducted by both authors who independently classified the studies as relevant and irrelevant based on the predefined inclusion and exclusion criteria. While internal consistency was quite strong (Cohen’s Kappa was 0.88), studies on which there was disagreement were discussed in depth until consensus was reached. 11 studies were found as a result of this first step. In the second step, we screened the references sections of the 11 studies selected in the first step with the purpose of finding additional studies (Byron & Post, 2016). Employing the ancestry approach, 10 relevant studies were added. Finally, in the third step we expanded results from first two searches by reviewing the bibliographies of previous relevant systematic reviews and meta-analyses. 7 51 relevant studies were identified through this additional ancestry search. Since the number of relevant studies found through ancestry searches is significantly higher compared to the set of papers retrieved by the search in electronic databases, following the approach of Wilke and Pyka (2024), we performed some amendments to the list of keywords displayed in Appendix A. However, this did not lead to the inclusion of any additional relevant study, but only increased the number of irrelevant papers. The importance of using other sources of information in addition to electronic databases when conducting reviews on the topic of education and technology has been highlighted by Escueta et al. (2020) and Rodriguez-Segura (2022). Furthermore, in our case an additional difficulty lies in the identification of studies in more developed countries focusing on students with disadvantaged backgrounds. This is particularly challenging given that information about students’ socio-economic conditions is often not included in the studies’ titles, keywords and abstracts. Table 1 Inclusion and exclusion criteria. Criterion Included Excluded Language English Other languages, e.g. Spanish, German, French, Portuguese Data on an effect size or sufficient information to calculate it Data on an effect size (or sufficient information to compute it) and its standard error (or t-statistic, or p-value, or sufficient information to calculate it) Lack of data on an effect size (or insufficient information to compute it) or its standard error (or t-statistic, or p-value, or insufficient information to calculate it) Publication type Peer-reviewed journals, scholarly book chapters, conference papers, reports or working papers published between 2000 and 2023 Master’s and PhD dissertations as well as peer-reviewed journals, scholarly book chapters, conference papers, reports or working papers published earlier than 2000 and later than 2023 Measurement of student achievement Objective indicators including standardized test scores as well as scores from tests developed by teachers or researchers Achievements in digital literacy, student’s self-assessed grades, non-cognitive skills, and other outcomes (e.g., school attendance) Education context Primary (including kindergarten), lower and upper secondary education Higher education Research design Experimental (i.e., randomized controlled trials (RCTs)) or quasi-experimental (i.e., pre-test post-test study, regression discontinuity, instrumental variable, propensity score, difference in differences) research designs Non-experimental research designs Research setting Clear distinction between a treated group (exposure to ed-tech intervention) and a control group (no exposure to ed-tech intervention) Comparison of two alternative treatments within the treated group Disadvantaged context Evidence from: countries classified by the WB as highor upper-middle income countries at the time of the ed-tech intervention and where, following the approach of Dietrichson, Klint Jørgensen, and Filges (2017), at least 50% of the sample participants are from disadvantaged backgrounds defined in terms of parental occupation, education or income, access to free or reduced school meals, area of residence (e.g., rural or remote areas, low-income regions), migrant or minority status or countries classified by the WB as lowor lower-middle income countries at the time of the ed-tech intervention Evidence from countries classified by the WB as highor uppermiddle income countries at the time of the ed-tech intervention not focusing on more disadvantaged students or focusing on low-performing students or students with learning problems but with no information on their socio-economic background Definition of educational technology intervention Interventions explicitly aiming at improving student achievement through the use of technologies. They should complement in-person learning and not fully replacing it Interventions consisting of courses entirely delivered online Sample size Any sample size  7 These studies were selected among those excluded in the first and second steps. G. Di Pietro and J. Casta˜ no Mu˜ noz Computers & Education 226 (2025) 105197 4 A total of 72 studies was included in this meta-analysis. 8 The literature search and the screening procedure are summarised in Fig. 1 9 2.3. Study coding Before coding, the two authors created a coding manual that provides guidelines on how to consistently extract information about effect sizes and moderator variables from the studies included in the sample. Comments and suggestions on this coding manual were given by a researcher in the area of education and technology. The revised version of the coding manual was then tested by comparing the codes generated by the two authors for 5 randomly selected studies. Following some minor modifications, the two authors, working independently, performed the coding on the remaining 67 studies. The Kappa value between them was 0.83, indicating relatively good consistency. However, when disagreement arose, the studies in question were re-examined by both authors together until a final agreement was reached. 2.3.1. Effect size calculations In an attempt to compare the estimates of various ed-tech interventions on different academic outcomes, following the approach of similar earlier systematic reviews (e.g., Escueta et al., 2020) and meta-analyses (e.g., Ni, Cheung, & Shi, 2022), we used Cohen’s d. Although results from different studies are never fully comparable, Cohen’s d does offer valuable insights into the overall magnitude of impact across diverse programme contexts. Not only is Cohen’s d the most widely employed effect size to measure the magnitude of group differences (McCoach & Siegle, 2009), but it is also the most used among the studies in our sample that report effect sizes. In those studies where Cohen’s d values are not provided, it was possible to compute these using information therein contained. Cohen’s d was calculated by dividing the mean difference in performance between treatment (exposure to ed-tech intervention) and control conditions (no exposure to ed-tech intervention) by the pooled standard deviation (d =M1−M2 Sp; Sp= (n1−1)S2 1+(n2−1)S2 2 (n1−1)+(n2−1) √, with M 1 , M 2 , S p , n 1 , n 2 , S 1 , S 2 , denoting the means of the treatment group and control group, pooled standard deviation of both groups, the sample sizes of the treatment group and control group, and the standard deviation of the treatment group and control group). Additionally, if effect sizes are reported using Hedge’s g, these were converted into Cohen’s d statistics using the formula in Harrer, Cuijpers, Furukawa, and Ebert (2021) (d =g 1−(3 4(n1+n2−2)−1)⎞ ⎟ ⎟ ⎠. Finally, Cohen’s d was calculated from pre-test post-test designs employing the formula in Morris (2008). Cohen’s d standard error is also missing in a number of studies. A few strategies have been employed to address this situation. For example, if information on sample sizes is available, Cohen’s d standard error was calculated through the formula reported in Cooper and Hedges (1994). Where information on sample sizes is not included in the studies but exact p-values are instead reported, the formula provided by Higgins and Green (2011) was employed to calculate standard errors. 2.3.2. Moderator variables For each effect size, we coded several moderator variables 10 , that is, factors potentially influencing the size of the impact of ed-tech interventions on student achievement. a) Type of publication We distinguished between peer-reviewed journal articles and other studies. Publication type is a common moderator in metaanalysis. It is expected that peer-reviewed journal articles are of higher scientific rigour and less likely to include typos or errors in the reported results. b) Publication year Publication year is yet another typical moderator variable in meta-analysis. In our study, this factor may provide an indication about how the overall effectiveness of ed-tech applications has changed over time 11 (Cheung & Slavin, 2013). This is relevant because 8 Information on the timing of the ed-tech intervention is missing in some of the selected studies (e.g., Verhallen & Bus, 2010). However, these studies take place in countries whose income classification by the WB has not changed over time (e.g., Netherlands has always been a high-income country). This means that the expression “at the time of the ed-tech intervention” in the eight inclusion and exclusion criterion (see Table 1) is redundant in these cases. Please note also that, although the title of the studies by Berlinski et al. (2021) and Berlinski et al. (2022) is the same, the latter is a revised version of the former and includes different estimates. 9 As illustrated in Fig. 1, we were able to have access to the full text of all the studies identified in each step of the literature search. 10 Information on all moderator variables was available for all the effect sizes included in our sample. 11 Although the timing of the ed-tech interventions is the appropriate variable to look at in assessing how the overall effectiveness of these interventions has changed over time, as stated earlier, this information is missing in some of the studies included in our sample. The rationale for using year of publication is that it may be correlated with the timing of the ed-tech interventions. G. Di Pietro and J. Casta˜ no Mu˜ noz Computers & Education 226 (2025) 105197 5 one might expect that ed-tech interventions will become more effective as time goes on because of the continued and significant advances in technology. c) Type of ed-tech intervention Following Escueta et al. (2020), ed-tech interventions are classified into three categories: access to technology, computer-assisted learning (CAL), and behavioural interventions. 12 The first category comprises measures providing or facilitating the provision of computers/tablets/software and the internet to students. This type of intervention is common in rural areas and in less developed countries where many students lack the technology or internet access required for academic learning. For example, Cristia, Ibarraran, Cueto, Santiago, and Severin (2017) looked at the impact of a programme providing laptops to children in Peru, whereas Leuven, Lindahl, Oosterbeek, and Webbink (2007) analysed the effects of a computer subsidy in the Netherlands, designed for schools with a Fig. 1. Flowchart illustrating the review selection process. 12 Four categories are included in the original classification developed by Escueta et al. (2020). However, the category of online courses cannot be considered in this work because studies evaluating interventions consisting of courses entirely delivered through the internet are excluded from our analysis (see inclusion and exclusion criteria). G. Di Pietro and J. Casta˜ no Mu˜ noz Computers & Education 226 (2025) 105197 6 large proportion of disadvantaged students. The second category (CAL) comprises interventions utilizing technology to supplement traditional classroom instruction. Common examples of this category are educational software and applications to enhance math (e.g., Rutherford et al., 2014) and language (e.g., Macaruso, Hook, & McCabe, 2006) skills. However, CAL can also include more sophisticated tools like intelligent tutoring systems, educational games, virtual reality environments, or even software facilitating online tutoring programmes (e.g., Gortazar, Hupkau, & Rold´ an, 2022). Finally, one should note that CAL includes also Computer-Aided Instruction (CAI). Behavioural interventions are defined as technology-mediated interventions designed to overcome or compensate for noncognitive skill deficits that lead to negative student academic outcomes. They are often targeted at increasing parental involvement in their children’s learning activities (e.g., programmes through which parents receive regular text messages containing tips on how to engage their children in reading activities). 13 Previous review studies (e.g., Escueta et al., 2020) question the role of access to technology in positively affecting student academic achievement. On the other hand, they highlight the potential benefits associated with the other two types of interventions, especially CAL. d) Level of education In our analysis, we distinguished between primary (including kindergartens) and secondary education. 14 Earlier meta-analyses show mixed results about the role of educational level in explaining variations in the effects of ed-tech interventions on student performance. In their meta-analysis, Kazu and Kurto˘ glu Yalçın (2022) show that there is no statistically significant difference in the impact of flipped classroom learning on student performance among studies focusing on elementary, secondary and postsecondary education. On the other hand, Ran, Kasli, and Secada (2021) observe that computer technology interventions have a larger effect for kindergarten and primary school students than for high school students. Digital tools can play a key role in enhancing the learning outcomes of students in the first stages of their education. They enable students to enjoy and have a positive attitude towards learning, promote engagement and contribute to the formation of problem-solving skills (Sun, Chen, & Ruokamo, 2021). e) Subject area Following the approach of several previous meta-analyses (e.g., Di Pietro, 2023), we grouped subjects into three different broad categories: math/science, humanities and a mix category. In addition to general math and science, the first category covers biology as well as various areas of math (e.g., algebra) and specific math functions (e.g., subtraction). There is also one study (Beg, Lucas, Halim, & Saif, 2022) using combined math and science test scores. As for the category of humanities, this comprises the subjects of language, foreign language, and social studies. Different aspects of language learning are considered (e.g., reading, word recognition). Finally, the mix category refers to tests combining different subjects belonging to the first two categories together (e.g., math +Chinese in Mo, Huang, et al., 2015), GPA or overall academic achievement. The hypothesis of heterogeneous effects of ed-tech interventions by subject is supported by the findings of several studies. Bulman and Fairlie (2016) argue that it is easier to develop effective software packages for math than for language. In the meta-analysis by Chauhan (2017), technological applications are found to have a smaller impact on student learning in social studies compared to language, math, and science and technology. Zheng, Warschauer, Lin, and Chang (2016) conclude that, while the impact of one-to-one laptop programmes on academic achievement is positive across five different subject areas, the largest effect is found for science. On the other hand, in their meta-analysis Major et al. (2021) find that in lowand middle-income countries technology-supported learning initiatives are equally important in enhancing student performance in math and literacy. Similar effects in math and literacy are also found by Kim, Gilbert, Yu, and Gale (2021). f) Geographical location As outlined in the inclusion and exclusion criteria, we selected studies examining students in less developed countries as well as studies on more developed countries where more disadvantaged students make up the whole sample or its majority. Based on the literature, it is unclear whether ed-tech interventions can be more effective for the former or latter group of students. On the one hand, one may argue that technology is especially beneficial in less developed countries where teachers are, on average, less prepared and more absent than those in more developed countries. Therefore, in the former countries, as stated by Banerjee, Cole, Duflo, and Linden (2007), computers can replace teachers with less motivation and training. A similar but more general argument is advanced by Bulman 13 In studies examining the effect of access to technology programmes the untreated group consists of students who did not receive any computer/ tablet/software, were not connected to the internet or were not financially incentivised to purchase educational technology (Leuven et al., 2007). In studies looking at the impact of technology-enabled behavioural interventions the control group is composed by individuals (e.g., parents, teachers) that were not exposed to the treatment. Finally, in studies assessing the effectiveness of CAL the control group is made up by students (classes/- schools) that did not use technology to supplement inand out-of-class teaching. 14 Although this is a broad categorization, one needs to consider that it is very difficult to distinguish between lower primary and upper primary education and between lower secondary and upper secondary education in a wide cross-national context. For example, in Tanzania junior secondary education ends at grade 11 (Seo, 2017), whereas in the US senior high school begins at grade 9. G. Di Pietro and J. Casta˜ no Mu˜ noz Computers & Education 226 (2025) 105197 7 and Fairlie (2016). They claim that in less developed countries ed-tech may make up for the lower quality of education. On the other hand, however, as suggested by DeWitt and Alias (2019), several issues may make it more difficult to implement ed-tech programmes in less developed countries, which in turn may undermine their effectiveness. First, these countries are more likely to experience irregular electrical supply and slow internet speeds. This, for instance, could reduce the potential benefits associated with the use of a learning software that can only be accessed online. Second, there is also the possibility that specific instructional materials delivered through technology or even some learning tools may turn out not to be suitable for the culture, customs and morale of a less developed country. For example, between 2005 and 2017 in Malaysia teachers strongly objected to the introduction of mobile phones for learning and teaching as they argued that their use would cause a lot of disciplinary problems. Third, in the literature it is often suggested that to be successful in enhancing student achievement, technology needs to be properly integrated into the teachers’ instruction and curriculum (Rodriguez-Segura, 2022). However, this is less likely to occur in less developed countries where many teachers have not received training in the pedagogies for computers in education. Additionally, in these countries teachers may be especially reluctant to acquire these competencies as they perceive this change to be a threat to traditional teaching practices (Hinostroza, 2018). g) Control/s Finally, we coded a variable which equals one if the model from which the effect size is extracted includes one or more control variables, and zero otherwise. One would expect the presence of control variables to reduce the magnitude of the effect of ed-tech interventions on student achievement. 2.4. Sample characteristics Table 2 presents the studies included in the dataset. For each study, we report information on the author(s), year of publication, country examined, number of the effect sizes collected and their mean value. The dataset used for the meta-analysis includes 740 effect sizes from 72 studies published between 2004 and 2023. Each study included in our dataset contains a number of effect sizes that vary from 1 to 48. The studies cover a total of 26 countries. The largest source countries are India (168 effect sizes) and the US (144 effect sizes). Appendix B reports the definition and the descriptive statistics of the variables used in our analysis. It also indicates the number of effect sizes for each moderator variable and the number of studies that include each moderator variable. 2.5. Risk of bias assessment The risk of bias in each of the studies included in our sample was independently assessed by the two authors. Inter-rater reliability reported by Cohen’s Kappa was rather high (0.78), but any disagreement was resolved through discussions. While the Risk Of Bias in Non-randomised Studies of Interventions (ROBINS-I) (Sterne et al., 2016) was used to evaluate studies with a quasi-experimental design, Version 2 of the Cochrane Risk of Bias tool for randomised trials (RoB 2) (Sterne et al., 2019) was employed to assess the other studies. As shown in Appendix C, a rather common issue with the latter group of studies lies in limited information on, or problems with, the randomisation process. An additional issue is the use of an unreliable measure for student achievement. As regard studies using quasi-experimental approaches, as reported in Appendix D, sample selection issues and missing data problems (e.g., no outcome data for some members of the control (treated) group) are the most common sources of potential bias. 2.6. Models and estimators 2.6.1. Model to compute summary effect size estimates The fixed effects (FE) and random effects (RE) models are two approaches frequently employed in meta-analysis. They are based on different assumptions. The FE model assumes that there is one true effect size common to all studies and that all differences in the observed effects can be ascribed to within-study sampling error. In contrast to this, the RE model assumes that the effect size may vary between studies not only as a result of the within-study sampling error, but also because there is heterogeneity in true effects between studies. This additional variability is typically modelled using a between-study variance parameter (often called τ 2). Considering the different characteristics of the studies included in our sample, it is difficult to assume that there is a common true effect shared by all studies. Thus, it is anticipated that the RE model would be more appropriate, i.e., estimating the mean of the distribution of true effects. Specifically, in line with the approach of Kaiser and Menkhoff (2020), we estimate the mean of the distribution of true effects using a RE meta-analysis based on a Robust Variance Estimation (RVE) developed by Tanner-Smith and Tipton (2014). The RVE approach enables to account for the possibility that multiple effect sizes from the same study are correlated between each other. The advantages of this method are that there is no need to eliminate any effect size (to ensure their statistical independency) and no information is needed on the intercorrelation between effect sizes within studies. 2.6.2. Methods to test and correct for publication bias There are two factors suggesting a potential bias towards positive results. First, manuscripts finding statistically significant positive findings are more attractive to researchers, referees and editors (Begg & Belin, 1988). Second, companies significantly investing in ed-tech products and services may also welcome research results suggesting a positive effect of technology on student learning (Escueta & Holloway, 2019). G. Di Pietro and J. Casta˜ no Mu˜ noz Computers & Education 226 (2025) 105197 8 Table 2 Sources for meta-analysis. Authors Year of publication Country Number of effect sizes collected Mean effect size Abrami et al. 2016 Kenya 3 0.38 Amendum et al. 2011 US 7 0.49 Aunio & Mononen 2018 Finland 9 −0.17 Bai et al. 2016 China 6 0.05 Bai et al. 2023 China 4 0.18 Baker et al. 2017 US 6 −0.01 Bando et al. 2017 Honduras 6 −0.06 Banerjee et al. 2007 India 13 0.21 Barrow et al. 2009 US 8 0.2 Beg et al. 2022 Pakistan 18 0.06 Bergman 2021 US 12 0.12 Bergman & Chan 2021 US 14 0.03 Bergman & Rogers 2016 US 4 0.06 Berlinski et al. 2022 Chile 5 0.09 Berlinski et al. 2021 Chile 1 0.08 Beuermann et al. 2015 Peru 2 0.07 Bianchi et al. 2022 China 4 0.19 Blimpo et al. 2020 Gambia 6 0.51 Borzekowski 2018 Tanzania 7 0.13 Borzekowski et al. 2019 India 48 0.16 Brown et al. 2020 Sudan 2 0.95 Büchel et al. 2022 El Salvador 6 0.28 Cardim et al. 2023 Angola 3 0.02 Carrillo et al. 2010 Ecuador 30 0.12 Chambers et al. 2006 US 10 0.14 Chambers et al. 2008 US 10 0.44 Cilliers et al. 2022 South Africa 9 0.05 Cristia et al. 2017 Peru 24 0.02 de Hoop et al. 2023 Zambia 21 0.25 Derksen et al. 2020 Malawi 6 0.02 Duflo et al. 2012 India 12 0.16 Gortazar et al. 2022 Spain 10 0.38 Ibe & Abamuche 2019 Nigeria 1 0.97 Ito et al. 2021 Cambodia 6 0.69 Johnston & Ksoll 2022 Ghana 29 0.16 Kraft & Monti-Nussbaum 2017 US 20 0.11 Kumar & Mehra 2018 India 1 0.16 Lai et al. 2015 China 4 0.1 Lai et al. 2016 China 8 0.14 Lai et al. 2013 China 6 0.12 Lehrer et al. 2019 Senegal 21 0.24 Leuven et al. 2007 Netherlands 18 −0.04 Linden 2008 India 26 −0.1 Linebarger et al. 2010 US 8 0.29 Lysenko et al. 2019 Kenya 15 0.58 Macaruso et al. 2006 US 4 0.96 Malamud et al. 2019 Peru 6 0.01 McManis & McManis 2016 US 2 0.38 Miller & Robertson 2011 Scotland 1 0.09 Mo et al. 2020 China 4 0.06 Mo et al. 2015(a) China 24 0.12 Mo et al. 2013 China 4 0.06 Mo et al. 2014 China 6 0.18 Mo et al. 2015(b) China 8 0.19 Muralidharan et al. 2019 India 29 0.26 Naik et al. 2020 India 36 0.13 Ntaila & Mbaraka 2023 Malawi 2 2.18 Piper et al. 2016 Kenya 6 0.26 Pitchford 2015 Malawi 13 0.41 Riley 2018 Uganda 16 0.05 Rouse & Krueger 2004 US 13 0.15 Rutherford et al. 2014 US 17 0.08 Santana et al. 2019 Chile 7 0.42 Schacter & Jo 2016 US 1 1.09 Schacter et al. 2016 US 1 0.57 Seo 2017 Tanzania 24 0.02 Setren 2023 US 4 0.12 Silander et al. 2016 US 3 0.12 (continued on next page) G. Di Pietro and J. Casta˜ no Mu˜ noz Computers & Education 226 (2025) 105197 9 this way, one can maximise the benefits, potentially leading to synergistic outcomes. Furthermore, given the low cost of implementation of behavioural interventions, such a combination may turn out to be a promising cost-effective approach for using technologies to reduce educational inequalities. 5. Limitations and future directions This article has three limitations that may have implications for future research. First, we were unable to differentiate between lower and upper primary or secondary education because of the challenges of conducting this analysis in a wide transnational context. Future research could look at these distinctions focusing on a group of more homogeneous countries (e.g., the EU). Second, while we have not considered the impact of technology on digital literacy, there is evidence suggesting a potential link between the two, even when the intervention is limited to technology provision (e.g., Beuermann, Cristia, Cueto, Malamud, & Cruz-Aguayo, 2015; Malamud, Cueto, Cristia, & Beuermann, 2019). Third, we acknowledge that educational technology is a fast-evolving area and some of our findings may possibly change in the future due to an increase in the number of studies examining the effect of emerging technologies on the achievement of less advantaged students. For instance, the study by García-Vandewalle García, García-Carmona, Trujillo Torres, and Moya-Fern´ andez (2022), which relies on the views of eight international experts in education, concludes that new technologies can play an important role in improving the achievement of students in disadvantaged contexts. CRediT authorship contribution statement Giorgio Di Pietro: Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Jonatan Casta˜ no Mu˜ noz: Writing – review & editing, Methodology, Investigation, Data curation, Conceptualization. Declarations of competing interest None. Appendix A. Search terms Category Keywords Setting of interest primary (OR education OR student OR school) OR elementary (OR education OR student OR school) OR secondary (OR education OR student OR school) OR preschool OR kindergarten OR middle (school OR student) AND Exposure computer OR mobile OR laptop OR tablet OR software OR internet OR apps (OR applications) OR digital OR virtual OR technolog* OR text messages OR SMS AND Methodology experimental OR quasi-experimental OR instrumental OR regression discontinuity OR randomized control trial OR randomised control trial OR RCT OR propensity score OR difference-in-difference* AND Outcome student (OR academic OR scholastic) performance (OR achievement OR learning OR outcome) OR (pupil (OR academic OR scholastic) performance (OR achievement OR learning OR outcome) OR test score OR learning ability AND Socio-economic condition less developed countr* OR developing countr* OR underdeveloped countr* OR under-developed countr* OR low* GDP countr* OR low* socio-economic (OR socioeconomic) OR disadvantaged OR less advantaged OR less privileged OR unprivileged OR vulnerable OR less affluent OR less wealthy OR minority OR low*-income OR poor* income OR migrant OR rural OR remote Appendix B. Definition and descriptive statistics Variable name Variable description Unweighted Mean (Standard deviation) (1) Weighted (by the inverse of the number of estimates reported in each study) Mean (Standard deviation) (2) Number of effect sizes/ number of studies including each moderator variable (3) Effect size Estimated effect size (Cohen’s d) 0.177 0.260 740/72 (0.288) (0.394) Effect size’s standard error Estimated standard error of Cohen’s d0.114 0.125 740/72 (0.099) (0.102) Year of publication Year of publication of the study where the effect size is extracted 2016.197 2016.386 740/72 (4.916) (4.755) (continued on next page) G. Di Pietro and J. Casta˜ no Mu˜ noz Computers & Education 226 (2025) 105197 16 (continued) Variable name Variable description Unweighted Mean (Standard deviation) (1) Weighted (by the inverse of the number of estimates reported in each study) Mean (Standard deviation) (2) Number of effect sizes/ number of studies including each moderator variable (3) Control/s Dummy, 1 if the model from which the effect size is extracted includes one or more control variables, 0 otherwise 0.696 0.592 515/52 (0.460) (0.492) Subject area Math/Science Dummy, 1 if the subject area is math or science, 0 otherwise 0.451 0.501 334/56 (0.498) (0.500) Humanities (base category) Dummy, 1 if the subject area is humanities, 0 otherwise 0.423 0.406 313/51 (0.494) (0.491) Mix Dummy, 1 if student achievement in different subject areas is considered, 0 otherwise 0.126 0.093 93/16 (0.332) (0.290) Level of education Primary (base category) Dummy, 1 if the education level is primary school (or kindergarten), 0 otherwise 0.727 0.752 538/54 (0.446) (0.432) Secondary Dummy, 1 if the education level is secondary school, 0 otherwise 0.250 0.221 185/16 (0.433) (0.415) Primary and Secondary Dummy, 1 if the education level is both primary and secondary school, 0 otherwise 0.023 0.028 17/2 (0.150) (0.164) Type of ed-tech intervention CAL Dummy, 1 if the ed-tech intervention is a computer-assisted learning programme, 0 otherwise 0.757 0.742 560/54 (0.429) (0.438) Behavioural interventions Dummy, 1 if the ed-tech intervention is behavioural in nature, 0 otherwise 0.138 0.152 102/11 (0.345) (0.359) Access to technology (base category) Dummy, 1 if the ed-tech intervention provides students with access to technology and/or the internet, 0 otherwise 0.105 0.107 78/10 (0.307) (0.309) Geographical location Students in less developed countries (base category) Dummy, 1 if the effect size is extracted from a study examining students in less developed countries, 0 otherwise 0.604 0.476 447/34 (0.489) (0.500) More disadvantaged students in more developed countries Dummy, 1 if the effect size is extracted from a study examining more disadvantaged students in more developed countries, 0 otherwise 0.396 0.524 293/38 (0.489) (0.500) Type of publication Peer-reviewed journal articles Dummy, 1 if the effect size is extracted from a peer-reviewed journal article, 0 otherwise 0.788 0.807 583/58 (0.409) (0.395) Other publications (base category) Dummy, 1 if the effect size is extracted from a publication that is not a peer-reviewed journal article, 0 otherwise 0.212 0.193 157/14 (0.409) (0.396) Appendix C. Quality assessment for RCTs (RoB 2) Study Risk of bias arising from the randomisation process Risk of bias due to deviations from the intended intervention Missing outcome data Risk of bias in the measurement of the outcome Risk of bias in the selection of the reported results Overall risk of bias Abrami, Wade, Lysenko, Marsh, and Gioko (2016) High Low Some concerns Low Low High Amendum, Vernon-Feagans, and Ginsberg (2011) Some concerns Low Low Some concerns Low Some concerns Aunio and Mononen (2018) High Low Low Low Low High Bai, Mo, Zhang, Boswell, and Rozelle (2016) Low Low Low Low Low Low Bai et al. (2023) Some concerns Low Low Low Low Some concerns Baker et al. (2017) Some concerns Low Some concerns Low Low Some concerns Bando, Gallego, Gertler, and Romero Fonseca (2017) Some concerns Low Low Low Low Some concerns (continued on next page) G. Di Pietro and J. Casta˜ no Mu˜ noz Computers & Education 226 (2025) 105197 17 (continued) Study Risk of bias arising from the randomisation process Risk of bias due to deviations from the intended intervention Missing outcome data Risk of bias in the measurement of the outcome Risk of bias in the selection of the reported results Overall risk of bias Banerjee et al. (2007) Low Low Low Low Low Low Barrow et al. (2009) Low Some concerns Low Low Low Some concerns Beg et al. (2022) Some concerns Low Low Low Low Some concerns Bergman (2021) Low Low Low Low Low Low Bergman and Chan (2021) Low Low Low Low Low Low Bergman and Rogers (2016) Some concerns Some concerns Low Low Low Some concerns Berlinski, Busso, Dinkelman, and Martinez (2022) Low Low Some concerns Low Low Some concerns Berlinski, Busso, Dinkelman, and Martinez (2021) Low Low Some concerns Low Low Some concerns Beuermann et al. (2015) Low Low Low Low Low Low Borzekowski (2018) Some concerns High Low Low Low High Borzekowski, Singpurwalla, Mehrotra, and Howard (2019) Some concerns Low Low Some concerns Low Some concerns Büchel, Jakob, Kuhnhanss, Steffen, & Brunetti (2022) Some concerns Low Low Low Low Some concerns Cardim, Molina-Mill´ an, and Vicente (2023) Some concerns Low Low Some concerns Low Some concerns Carrillo, Onofa, and Ponce (2010) Some concerns Low Low Low Low Some concerns Chambers, Cheung, Gifford, Madden, and Slavin (2006) Low Low Low Low Low Low Chambers et al. (2008) Some concerns Low Low Low Low Some concerns Cilliers et al. (2022) Low Low Low Low Low Low Cristia et al. (2017) Some concerns Low Low Low Low Some concerns de Hoop et al. (2023) Some concerns Low Low Low Low Some concerns Derksen, Leclerc, and Souza (2020) Low High Some concerns Low Low High Duflo, Hanna, and Ryan (2012) Low Low Low Low Low Low Gortazar et al. (2022) Low Low Low Some concerns Low Some concerns Ibe and Abamuche (2019) High Low Low Low Low High Ito, Kasai, and Nakamuro (2021) Low Some concerns Low Low Low Some concerns Johnston and Ksoll (2022) Some concerns Low Low Some concerns Low Some concerns Kraft and Monti-Nussbaum (2017) Low Low Some concerns Low Low Some concerns Kumar and Mehra (2018) Low Some concerns Low Low Low Some concerns Lai, Luo, Zhang, Huang, and Rozelle (2015) Low Low Low Low Low Low Lai et al. (2016) Some concerns Low Low Low Low Some concerns Lai et al. (2013) Low Low Low Low Low Low Linden (2008) Low Low Low Low Low Low Linebarger, Piotrowski, and Greenwood (2010) Low Low Low Some concerns Low Some concerns Malamud et al. (2019) Some concerns Low Some concerns Low Low Some concerns McManis and McManis (2016) High Low Some concerns Low Low High Miller and Robertson (2011) Some concerns Low Some concerns Low Low Some concerns (continued on next page) G. Di Pietro and J. Casta˜ no Mu˜ noz Computers & Education 226 (2025) 105197 18 (continued) Study Risk of bias arising from the randomisation process Risk of bias due to deviations from the intended intervention Missing outcome data Risk of bias in the measurement of the outcome Risk of bias in the selection of the reported results Overall risk of bias Mo et al. (2020) Low Low Low Low Low Low Mo, Huang, et al. (2015) Low Low Low Low Low Low Mo et al. (2013) Low Low Low Low Low Low Mo et al. (2014) Some concerns Low Low Low Low Some concerns Mo, Zhang, et al. (2015) Low Low Low Low Low Low Muralidharan, Singh, and Ganimian (2019) Low Low Low Low Low Low Naik, Chitre, Bhalla, and Rajan (2020) Some concerns Low Low Low Low Some concerns Piper, Simmons Zuilkowski, Kwayumba, and Strigel (2016) Some concerns Low Low Some concerns Low Some concerns Pitchford (2015) High Low Low Some concerns Low High Riley (2018) Some concerns Low Low Low Some concerns Some concerns Rouse and Krueger (2004) Low Low Some concerns Low Low Some concerns Rutherford et al. (2014) Low Low Low Low Low Low Santana et al. (2019) Low Low Some concerns Low Low Some concerns Schacter et al. (2016) High Low High Low Low High Seo (2017) Low Low Some concerns Low Low Some concerns Setren (2023) Low Some concerns Some concerns Low Low Some concerns Silander et al. (2016) High Low Some concerns Some concerns Low High Verhallen and Bus (2010) Some concerns Low Low Low Low Some concerns Wolf, Aber, Behrman, and Tsinigo (2019) Low Low Low Some concerns Low Some concerns Yang et al. (2013) Some concerns Low Low Low Low Some concerns Appendix D. Quality assessment for quasi-experimental studies (ROBINS-I) Study Bias due to confounding Bias in selection of participants into the study Bias in classification of interventions Bias due to deviations from intended interventions Bias due to missing data Bias in measurement of outcomes Bias in selection of reported results Overall bias Bianchi, Lu, and Song (2022) Low Low Low Low Low Low Low Low Blimpo, Gajigo, Owusu, Tomita, and Xu (2020) Low Moderate Low Low Low Low Moderate Moderate Brown et al. (2020) Low Moderate Low Low Moderate Low Low Moderate Lehrer, Mawoyo, and Mbaye (2019) Low Moderate Low Low Moderate Low Low Moderate Leuven et al. (2007) Low Low Low Low Low Low Low Low Lysenko et al. (2019) Low Moderate Low Low Moderate Low Low Moderate Macaruso et al. (2006) Low Moderate Low Low Moderate Low Low Moderate Ntaila and Mbaraka (2023) High Moderate Low Low Moderate Low Low High (continued on next page) G. 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