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  • Published: 02 December 2020

Enhancing senior high school student engagement and academic performance using an inclusive and scalable inquiry-based program

  • Locke Davenport Huyer   ORCID: orcid.org/0000-0003-1526-7122 1 , 2   na1 ,
  • Neal I. Callaghan   ORCID: orcid.org/0000-0001-8214-3395 1 , 3   na1 ,
  • Sara Dicks 4 ,
  • Edward Scherer 4 ,
  • Andrey I. Shukalyuk 1 ,
  • Margaret Jou 4 &
  • Dawn M. Kilkenny   ORCID: orcid.org/0000-0002-3899-9767 1 , 5  

npj Science of Learning volume  5 , Article number:  17 ( 2020 ) Cite this article

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The multi-disciplinary nature of science, technology, engineering, and math (STEM) careers often renders difficulty for high school students navigating from classroom knowledge to post-secondary pursuits. Discrepancies between the knowledge-based high school learning approach and the experiential approach of future studies leaves some students disillusioned by STEM. We present Discovery , a term-long inquiry-focused learning model delivered by STEM graduate students in collaboration with high school teachers, in the context of biomedical engineering. Entire classes of high school STEM students representing diverse cultural and socioeconomic backgrounds engaged in iterative, problem-based learning designed to emphasize critical thinking concomitantly within the secondary school and university environments. Assessment of grades and survey data suggested positive impact of this learning model on students’ STEM interests and engagement, notably in under-performing cohorts, as well as repeating cohorts that engage in the program on more than one occasion. Discovery presents a scalable platform that stimulates persistence in STEM learning, providing valuable learning opportunities and capturing cohorts of students that might otherwise be under-engaged in STEM.

Introduction

High school students with diverse STEM interests often struggle to understand the STEM experience outside the classroom 1 . The multi-disciplinary nature of many career fields can foster a challenge for students in their decision to enroll in appropriate high school courses while maintaining persistence in study, particularly when these courses are not mandatory 2 . Furthermore, this challenge is amplified by the known discrepancy between the knowledge-based learning approach common in high schools and the experiential, mastery-based approaches afforded by the subsequent undergraduate model 3 . In the latter, focused classes, interdisciplinary concepts, and laboratory experiences allow for the application of accumulated knowledge, practice in problem solving, and development of both general and technical skills 4 . Such immersive cooperative learning environments are difficult to establish in the secondary school setting and high school teachers often struggle to implement within their classroom 5 . As such, high school students may become disillusioned before graduation and never experience an enriched learning environment, despite their inherent interests in STEM 6 .

It cannot be argued that early introduction to varied math and science disciplines throughout high school is vital if students are to pursue STEM fields, especially within engineering 7 . However, the majority of literature focused on student interest and retention in STEM highlights outcomes in US high school learning environments, where the sciences are often subject-specific from the onset of enrollment 8 . In contrast, students in the Ontario (Canada) high school system are required to complete Level 1 and 2 core courses in science and math during Grades 9 and 10; these courses are offered as ‘applied’ or ‘academic’ versions and present broad topics of content 9 . It is not until Levels 3 and 4 (generally Grades 11 and 12, respectively) that STEM classes become subject-specific (i.e., Biology, Chemistry, and/or Physics) and are offered as “university”, “college”, or “mixed” versions, designed to best prepare students for their desired post-secondary pursuits 9 . Given that Levels 3 and 4 science courses are not mandatory for graduation, enrollment identifies an innate student interest in continued learning. Furthermore, engagement in these post-secondary preparatory courses is also dependent upon achieving successful grades in preceding courses, but as curriculum becomes more subject-specific, students often yield lower degrees of success in achieving course credit 2 . Therefore, it is imperative that learning supports are best focused on ensuring that those students with an innate interest are able to achieve success in learning.

When given opportunity and focused support, high school students are capable of successfully completing rigorous programs at STEM-focused schools 10 . Specialized STEM schools have existed in the US for over 100 years; generally, students are admitted after their sophomore year of high school experience (equivalent to Grade 10) based on standardized test scores, essays, portfolios, references, and/or interviews 11 . Common elements to this learning framework include a diverse array of advanced STEM courses, paired with opportunities to engage in and disseminate cutting-edge research 12 . Therein, said research experience is inherently based in the processes of critical thinking, problem solving, and collaboration. This learning framework supports translation of core curricular concepts to practice and is fundamental in allowing students to develop better understanding and appreciation of STEM career fields.

Despite the described positive attributes, many students do not have the ability or resources to engage within STEM-focused schools, particularly given that they are not prevalent across Canada, and other countries across the world. Consequently, many public institutions support the idea that post-secondary led engineering education programs are effective ways to expose high school students to engineering education and relevant career options, and also increase engineering awareness 13 . Although singular class field trips are used extensively to accomplish such programs, these may not allow immersive experiences for application of knowledge and practice of skills that are proven to impact long-term learning and influence career choices 14 , 15 . Longer-term immersive research experiences, such as after-school programs or summer camps, have shown successful at recruiting students into STEM degree programs and careers, where longevity of experience helps foster self-determination and interest-led, inquiry-based projects 4 , 16 , 17 , 18 , 19 .

Such activities convey the elements that are suggested to make a post-secondary led high school education programs successful: hands-on experience, self-motivated learning, real-life application, immediate feedback, and problem-based projects 20 , 21 . In combination with immersion in university teaching facilities, learning is authentic and relevant, similar to the STEM school-focused framework, and consequently representative of an experience found in actual STEM practice 22 . These outcomes may further be a consequence of student engagement and attitude: Brown et al. studied the relationships between STEM curriculum and student attitudes, and found the latter played a more important role in intention to persist in STEM when compared to self-efficacy 23 . This is interesting given that student self-efficacy has been identified to influence ‘motivation, persistence, and determination’ in overcoming challenges in a career pathway 24 . Taken together, this suggests that creation and delivery of modern, exciting curriculum that supports positive student attitudes is fundamental to engage and retain students in STEM programs.

Supported by the outcomes of identified effective learning strategies, University of Toronto (U of T) graduate trainees created a novel high school education program Discovery , to develop a comfortable yet stimulating environment of inquiry-focused iterative learning for senior high school students (Grades 11 & 12; Levels 3 & 4) at non-specialized schools. Built in strong collaboration with science teachers from George Harvey Collegiate Institute (Toronto District School Board), Discovery stimulates application of STEM concepts within a unique term-long applied curriculum delivered iteratively within both U of T undergraduate teaching facilities and collaborating high school classrooms 25 . Based on the volume of medically-themed news and entertainment that is communicated to the population at large, the rapidly-growing and diverse field of biomedical engineering (BME) were considered an ideal program context 26 . In its definition, BME necessitates cross-disciplinary STEM knowledge focused on the betterment of human health, wherein Discovery facilitates broadening student perspective through engaging inquiry-based projects. Importantly, Discovery allows all students within a class cohort to work together with their classroom teacher, stimulating continued development of a relevant learning community that is deemed essential for meaningful context and important for transforming student perspectives and understandings 27 , 28 . Multiple studies support the concept that relevant learning communities improve student attitudes towards learning, significantly increasing student motivation in STEM courses, and consequently improving the overall learning experience 29 . Learning communities, such as that provided by Discovery , also promote the formation of self-supporting groups, greater active involvement in class, and higher persistence rates for participating students 30 .

The objective of Discovery , through structure and dissemination, is to engage senior high school science students in challenging, inquiry-based practical BME activities as a mechanism to stimulate comprehension of STEM curriculum application to real-world concepts. Consequent focus is placed on critical thinking skill development through an atmosphere of perseverance in ambiguity, something not common in a secondary school knowledge-focused delivery but highly relevant in post-secondary STEM education strategies. Herein, we describe the observed impact of the differential project-based learning environment of Discovery on student performance and engagement. We identify the value of an inquiry-focused learning model that is tangible for students who struggle in a knowledge-focused delivery structure, where engagement in conceptual critical thinking in the relevant subject area stimulates student interest, attitudes, and resulting academic performance. Assessment of study outcomes suggests that when provided with a differential learning opportunity, student performance and interest in STEM increased. Consequently, Discovery provides an effective teaching and learning framework within a non-specialized school that motivates students, provides opportunity for critical thinking and problem-solving practice, and better prepares them for persistence in future STEM programs.

Program delivery

The outcomes of the current study result from execution of Discovery over five independent academic terms as a collaboration between Institute of Biomedical Engineering (graduate students, faculty, and support staff) and George Harvey Collegiate Institute (science teachers and administration) stakeholders. Each term, the program allowed senior secondary STEM students (Grades 11 and 12) opportunity to engage in a novel project-based learning environment. The program structure uses the problem-based engineering capstone framework as a tool of inquiry-focused learning objectives, motivated by a central BME global research topic, with research questions that are inter-related but specific to the curriculum of each STEM course subject (Fig. 1 ). Over each 12-week term, students worked in teams (3–4 students) within their class cohorts to execute projects with the guidance of U of T trainees ( Discovery instructors) and their own high school teacher(s). Student experimental work was conducted in U of T teaching facilities relevant to the research study of interest (i.e., Biology and Chemistry-based projects executed within Undergraduate Teaching Laboratories; Physics projects executed within Undergraduate Design Studios). Students were introduced to relevant techniques and safety procedures in advance of iterative experimentation. Importantly, this experience served as a course term project for students, who were assessed at several points throughout the program for performance in an inquiry-focused environment as well as within the regular classroom (Fig. 1 ). To instill the atmosphere of STEM, student teams delivered their outcomes in research poster format at a final symposium, sharing their results and recommendations with other post-secondary students, faculty, and community in an open environment.

figure 1

The general program concept (blue background; top left ) highlights a global research topic examined through student dissemination of subject-specific research questions, yielding multifaceted student outcomes (orange background; top right ). Each program term (term workflow, yellow background; bottom panel ), students work on program deliverables in class (blue), iterate experimental outcomes within university facilities (orange), and are assessed accordingly at numerous deliverables in an inquiry-focused learning model.

Over the course of five terms there were 268 instances of tracked student participation, representing 170 individual students. Specifically, 94 students participated during only one term of programming, 57 students participated in two terms, 16 students participated in three terms, and 3 students participated in four terms. Multiple instances of participation represent students that enrol in more than one STEM class during their senior years of high school, or who participated in Grade 11 and subsequently Grade 12. Students were surveyed before and after each term to assess program effects on STEM interest and engagement. All grade-based assessments were performed by high school teachers for their respective STEM class cohorts using consistent grading rubrics and assignment structure. Here, we discuss the outcomes of student involvement in this experiential curriculum model.

Student performance and engagement

Student grades were assigned, collected, and anonymized by teachers for each Discovery deliverable (background essay, client meeting, proposal, progress report, poster, and final presentation). Teachers anonymized collective Discovery grades, the component deliverable grades thereof, final course grades, attendance in class and during programming, as well as incomplete classroom assignments, for comparative study purposes. Students performed significantly higher in their cumulative Discovery grade than in their cumulative classroom grade (final course grade less the Discovery contribution; p  < 0.0001). Nevertheless, there was a highly significant correlation ( p  < 0.0001) observed between the grade representing combined Discovery deliverables and the final course grade (Fig. 2a ). Further examination of the full dataset revealed two student cohorts of interest: the “Exceeds Expectations” (EE) subset (defined as those students who achieved ≥1 SD [18.0%] grade differential in Discovery over their final course grade; N  = 99 instances), and the “Multiple Term” (MT) subset (defined as those students who participated in Discovery more than once; 76 individual students that collectively accounted for 174 single terms of assessment out of the 268 total student-terms delivered) (Fig. 2b, c ). These subsets were not unrelated; 46 individual students who had multiple experiences (60.5% of total MTs) exhibited at least one occasion in achieving a ≥18.0% grade differential. As students participated in group work, there was concern that lower-performing students might negatively influence the Discovery grade of higher-performing students (or vice versa). However, students were observed to self-organize into groups where all individuals received similar final overall course grades (Fig. 2d ), thereby alleviating these concerns.

figure 2

a Linear regression of student grades reveals a significant correlation ( p  = 0.0009) between Discovery performance and final course grade less the Discovery contribution to grade, as assessed by teachers. The dashed red line and intervals represent the theoretical 1:1 correlation between Discovery and course grades and standard deviation of the Discovery -course grade differential, respectively. b , c Identification of subgroups of interest, Exceeds Expectations (EE; N  = 99, orange ) who were ≥+1 SD in Discovery -course grade differential and Multi-Term (MT; N  = 174, teal ), of which N  = 65 students were present in both subgroups. d Students tended to self-assemble in working groups according to their final course performance; data presented as mean ± SEM. e For MT students participating at least 3 terms in Discovery , there was no significant correlation between course grade and time, while ( f ) there was a significant correlation between Discovery grade and cumulative terms in the program. Histograms of total absences per student in ( g ) Discovery and ( h ) class (binned by 4 days to be equivalent in time to a single Discovery absence).

The benefits experienced by MT students seemed progressive; MT students that participated in 3 or 4 terms ( N  = 16 and 3, respectively ) showed no significant increase by linear regression in their course grade over time ( p  = 0.15, Fig. 2e ), but did show a significant increase in their Discovery grades ( p  = 0.0011, Fig. 2f ). Finally, students demonstrated excellent Discovery attendance; at least 91% of participants attended all Discovery sessions in a given term (Fig. 2g ). In contrast, class attendance rates reveal a much wider distribution where 60.8% (163 out of 268 students) missed more than 4 classes (equivalent in learning time to one Discovery session) and 14.6% (39 out of 268 students) missed 16 or more classes (equivalent in learning time to an entire program of Discovery ) in a term (Fig. 2h ).

Discovery EE students (Fig. 3 ), roughly by definition, obtained lower course grades ( p  < 0.0001, Fig. 3a ) and higher final Discovery grades ( p  = 0.0004, Fig. 3b ) than non-EE students. This cohort of students exhibited program grades higher than classmates (Fig. 3c–h ); these differences were significant in every category with the exception of essays, where they outperformed to a significantly lesser degree ( p  = 0.097; Fig. 3c ). There was no statistically significant difference in EE vs. non-EE student classroom attendance ( p  = 0.85; Fig. 3i, j ). There were only four single day absences in Discovery within the EE subset; however, this difference was not statistically significant ( p  = 0.074).

figure 3

The “Exceeds Expectations” (EE) subset of students (defined as those who received a combined Discovery grade ≥1 SD (18.0%) higher than their final course grade) performed ( a ) lower on their final course grade and ( b ) higher in the Discovery program as a whole when compared to their classmates. d – h EE students received significantly higher grades on each Discovery deliverable than their classmates, except for their ( c ) introductory essays and ( h ) final presentations. The EE subset also tended ( i ) to have a higher relative rate of attendance during Discovery sessions but no difference in ( j ) classroom attendance. N  = 99 EE students and 169 non-EE students (268 total). Grade data expressed as mean ± SEM.

Discovery MT students (Fig. 4 ), although not receiving significantly higher grades in class than students participating in the program only one time ( p  = 0.29, Fig. 4a ), were observed to obtain higher final Discovery grades than single-term students ( p  = 0.0067, Fig. 4b ). Although trends were less pronounced for individual MT student deliverables (Fig. 4c–h ), this student group performed significantly better on the progress report ( p  = 0.0021; Fig. 4f ). Trends of higher performance were observed for initial proposals and final presentations ( p  = 0.081 and 0.056, respectively; Fig. 4e, h ); all other deliverables were not significantly different between MT and non-MT students (Fig. 4c, d, g ). Attendance in Discovery ( p  = 0.22) was also not significantly different between MT and non-MT students, although MT students did miss significantly less class time ( p  = 0.010) (Fig. 4i, j ). Longitudinal assessment of individual deliverables for MT students that participated in three or more Discovery terms (Fig. 5 ) further highlights trend in improvement (Fig. 2f ). Greater performance over terms of participation was observed for essay ( p  = 0.0295, Fig. 5a ), client meeting ( p  = 0.0003, Fig. 5b ), proposal ( p  = 0.0004, Fig. 5c ), progress report ( p  = 0.16, Fig. 5d ), poster ( p  = 0.0005, Fig. 5e ), and presentation ( p  = 0.0295, Fig. 5f ) deliverable grades; these trends were all significant with the exception of the progress report ( p  = 0.16, Fig. 5d ) owing to strong performance in this deliverable in all terms.

figure 4

The “multi-term” (MT) subset of students (defined as having attended more than one term of Discovery ) demonstrated favorable performance in Discovery , ( a ) showing no difference in course grade compared to single-term students, but ( b outperforming them in final Discovery grade. Independent of the number of times participating in Discovery , MT students did not score significantly differently on their ( c ) essay, ( d ) client meeting, or ( g ) poster. They tended to outperform their single-term classmates on the ( e ) proposal and ( h ) final presentation and scored significantly higher on their ( f ) progress report. MT students showed no statistical difference in ( i ) Discovery attendance but did show ( j ) higher rates of classroom attendance than single-term students. N  = 174 MT instances of student participation (76 individual students) and 94 single-term students. Grade data expressed as mean ± SEM.

figure 5

Longitudinal assessment of a subset of MT student participants that participated in three ( N  = 16) or four ( N  = 3) terms presents a significant trend of improvement in their ( a ) essay, ( b ) client meeting, ( c ) proposal, ( e ) poster, and ( f ) presentation grade. d Progress report grades present a trend in improvement but demonstrate strong performance in all terms, limiting potential for student improvement. Grade data are presented as individual student performance; each student is represented by one color; data is fitted with a linear trendline (black).

Finally, the expansion of Discovery to a second school of lower LOI (i.e., nominally higher aggregate SES) allowed for the assessment of program impact in a new population over 2 terms of programming. A significant ( p  = 0.040) divergence in Discovery vs. course grade distribution from the theoretical 1:1 relationship was found in the new cohort (S 1 Appendix , Fig. S 1 ), in keeping with the pattern established in this study.

Teacher perceptions

Qualitative observation in the classroom by high school teachers emphasized the value students independently placed on program participation and deliverables. Throughout the term, students often prioritized Discovery group assignments over other tasks for their STEM courses, regardless of academic weight and/or due date. Comparing within this student population, teachers spoke of difficulties with late and incomplete assignments in the regular curriculum but found very few such instances with respect to Discovery -associated deliverables. Further, teachers speculated on the good behavior and focus of students in Discovery programming in contrast to attentiveness and behavior issues in their school classrooms. Multiple anecdotal examples were shared of renewed perception of student potential; students that exhibited poor academic performance in the classroom often engaged with high performance in this inquiry-focused atmosphere. Students appeared to take a sense of ownership, excitement, and pride in the setting of group projects oriented around scientific inquiry, discovery, and dissemination.

Student perceptions

Students were asked to consider and rank the academic difficulty (scale of 1–5, with 1 = not challenging and 5 = highly challenging) of the work they conducted within the Discovery learning model. Considering individual Discovery terms, at least 91% of students felt the curriculum to be sufficiently challenging with a 3/5 or higher ranking (Term 1: 87.5%, Term 2: 93.4%, Term 3: 85%, Term 4: 93.3%, Term 5: 100%), and a minimum of 58% of students indicating a 4/5 or higher ranking (Term 1: 58.3%, Term 2: 70.5%, Term 3: 67.5%, Term 4: 69.1%, Term 5: 86.4%) (Fig. 6a ).

figure 6

a Histogram of relative frequency of perceived Discovery programming academic difficulty ranked from not challenging (1) to highly challenging (5) for each session demonstrated the consistently perceived high degree of difficulty for Discovery programming (total responses: 223). b Program participation increased student comfort (94.6%) with navigating lab work in a university or college setting (total responses: 220). c Considering participation in Discovery programming, students indicated their increased (72.4%) or decreased (10.1%) likelihood to pursue future experiences in STEM as a measure of program impact (total responses: 217). d Large majority of participating students (84.9%) indicated their interest for future participation in Discovery (total responses: 212). Students were given the opportunity to opt out of individual survey questions, partially completed surveys were included in totals.

The majority of students (94.6%) indicated they felt more comfortable with the idea of performing future work in a university STEM laboratory environment given exposure to university teaching facilities throughout the program (Fig. 6b ). Students were also queried whether they were (i) more likely, (ii) less likely, or (iii) not impacted by their experience in the pursuit of STEM in the future. The majority of participants (>82%) perceived impact on STEM interests, with 72.4% indicating they were more likely to pursue these interests in the future (Fig. 6c ). When surveyed at the end of term, 84.9% of students indicated they would participate in the program again (Fig. 6d ).

We have described an inquiry-based framework for implementing experiential STEM education in a BME setting. Using this model, we engaged 268 instances of student participation (170 individual students who participated 1–4 times) over five terms in project-based learning wherein students worked in peer-based teams under the mentorship of U of T trainees to design and execute the scientific method in answering a relevant research question. Collaboration between high school teachers and Discovery instructors allowed for high school student exposure to cutting-edge BME research topics, participation in facilitated inquiry, and acquisition of knowledge through scientific discovery. All assessments were conducted by high school teachers and constituted a fraction (10–15%) of the overall course grade, instilling academic value for participating students. As such, students exhibited excitement to learn as well as commitment to their studies in the program.

Through our observations and analysis, we suggest there is value in differential learning environments for students that struggle in a knowledge acquisition-focused classroom setting. In general, we observed a high level of academic performance in Discovery programming (Fig. 2a ), which was highlighted exceptionally in EE students who exhibited greater academic performance in Discovery deliverables compared to normal coursework (>18% grade improvement in relevant deliverables). We initially considered whether this was the result of strong students influencing weaker students; however, group organization within each course suggests this is not the case (Fig. 2d ). With the exception of one class in one term (24 participants assigned by their teacher), students were allowed to self-organize into working groups and they chose to work with other students of relatively similar academic performance (as indicated by course grade), a trend observed in other studies 31 , 32 . Remarkably, EE students not only excelled during Discovery when compared to their own performance in class, but this cohort also achieved significantly higher average grades in each of the deliverables throughout the program when compared to the remaining Discovery cohort (Fig. 3 ). This data demonstrates the value of an inquiry-based learning environment compared to knowledge-focused delivery in the classroom in allowing students to excel. We expect that part of this engagement was resultant of student excitement with a novel learning opportunity. It is however a well-supported concept that students who struggle in traditional settings tend to demonstrate improved interest and motivation in STEM when given opportunity to interact in a hands-on fashion, which supports our outcomes 4 , 33 . Furthermore, these outcomes clearly represent variable student learning styles, where some students benefit from a greater exchange of information, knowledge and skills in a cooperative learning environment 34 . The performance of the EE group may not be by itself surprising, as the identification of the subset by definition required high performers in Discovery who did not have exceptionally high course grades; in addition, the final Discovery grade is dependent on the component assignment grades. However, the discrepancies between EE and non-EE groups attendance suggests that students were engaged by Discovery in a way that they were not by regular classroom curriculum.

In addition to quantified engagement in Discovery observed in academic performance, we believe remarkable attendance rates are indicative of the value students place in the differential learning structure. Given the differences in number of Discovery days and implications of missing one day of regular class compared to this immersive program, we acknowledge it is challenging to directly compare attendance data and therefore approximate this comparison with consideration of learning time equivalence. When combined with other subjective data including student focus, requests to work on Discovery during class time, and lack of discipline/behavior issues, the attendance data importantly suggests that students were especially engaged by the Discovery model. Further, we believe the increased commute time to the university campus (students are responsible for independent transit to campus, a much longer endeavour than the normal school commute), early program start time, and students’ lack of familiarity with the location are non-trivial considerations when determining the propensity of students to participate enthusiastically in Discovery . We feel this suggests the students place value on this team-focused learning and find it to be more applicable and meaningful to their interests.

Given post-secondary admission requirements for STEM programs, it would be prudent to think that students participating in multiple STEM classes across terms are the ones with the most inherent interest in post-secondary STEM programs. The MT subset, representing students who participated in Discovery for more than one term, averaged significantly higher final Discovery grades. The increase in the final Discovery grade was observed to result from a general confluence of improved performance over multiple deliverables and a continuous effort to improve in a STEM curriculum. This was reflected in longitudinal tracking of Discovery performance, where we observed a significant trend of improved performance. Interestingly, the high number of MT students who were included in the EE group suggests that students who had a keen interest in science enrolled in more than one course and in general responded well to the inquiry-based teaching method of Discovery , where scientific method was put into action. It stands to reason that students interested in science will continue to take STEM courses and will respond favorably to opportunities to put classroom theory to practical application.

The true value of an inquiry-based program such as Discovery may not be based in inspiring students to perform at a higher standard in STEM within the high school setting, as skills in critical thinking do not necessarily translate to knowledge-based assessment. Notably, students found the programming equally challenging throughout each of the sequential sessions, perhaps somewhat surprising considering the increasing number of repeat attendees in successive sessions (Fig. 6a ). Regardless of sub-discipline, there was an emphasis of perceived value demonstrated through student surveys where we observed indicated interest in STEM and comfort with laboratory work environments, and desire to engage in future iterations given the opportunity. Although non-quantitative, we perceive this as an indicator of significant student engagement, even though some participants did not yield academic success in the program and found it highly challenging given its ambiguity.

Although we observed that students become more certain of their direction in STEM, further longitudinal study is warranted to make claim of this outcome. Additionally, at this point in our assessment we cannot effectively assess the practical outcomes of participation, understanding that the immediate effects observed are subject to a number of factors associated with performance in the high school learning environment. Future studies that track graduates from this program will be prudent, in conjunction with an ever-growing dataset of assessment as well as surveys designed to better elucidate underlying perceptions and attitudes, to continue to understand the expected benefits of this inquiry-focused and partnered approach. Altogether, a multifaceted assessment of our early outcomes suggests significant value of an immersive and iterative interaction with STEM as part of the high school experience. A well-defined divergence from knowledge-based learning, focused on engagement in critical thinking development framed in the cutting-edge of STEM, may be an important step to broadening student perspectives.

In this study, we describe the short-term effects of an inquiry-based STEM educational experience on a cohort of secondary students attending a non-specialized school, and suggest that the framework can be widely applied across virtually all subjects where inquiry-driven and mentored projects can be undertaken. Although we have demonstrated replication in a second cohort of nominally higher SES (S 1 Appendix , Supplementary Fig. 1 ), a larger collection period with more students will be necessary to conclusively determine impact independent of both SES and specific cohort effects. Teachers may also find this framework difficult to implement depending on resources and/or institutional investment and support, particularly if post-secondary collaboration is inaccessible. Offerings to a specific subject (e.g., physics) where experiments yielding empirical data are logistically or financially simpler to perform may be valid routes of adoption as opposed to the current study where all subject cohorts were included.

As we consider Discovery in a bigger picture context, expansion and implementation of this model is translatable. Execution of the scientific method is an important aspect of citizen science, as the concepts of critical thing become ever-more important in a landscape of changing technological landscapes. Giving students critical thinking and problem-solving skills in their primary and secondary education provides value in the context of any career path. Further, we feel that this model is scalable across disciplines, STEM or otherwise, as a means of building the tools of inquiry. We have observed here the value of differential inclusive student engagement and critical thinking through an inquiry-focused model for a subset of students, but further to this an engagement, interest, and excitement across the body of student participants. As we educate the leaders of tomorrow, we suggest that use of an inquiry-focused model such as Discovery could facilitate growth of a data-driven critical thinking framework.

In conclusion, we have presented a model of inquiry-based STEM education for secondary students that emphasizes inclusion, quantitative analysis, and critical thinking. Student grades suggest significant performance benefits, and engagement data suggests positive student attitude despite the perceived challenges of the program. We also note a particular performance benefit to students who repeatedly engage in the program. This framework may carry benefits in a wide variety of settings and disciplines for enhancing student engagement and performance, particularly in non-specialized school environments.

Study design and implementation

Participants in Discovery include all students enrolled in university-stream Grade 11 or 12 biology, chemistry, or physics at the participating school over five consecutive terms (cohort summary shown in Table 1 ). Although student participation in educational content was mandatory, student grades and survey responses (administered by high school teachers) were collected from only those students with parent or guardian consent. Teachers replaced each student name with a unique coded identifier to preserve anonymity but enable individual student tracking over multiple terms. All data collected were analyzed without any exclusions save for missing survey responses; no power analysis was performed prior to data collection.

Ethics statement

This study was approved by the University of Toronto Health Sciences Research Ethics Board (Protocol # 34825) and the Toronto District School Board External Research Review Committee (Protocol # 2017-2018-20). Written informed consent was collected from parents or guardians of participating students prior to the acquisition of student data (both post-hoc academic data and survey administration). Data were anonymized by high school teachers for maintenance of academic confidentiality of individual students prior to release to U of T researchers.

Educational program overview

Students enrolled in university-preparatory STEM classes at the participating school completed a term-long project under the guidance of graduate student instructors and undergraduate student mentors as a mandatory component of their respective course. Project curriculum developed collaboratively between graduate students and participating high school teachers was delivered within U of T Faculty of Applied Science & Engineering (FASE) teaching facilities. Participation allows high school students to garner a better understanding as to how undergraduate learning and career workflows in STEM vary from traditional high school classroom learning, meanwhile reinforcing the benefits of problem solving, perseverance, teamwork, and creative thinking competencies. Given that Discovery was a mandatory component of course curriculum, students participated as class cohorts and addressed questions specific to their course subject knowledge base but related to the defined global health research topic (Fig. 1 ). Assessment of program deliverables was collectively assigned to represent 10–15% of the final course grade for each subject at the discretion of the respective STEM teacher.

The Discovery program framework was developed, prior to initiation of student assessment, in collaboration with one high school selected from the local public school board over a 1.5 year period of time. This partner school consistently scores highly (top decile) in the school board’s Learning Opportunities Index (LOI). The LOI ranks each school based on measures of external challenges affecting its student population therefore schools with the greatest level of external challenge receive a higher ranking 35 . A high LOI ranking is inversely correlated with socioeconomic status (SES); therefore, participating students are identified as having a significant number of external challenges that may affect their academic success. The mandatory nature of program participation was established to reach highly capable students who may be reluctant to engage on their own initiative, as a means of enhancing the inclusivity and impact of the program. The selected school partner is located within a reasonable geographical radius of our campus (i.e., ~40 min transit time from school to campus). This is relevant as participating students are required to independently commute to campus for Discovery hands-on experiences.

Each program term of Discovery corresponds with a five-month high school term. Lead university trainee instructors (3–6 each term) engaged with high school teachers 1–2 months in advance of high school student engagement to discern a relevant overarching global healthcare theme. Each theme was selected with consideration of (a) topics that university faculty identify as cutting-edge biomedical research, (b) expertise that Discovery instructors provide, and (c) capacity to showcase the diversity of BME. Each theme was sub-divided into STEM subject-specific research questions aligning with provincial Ministry of Education curriculum concepts for university-preparatory Biology, Chemistry, and Physics 9 that students worked to address, both on-campus and in-class, during a term-long project. The Discovery framework therefore provides students a problem-based learning experience reflective of an engineering capstone design project, including a motivating scientific problem (i.e., global topic), subject-specific research question, and systematic determination of a professional recommendation addressing the needs of the presented problem.

Discovery instructors were volunteers recruited primarily from graduate and undergraduate BME programs in the FASE. Instructors were organized into subject-specific instructional teams based on laboratory skills, teaching experience, and research expertise. The lead instructors of each subject (the identified 1–2 trainees that built curriculum with high school teachers) were responsible to organize the remaining team members as mentors for specific student groups over the course of the program term (~1:8 mentor to student ratio).

All Discovery instructors were familiarized with program expectations and trained in relevant workspace safety, in addition to engagement at a teaching workshop delivered by the Faculty Advisor (a Teaching Stream faculty member) at the onset of term. This workshop was designed to provide practical information on teaching and was co-developed with high school teachers based on their extensive training and experience in fundamental teaching methods. In addition, group mentors received hands-on training and guidance from lead instructors regarding the specific activities outlined for their respective subject programming (an exemplary term of student programming is available in S 2 Appendix) .

Discovery instructors were responsible for introducing relevant STEM skills and mentoring high school students for the duration of their projects, with support and mentorship from the Faculty Mentor. Each instructor worked exclusively throughout the term with the student groups to which they had been assigned, ensuring consistent mentorship across all disciplinary components of the project. In addition to further supporting university trainees in on-campus mentorship, high school teachers were responsible for academic assessment of all student program deliverables (Fig. 1 ; the standardized grade distribution available in S 3 Appendix ). Importantly, trainees never engaged in deliverable assessment; for continuity of overall course assessment, this remained the responsibility of the relevant teacher for each student cohort.

Throughout each term, students engaged within the university facilities four times. The first three sessions included hands-on lab sessions while the fourth visit included a culminating symposium for students to present their scientific findings (Fig. 1 ). On average, there were 4–5 groups of students per subject (3–4 students per group; ~20 students/class). Discovery instructors worked exclusively with 1–2 groups each term in the capacity of mentor to monitor and guide student progress in all project deliverables.

After introducing the selected global research topic in class, teachers led students in completion of background research essays. Students subsequently engaged in a subject-relevant skill-building protocol during their first visit to university teaching laboratory facilities, allowing opportunity to understand analysis techniques and equipment relevant for their assessment projects. At completion of this session, student groups were presented with a subject-specific research question as well as the relevant laboratory inventory available for use during their projects. Armed with this information, student groups continued to work in their classroom setting to develop group-specific experimental plans. Teachers and Discovery instructors provided written and oral feedback, respectively , allowing students an opportunity to revise their plans in class prior to on-campus experimental execution.

Once at the relevant laboratory environment, student groups executed their protocols in an effort to collect experimental data. Data analysis was performed in the classroom and students learned by trial & error to optimize their protocols before returning to the university lab for a second opportunity of data collection. All methods and data were re-analyzed in class in order for students to create a scientific poster for the purpose of study/experience dissemination. During a final visit to campus, all groups presented their findings at a research symposium, allowing students to verbally defend their process, analyses, interpretations, and design recommendations to a diverse audience including peers, STEM teachers, undergraduate and graduate university students, postdoctoral fellows and U of T faculty.

Data collection

Teachers evaluated their students on the following associated deliverables: (i) global theme background research essay; (ii) experimental plan; (iii) progress report; (iv) final poster content and presentation; and (v) attendance. For research purposes, these grades were examined individually and also as a collective Discovery program grade for each student. For students consenting to participation in the research study, all Discovery grades were anonymized by the classroom teacher before being shared with study authors. Each student was assigned a code by the teacher for direct comparison of deliverable outcomes and survey responses. All instances of “Final course grade” represent the prorated course grade without the Discovery component, to prevent confounding of quantitative analyses.

Survey instruments were used to gain insight into student attitudes and perceptions of STEM and post-secondary study, as well as Discovery program experience and impact (S 4 Appendix ). High school teachers administered surveys in the classroom only to students supported by parental permission. Pre-program surveys were completed at minimum 1 week prior to program initiation each term and exit surveys were completed at maximum 2 weeks post- Discovery term completion. Surveys results were validated using a principal component analysis (S 1 Appendix , Supplementary Fig. 2 ).

Identification and comparison of population subsets

From initial analysis, we identified two student subpopulations of particular interest: students who performed ≥1 SD [18.0%] or greater in the collective Discovery components of the course compared to their final course grade (“EE”), and students who participated in Discovery more than once (“MT”). These groups were compared individually against the rest of the respective Discovery population (“non-EE” and “non-MT”, respectively ). Additionally, MT students who participated in three or four (the maximum observed) terms of Discovery were assessed for longitudinal changes to performance in their course and Discovery grades. Comparisons were made for all Discovery deliverables (introductory essay, client meeting, proposal, progress report, poster, and presentation), final Discovery grade, final course grade, Discovery attendance, and overall attendance.

Statistical analysis

Student course grades were analyzed in all instances without the Discovery contribution (calculated from all deliverable component grades and ranging from 10 to 15% of final course grade depending on class and year) to prevent correlation. Aggregate course grades and Discovery grades were first compared by paired t-test, matching each student’s course grade to their Discovery grade for the term. Student performance in Discovery ( N  = 268 instances of student participation, comprising 170 individual students that participated 1–4 times) was initially assessed in a linear regression of Discovery grade vs. final course grade. Trends in course and Discovery performance over time for students participating 3 or 4 terms ( N  = 16 and 3 individuals, respectively ) were also assessed by linear regression. For subpopulation analysis (EE and MT, N  = 99 instances from 81 individuals and 174 instances from 76 individuals, respectively ), each dataset was tested for normality using the D’Agostino and Pearson omnibus normality test. All subgroup comparisons vs. the remaining population were performed by Mann–Whitney U -test. Data are plotted as individual points with mean ± SEM overlaid (grades), or in histogram bins of 1 and 4 days, respectively , for Discovery and class attendance. Significance was set at α ≤ 0.05.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The data that support the findings of this study are available upon reasonable request from the corresponding author DMK. These data are not publicly available due to privacy concerns of personal data according to the ethical research agreements supporting this study.

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Acknowledgements

This study has been possible due to the support of many University of Toronto trainee volunteers, including Genevieve Conant, Sherif Ramadan, Daniel Smieja, Rami Saab, Andrew Effat, Serena Mandla, Cindy Bui, Janice Wong, Dawn Bannerman, Allison Clement, Shouka Parvin Nejad, Nicolas Ivanov, Jose Cardenas, Huntley Chang, Romario Regeenes, Dr. Henrik Persson, Ali Mojdeh, Nhien Tran-Nguyen, Ileana Co, and Jonathan Rubianto. We further acknowledge the staff and administration of George Harvey Collegiate Institute and the Institute of Biomedical Engineering (IBME), as well as Benjamin Rocheleau and Madeleine Rocheleau for contributions to data collation. Discovery has grown with continued support of Dean Christopher Yip (Faculty of Applied Science and Engineering, U of T), and the financial support of the IBME and the National Science and Engineering Research Council (NSERC) PromoScience program (PROSC 515876-2017; IBME “Igniting Youth Curiosity in STEM” initiative co-directed by DMK and Dr. Penney Gilbert). LDH and NIC were supported by Vanier Canada graduate scholarships from the Canadian Institutes of Health Research and NSERC, respectively . DMK holds a Dean’s Emerging Innovation in Teaching Professorship in the Faculty of Engineering & Applied Science, U of T.

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These authors contributed equally: Locke Davenport Huyer, Neal I. Callaghan.

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Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada

Locke Davenport Huyer, Neal I. Callaghan, Andrey I. Shukalyuk & Dawn M. Kilkenny

Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada

Locke Davenport Huyer

Translational Biology and Engineering Program, Ted Rogers Centre for Heart Research, University of Toronto, Toronto, ON, Canada

Neal I. Callaghan

George Harvey Collegiate Institute, Toronto District School Board, Toronto, ON, Canada

Sara Dicks, Edward Scherer & Margaret Jou

Institute for Studies in Transdisciplinary Engineering Education & Practice, University of Toronto, Toronto, ON, Canada

Dawn M. Kilkenny

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LDH, NIC and DMK conceived the program structure, designed the study, and interpreted the data. LDH and NIC ideated programming, coordinated execution, and performed all data analysis. SD, ES, and MJ designed and assessed student deliverables, collected data, and anonymized data for assessment. SD assisted in data interpretation. AIS assisted in programming ideation and design. All authors provided feedback and approved the manuscript that was written by LDH, NIC and DMK.

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Correspondence to Dawn M. Kilkenny .

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Davenport Huyer, L., Callaghan, N.I., Dicks, S. et al. Enhancing senior high school student engagement and academic performance using an inclusive and scalable inquiry-based program. npj Sci. Learn. 5 , 17 (2020). https://doi.org/10.1038/s41539-020-00076-2

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research paper on secondary education

Gender Segregation in Education: Evidence From Higher Secondary Stream Choice in India

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Soham Sahoo , Stephan Klasen; Gender Segregation in Education: Evidence From Higher Secondary Stream Choice in India. Demography 1 June 2021; 58 (3): 987–1010. doi: https://doi.org/10.1215/00703370-9101042

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This paper investigates gender-based segregation across different fields of study at the senior secondary level of schooling in a large developing country. We use a nationally representative longitudinal data set from India to analyze the extent and determinants of gender gap in higher secondary stream choice. Using fixed-effects regressions that control for unobserved heterogeneity at the regional and household levels, we find that girls are about 20 percentage points less likely than boys to study in science (STEM) and commerce streams as compared with humanities. This gender disparity is unlikely to be driven by gender-specific differences in cognitive ability, given that the gap remains large and significant even after we control for individuals' past test scores. We establish the robustness of these estimates through various sensitivity analyses: including sibling fixed effects, considering intrahousehold relationships among individuals, and addressing sample selection issues. Disaggregating the effect on separate streams, we find that girls are most underrepresented in the study of science. Our findings indicate that gender inequality in economic outcomes, such as occupational segregation and gender pay gaps, is determined by gendered trajectories set much earlier in the life course, especially at the school level.

  • Introduction

Various forms of gender inequality are observed in different parts of the world. In South Asia, such inequalities have manifested throughout the life course of individuals: sex imbalance at birth due to sex-selective abortions, unequal survival rates, differential human capital investments, discrimination in the labor market, and so on. The last few decades, however, have seen progress toward gender equality, most notably in education. Although the gender gap in enrollment rates at all levels of education has diminished, it has not translated into a commensurate improvement in women's labor market outcomes. For instance, female labor force participation, which is viewed as one of the important indicators of inclusive development and female economic empowerment, has remained very low and stagnant and has sometimes declined in India, despite the nation's rapid economic growth, female educational expansion, and fertility decline in the last two decades ( Klasen and Pieters 2015 ). Underparticipation of women will restrict the country from properly utilizing its demographic gift of having a high proportion of the population at working ages. Additionally, occupational and sectoral segregation of employment by gender is remarkably persistent and is a key issue behind perpetuating female disadvantage, such as the gender pay gap in the labor market ( Borrowman and Klasen 2020 ).

Against this backdrop, we show here that the gender gap in economic participation in adulthood in India is shaped by gendered trajectories set earlier in life, especially at the school level. Specifically, we identify the gender gap in science, technology, engineering, and mathematics (STEM) and commerce-related fields at the senior secondary level of education, which is likely to have a significant and far-reaching effect on individuals' adult-life outcomes.

The literature has analyzed the determinants and consequences of stream choice at the postsecondary and tertiary levels of education ( Arcidiacono 2004 ; Beffy et al. 2012 ; Fuller et al. 1982 ). Some studies have also recognized the link between educational segregation and occupational segregation and have reflected on the life course processes that determine women's career trajectories relative to men's ( Schneeweis and Zweimüller 2012 ; Xie and Shauman 2003 ). However, the causes of gender gap in stream choice are not fully understood, and they constitute an area of active research in the literature ( Kahn and Ginther 2017 ; Xie et al. 2015 ). In addition, barring a few exceptions, most of this research has focused on developed countries. 1

By investigating this issue in the context of an emerging economy, we contribute to the literature in various ways. First, India offers an important case study given the importance of STEM education in its economy. Over the last few decades after economic liberalization, the growth of the Indian economy has been led by the services sector, where the information technology–related industry has been a prime contributor ( Panagariya 2004 ). The nature of economic growth during this period has potentially had a spillover effect on education participation ( Jensen 2012 ; Oster and Steinberg 2013 ; Shastry 2012 ). India, along with China, has accounted for the majority of the world's recent STEM graduates, constituting a much larger share than the European Union and the United States ( UNCTAD 2018 ). Yet, the gender composition of these STEM students and their labor market prospects have remained unexplored in the literature.

Cross-country studies have shown variation in the overall levels and patterns of sex segregation in stream choice (e.g., Charles and Bradley 2009 ), indicating that the processes that generate this form of gender inequality in advanced and developing nations may be distinct. This set of findings justifies an empirical analysis in a developing country setting, such as India, where the prevalence of male-favoring gender norms affects individual decisions at various life stages. In a society where economic and cultural factors drive underinvestment in girls' education ( Azam and Kingdon 2013 ; Kingdon 2005 ), the magnitude and determinants of gender gap in STEM participation might be different from those in more gender-egalitarian countries. Moreover, from a demographic point of view, the Indian scenario differs from developed countries in its very low and stagnating female labor force participation. In this context, although a growing body of literature has examined the role of education in female labor force participation, it has focused on the level of education and not the type of education ( Klasen and Pieters 2015 ; Sarkar et al. 2019 ). The current paper contributes to this discourse by highlighting the persistence of gender segregation of academic fields despite improvements in female education levels.

The Indian education system also has distinct structural features that imply greater importance of stream choice made at the school level. After secondary schooling, completed after 10 years of education, students entering at the higher secondary level (lasting another two years) must specialize in one of the following streams: humanities, science, commerce, engineering/vocational, and other. 2 Unlike many developed countries, including the United States, stream choice at the tertiary level in India is made before admission to college, and because of eligibility requirements, this choice is largely determined by the stream studied at the higher secondary level. 3 Therefore, school-level stream choice is a crucial juncture in an individual's career because it determines the subsequent course of study at the college level and the nature of jobs that the individual may obtain in the future. Thus, the life course approach proposed by Xie and Shauman (2003) is especially relevant in the context of India, where the prevailing education system implies that choices made in adolescence affect adult-life outcomes. In fact, Sahoo and Klasen (2018) showed that stream choice at the higher secondary level in India strongly influences later labor market outcomes, including participation, occupational choice, and earnings.

Another contribution of this study is its exploration of the gender gap in the commerce stream, which is equivalent to a business major. Although a vast literature has focused on the gender gap in STEM, the gender gap in business studies is less explored. Analyzing trends in college major choice in the United States, Gemici and Wiswall (2014) found that women are significantly less likely than men to choose a business major, despite the documented overall rise in women's participation in tertiary education ( Goldin et al. 2006 ). We extend this literature by investigating gender disparity in the choice of the commerce stream at the higher secondary school level in India.

We use a nationally representative household-level panel data set that tracks the same households and individual members at two time points: 2005 and 2012. The novelty of this survey is that it asks all individuals about their performance in the secondary school leaving certificate (SSLC) examination and subsequently asks what stream they studied at the higher secondary level. Additionally, individuals aged 15–18 years (the ages corresponding to higher secondary schooling) in 2012 can be matched with information on their prior skills in mathematics, reading, and writing from an independent test conducted in the earlier round of survey in 2005. Thus, we have a unique setting to investigate individuals' higher secondary stream choice after controlling for their past academic performances, which serve as reasonable proxies of their cognitive ability.

Estimating fixed-effects regression models, we find a significant gender disparity of about 20 percentage points in the choice of nonhumanities streams (i.e., STEM and commerce) at the higher secondary level among youth aged 15–18 years. In addition to a rich set of covariates, we account for unobserved heterogeneity at the regional and household levels by including fixed effects in the regression. The gender gap remains unchanged even after we control for SSLC exam performance and lagged test scores from the previous survey. We establish the robustness of the estimates by considering the intrahousehold relationships of individuals, estimating sibling fixed-effects models, and addressing sample selection issues using an inverse probability weighting (IPW) framework.

We further investigate the determinants of gender difference in stream choice. Given the persistence of the gender gap even after we take into account the effect of cognitive ability as measured by past exam performance and test scores, we explore the roles of other relevant characteristics. We find that the gender gap does not vary with household income, suggesting that gender-based sorting into different streams is equally prevalent in richer and poorer households. Rather, the gender gap is significantly reduced when there is greater educational parity between parents, captured by the difference in education level between mother and father. We also show that better access to STEM-related education benefits girls more than boys, thus narrowing the gender gap. Additionally, investigating the choice of separate study tracks, we show that the pro-male gender bias is largest in science, followed by commerce and engineering/vocational streams.

  • Background and Related Literature

The last few decades have seen considerable progress in bridging the gender gap in educational attainment around the developing world. At the same time, trends in female labor force participation have been rather uneven, with South Asia actually experiencing declining female labor force participation rates ( Klasen 2019 ). Moreover, women have continued to be employed predominantly only in few sectors and occupations ( Borrowman and Klasen 2020 ). This perpetuating trend in occupational and sectoral segregation is a major reason for the persistence of the male-female earnings gap ( Blau and Kahn 2017 ). This pattern of gender stratification has also been found in the Indian labor market ( Duraisamy and Duraisamy 2014 ).

India has experienced a major expansion in education provision, resulting in a significant rise in school enrollment of both boys and girls. The Indian education system has a common structure throughout the country: students progress through primary, middle, and secondary education in their first 10 years of schooling, followed by another two years of higher secondary schooling and subsequently three to five years of tertiary education. Data from the National Sample Survey (NSS) show that in the mid-1990s, the average enrollment rate among children in the age group corresponding to elementary (i.e. primary and middle) schooling was about 70%, with a gender gap of 10 percentage points favoring boys. Over the next 20 years, this enrollment rate increased to 93%, with the gender gap declining to only 2 percentage points. The same pattern is visible in secondary and higher secondary levels: over the last two decades, the enrollment rate increased from 50% to 77%, and the gender gap declined from 16 percentage points to 2 percentage points.

The first 10 years of education in India include a common, nonselective curriculum for all students. After that, each student enrolling in higher secondary level must specialize in a particular stream; most choose the humanities, science, or commerce stream, and a minority opt for other tracks, such as engineering or vocational education. After completing the higher secondary level, students who continue to tertiary education enroll in colleges for bachelor's and master's degrees in a chosen stream. A crucial aspect of the Indian education system is that stream choice at the higher secondary level largely determines subsequent major choice at the college level. Particularly, students who have studied in the humanities stream in higher secondary school are deemed ineligible for a STEM or commerce major in almost all colleges. Therefore, stream choice at the higher secondary level is an important decision in an individual's career because it drives the field choice at subsequent levels of education, which in turn affects labor market outcomes through occupational choice. National-level statistics from repeated cross-sectional surveys of the NSS show that the proportion of students enrolled in higher secondary level choosing humanities declined from 56% in 2007–2008 to 42% in 2014. In contrast, science enrollment increased from 31% to 39% during this period, and commerce enrollment increased from 13% to 16%. These aggregate statistics also reveal that girls have a higher propensity to study humanities than science or commerce, and boys are more likely than girls to study science ( Figure 1 ). This gender disparity in school-level stream choice also leads to subsequent gender gaps in undergraduate studies: the share of women in STEM is only 37%, and the share in commerce is 45% ( Government of India 2016 ).

The literature on postsecondary stream choice, mostly based on developed countries, highlights that educational choices at this level are closely linked to labor market outcomes. First, stream choice is affected by the expected future earnings from different streams ( Beffy et al. 2012 ; Boudarbat 2008 ). Second, such educational choices also cause much of the variation in earnings later in life ( Dustmann 2004 ; Joensen and Nielsen 2009 ). Specifically, evidence suggests that a STEM or business major yields higher returns than studying humanities ( Flabbi 2011 ). This pattern is corroborated in the Indian context when we compare the earnings distributions of individuals who studied STEM/commerce with those of individuals who studied humanities at the higher secondary level ( Figure 2 ).

Focusing on gender, studies have shown gender disparities in stream choice: girls are especially underrepresented in STEM at the postsecondary level of education in most countries ( Hill et al. 2010 ; World Bank 2012 ). The incidence of gender segregation in education and its relation to occupational segregation has also been explored using data from the United States and Europe ( Bieri et al. 2016 ; Daymont and Andrisani 1984 ; Eide 1994 ; Flabbi 2011 ; Van Puyenbroeck et al. 2012 ). These studies have found that men's and women's college major choice largely explains occupational choices and accounts for a significant part of the gender wage gap. For the case of India, Sahoo and Klasen (2018) found that, even with controls for exam results and household fixed effects, women who choose a STEM or commerce stream in higher secondary education have substantially higher chances of participating in the labor force, securing salaried employment, choosing a male-dominated occupation, and having higher earnings. The choice of STEM or commerce stream in turn leads to a reduction of gender gap within households in terms of all these economic outcomes. Also, among different streams, science appears to have the most significant effect.

One potential reason that girls are less likely to choose STEM subjects is that boys may have a comparative advantage in mathematics. Evidence shows that a male advantage in mathematics achievement starts manifesting in middle school and increases with age ( Bharadwaj et al. 2012 ; Kahn and Ginther 2017 ), but mathematical ability does not fully account for the gender gap in STEM choice ( Dickson 2010 ; Friedman-Sokuler and Justman 2016 ; Riegle-Crumb et al. 2012 ; Turner and Bowen 1999 ). Rather than inherent gender differences in cognitive ability, other societal, psychosocial, and preference-related factors play a larger role in explaining the underrepresentation of women in math-intensive STEM subjects ( Antecol and Cobb-Clark 2013 ; Buser et al. 2014 ; Zafar 2013 ). In fact, a large part of the observed gender gap can be attributed to the stereotypical beliefs about girls' mathematical ability and gendered preferences that are often shaped by cultural norms ( Charles and Bradley 2009 ; Kahn and Ginther 2017 ).

The salience of societal factors implies that contextual analysis is essential for understanding the incidence and determinants of gendered educational choices. In addition, the theoretical perspectives on the relationship between economic development and gender stratification in education do not always converge ( Hannum 2005 ). Modernization or neoclassical theory suggests that the expansion of market forces reduces discriminatory cultural practices that are linked to economic inefficiency, thereby reducing gender disparities in education ( Forsythe et al. 2000 ). On the other hand, Boserup (1970) hypothesized that inequality would first increase and then decrease in the process of development. Initially, men with better access to market opportunities may reap greater benefits of economic prosperity, and progress toward gender equality would be achieved as the structural transformation proceeds ( Lantican et al. 1996 ). Traditional institutions also mediate the effect of economic development on women's educational responses ( Munshi and Rosenzweig 2006 ). Particularly for school-age children, decisions are influenced by parents, who are likely to consider factors beyond labor market returns to education. In South Asia, these factors include dowry payment for daughters' marriage, a higher likelihood of receiving old-age support from sons than from daughters due to patrilocality, and gender norms about women's participation in activities outside the household ( Alderman and King 1998 ; Jayachandran 2015 ).

Our study contributes to the literature in two ways. First, we identify the pattern of gender segregation in stream choice in an emerging economy where such evidence has been lacking.

Second, we explore the plausible determinants of the gender disparity. Specifically, we analyze the role of cognitive ability, measured by past exam performance and test scores. In addition, we examine the influence of other pertinent factors in this context. Household income is likely to be a constraining factor for poorer students while choosing a STEM education, which is more costly to study than humanities. Indeed, the NSS data reveal that the average expenditure incurred by students in the science and commerce streams is more than twice the expenditure of those studying humanities at the higher secondary level. 4 Variation in household income may lead to gendered choices depending on whether resource constraints are binding and how son preference varies along with income ( Alderman and King 1998 ; Garg and Morduch 1998 ). Therefore, we investigate whether household income determines the gender difference in stream choice.

Another potential determinant we consider is parental education gap. A large literature has explored the intergenerational transmission of human capital, but this research has mostly analyzed the effect of parental education on children's years of schooling or grade progression rather than stream choice ( Holmlund et al. 2011 ). In addition, we introduce the gender dimension by focusing on the gap in educational attainment between mothers and fathers. We postulate that greater parity in parental education would induce equality in stream choice between boys and girls.

Finally, we consider access to STEM-related education, which is especially important in a developing country where students are often constrained by the availability of specific streams in the local schools. Reviewing the literature on several developing countries, Glick (2008) noted that access to education, despite being a gender-neutral factor, may disproportionately affect girls' participation. This possibility is plausible in the context of a patriarchal society like India, where strong gender norms may discourage adolescent girls from traveling long distances to attend school ( Muralidharan and Prakash 2017 ). Safety concerns may also dissuade girls from enrolling in their preferred stream if it involves traveling longer distances ( Borker 2017 ). Using regional variation in the availability of STEM colleges as a proxy for access, we test whether better access reduces the gender gap in stream choice.

  • Data Description

We use the India Human Development Survey (IHDS), a nationally representative, two-period longitudinal data set ( Desai et al. 2010 , 2015 ). 5 The first round of data was collected in 2004–2005 on 41,554 households in 1,503 villages and 971 urban neighborhoods across India. In 2011–2012, the second round of survey reinterviewed 83% of the same households; for households that could not be tracked, a replacement sample was used. Thus, the second round of survey covered 42,152 households across India. For brevity, we refer to the first round as 2005 data and the second round as 2012 data . IHDS is a multitopic survey collecting detailed information at the individual, household, and community levels. Our analysis mainly uses the sample from the 2012 survey and uses the 2005 survey to account for past characteristics of the same individuals.

We explore whether the choice of study stream exhibits a gender bias at the higher secondary level. In India, the official school entry age is 6 years, and the (lower) secondary level ends after 10 years of schooling. In the IHDS sample, the enrollment rate of children of secondary school age (14–15 years) is 87%, and the gender gap in the enrollment rate is only 2 percentage points. Because the higher secondary (or senior secondary) level consists of two years of schooling succeeding the secondary level, we concentrate on the sample of individuals who are in the corresponding age group of 15–18 years. 6 Information on stream choice at the higher secondary level is available only for individuals who have passed the secondary level and enrolled in the subsequent level of education. The secondary pass rate for our sample is 39.4% for males and 40.6% for females; a t test reveals that the gender difference in the secondary pass rate is not statistically significant. After we drop observations with missing values, the final analysis sample is 5,203 children.

The first step toward specialization begins at the higher secondary level of education, when students have to choose a stream mainly from the following options: arts/humanities, commerce, science, engineering/vocational, and others (e.g., home science, craft, and design). 7 Estimates from the IHDS data show patterns of stream choice that are similar to the national-level statistics around this period. Summary statistics presented in Table 1 show that 50% of students in the sample chose the humanities stream. The next most popular stream is science, followed by commerce, engineering/vocational, and others, the latter of which are chosen by very few. In the sample, 58% girls but only 41% boys chose humanities, indicating that girls are underrepresented in science, commerce, and engineering/vocational streams. Because these average differences may be confounded by various observable and unobservable factors that are correlated with both gender and stream choice, we next lay out an econometric model to identify the gender gap.

  • Empirical Model

We estimate a linear probability model where the dependent variable ( ⁠ S t r e a m i h v d k ⁠ ) is a binary indicator of whether an individual of higher secondary school age (15–18 years) has chosen to study stream k ⁠ , where k ∈ { H u m a n i t i e s ,   C o m m e r c e ,   S c i e n c e ,   E n g i n e e r i n g / V o c a t i o n a l ,   O t h e r } ⁠ . The subscripts i , h , v , and d (respectively) denote individual, household, village/town, and district. The main explanatory variable is an indicator variable ( ⁠ F e m a l e ⁠ ) denoting whether the individual is female. In addition, we control for individual-level covariates ( ⁠ X i h v d ⁠ ): age, birth order, number of siblings, mother's years of education, father's years of education, and dummy variables indicating relationship to the household head. Household-level covariates ( ⁠ Z h v d ⁠ ) include household size, wealth, dummy variables for social group (caste and religion), and whether the household is in a rural area. To control for regional characteristics, we first include fixed effects at the district level ( ⁠ μ d ⁠ ) and then the village/town level ( ⁠ φ v d ⁠ ). Inclusion of village/town fixed effects also helps us to control for access to education in the locality, which is important because some schools may not offer higher secondary education or may not offer all the streams at this level. Other regional characteristics, such as local labor market conditions and societal norms toward girls' education, are also subsumed by these fixed effects.

Because household-level factors, including unobserved tastes and preferences for different types of education, potentially affect the stream choice, we control for household-level heterogeneity by including household fixed effects ( ⁠ ϕ h v d ⁠ ) in an additional set of regressions. 8 This control is especially important in the context of India, where the household's unobserved preferences are correlated with gender inequality. For example, female children in India are often more likely to be found in larger families because fertility decisions are endogenously determined; parents keep having children until they have at least one boy ( Basu and de Jong 2010 ; Clark 2000 ; Yamaguchi 1989 ). If STEM education requires higher investments, then comparisons across households may artificially show a gender gap because girls belong to larger families, who invest less in the human capital of each child. For these and related reasons, studies investigating gender discrimination in educational investments have advocated using household fixed effects ( Jensen 2002 ; Kingdon 2005 ; Sahoo 2017 ). Although it includes household fixed effects, our model also takes into account the potential nonindependence of observations belonging to the same household by clustering the standard errors at the household level. 9

Gender differences in the choice of STEM education may be driven by girls' lower cognitive ability compared with boys, especially in mathematics. The literature on gender gaps in mathematics achievement suggests that most of the observed gap is explained by background factors ( Benbow and Stanley 1980 ; Nollenberger et al. 2016 ). In India, because of systematic and continual underinvestment in girls' human capital from early childhood, girls' cognitive ability may lag behind that of boys at the higher secondary level. A novel feature of our data is that they allow us to account for an individual's cognitive ability using two distinct measures.

The first measure of cognitive ability is given by the individual's performance in the secondary level board examination, which is potentially an important predictor of stream choice at the higher secondary level. In India, a standardized examination is conducted by the education board (at the state or national level) to which each school belongs. Every student must pass this examination and obtain the SSLC to be able to continue at higher secondary levels of education. The results of this examination are typically categorized into divisions 1, 2, and 3, in the declining order of the quality of grade obtained. We use this SSLC performance indicator to control for the individual's cognitive ability.

Furthermore, in the 2005 IHDS round, children who were aged 8–11 years were given cognitive tests on mathematics, reading, and writing ability. In the 2012 survey, these children are in the age group corresponding to the higher secondary level and are considered in the regression. Therefore, we are able to control for their past cognitive ability by including their performance on these tests. 10 Consequently, we control for achievement scores collected by two independent tests: one from the SSLC examination and the other conducted by IHDS enumerators in 2005. Hence, we believe that our regression adequately captures the differences in children's abilities and identifies the gender gap in stream choice.

A potential concern that remains is that stream choice is defined only for those individuals who have passed the secondary level and enrolled at the higher secondary level. In the age group considered, 40% of children passed the secondary level. These children are likely to be systematically different from those who have education below the secondary level. However, disaggregating this pass rate by boys and girls, we find that there is no gender gap in the secondary level pass rate. We also estimate a regression (see Table A2 , online appendix) similar to Eq. (1) but with the dependent variable being a binary indicator of whether a child has passed the secondary level (and hence is eligible for higher secondary stream choice). The coefficient on gender in this regression is almost always insignificant, and the magnitude is almost zero, suggesting that the probability of selecting into the sample for our main regression (stream choice) does not vary by gender. Hence, this selection is unlikely to confound the effect of gender in the regression of STEM/commerce stream choice.

Main Results

We begin by investigating the gender difference in the choice of STEM/commerce streams, combining science, engineering/vocational, and commerce into one category and comparing it with the humanities and other streams. The results, presented in Table 2 , show a statistically significant female disadvantage of about 20 percentage points in the choice of STEM/commerce streams compared with the humanities. This estimate remains stable across different specifications. Although all regressions include observable control variables and SSLC results to control for cognitive ability, we sequentially add fixed effects at the level of the district, village/town, and household. 11

Our final model further includes test scores from the 2005 survey. 12 Among all boys and girls, 50% study STEM/commerce streams; thus, the estimated gender gap translates into a magnitude of 40% of the mean participation, which is substantial. As expected, we find that students who scored better on the SSLC examination are more likely to study STEM/commerce at the higher secondary level. Students who in 2005 scored at the highest level of difficulty in mathematics (i.e., division) also have a higher probability of choosing these quantitative streams. 13 Because the estimate of the gender gap remains significant and stable even after we take into account the variation in cognitive abilities captured by two different measures, the gender gap in stream choice is unlikely to be driven by the intrinsic ability of students.

Robustness Analysis

Our main results reveal a gender gap in stream choice after we control for explanatory factors. We further investigate the intrahousehold differences in outcomes when we include household fixed effects in the analysis. In this section, we test whether our estimates of the intrahousehold gender gap remain robust after we take household structures into account.

First, we consider the relationship of individuals in the household more explicitly. In the sample of adolescents included in the analysis, 84% are children and 12% are grandchildren of the household heads. 14 To ensure that intrahousehold relationships do not confound the effect of gender, all the regressions control for dummy variables denoting an individual's relation to the household head. Moreover, we conduct a sensitivity analysis by restricting the comparison between individuals who are in a similar position within the household; in particular, we compare direct siblings by using a sibling fixed-effects model. 15 The observations pertaining to the siblings sharing the same parents may not be independent because the siblings are likely to have common unobservable characteristics. To address this issue, our model estimates cluster-robust standard errors, allowing the error terms to be correlated among siblings who share the same parents. Results presented in columns 1–2 of Table 3 reveal that the estimates remain almost unchanged in this analysis. In an additional exercise, we restrict the sample to sons and daughters of the household head and estimate the model. We again find a similar estimate of the gender gap, as shown in the last two columns of Table 3 . These analyses establish that the magnitude and precision of the estimated gender gap are not affected by the household structure and relationships among individuals in the household.

Next, we investigate whether the estimates from the household fixed-effects models are generalizable. Because the coefficients in these models are estimated using variation within households, observations belonging to households with multiple children contribute to this estimation. Moreover, for identification of the intrahousehold gender gap in stream choice, at least some of these households must have both multiple children and children of opposite gender. If the characteristics of these households systematically vary from those of the overall sample, then the estimates may not be generalizable. 16 To address this issue, we adopt IPW, which has been widely used in the literature in similar contexts ( Fitzgerald et al. 1998 ; Jones et al. 2006 ; Wooldridge 2010 ). This estimation technique follows two steps. In the first step, using our main sample of 5,203 adolescents, we model the probability of belonging to a household with multiple children, conditional on a set of covariates. These covariates include the observable explanatory variables used in Eq. (1) and their interaction with the gender dummy variable. In the second step, we use the inverse of these predicted probabilities as weights for the observations while estimating a household fixed-effects model restricting the sample to those households with multiple children. In another instance, we apply the IPW model for households with multiple children of opposite gender for the second step.

The findings of this robustness analysis are summarized graphically in Figure 3 , which juxtaposes the estimates that do not use IPW with those using IPW. We find that the point estimates and the confidence intervals are remarkably similar even after we use IPW to correct for any potential nonrandom selection of households when fixed effects are used. This analysis bolsters our main results and indicates that the estimated gender gap is robust to the issue of sample selection.

Heterogeneity Analysis Exploring the Determinants of the Gender Gap

To explore what drives the gender gap in stream choice, we augment our main empirical model (i.e., Eq. (1) ) by including interaction terms of gender with some key explanatory factors. Estimating how the effect of gender varies along with these factors sheds light on the underlying determinants of the gender gap. We investigate variations with respect to factors that have high contextual relevance: household affluence, parental educational parity, and access to STEM education. The first two factors are related to the demand for education, and the third factor reflects the supply of education, which is also policy-relevant.

Studying STEM or related streams likely involves a higher cost, which wealthier households are better able to pay ( Chandrasekhar et al. 2019 ). Indian households are also likely to make greater educational investments on boys ( Azam and Kingdon 2013 ; Kingdon 2005 ). Therefore, the higher cost of STEM-related education may discourage households from enrolling girls in such streams, especially when households have limited resources for children's education. To check whether resource constraint leads to gender disparity, we interact the gender dummy variable in our model with household income (per capita). We mitigate the potential endogeneity in household income by using baseline income from the earlier round rather than contemporaneous income. As revealed in Table 4 , household income has no significant effect on the gender gap in stream choice, although households with higher income are more likely to enroll boys in the STEM/commerce streams. 17 Thus, we find that the gender gap is quite pervasive, given that it is observed both in richer and poorer households. This result implies that either resource constraint is relatively less crucial than other determinants of the gender gap, or the gendered preference concerning stream choice does not change with respect to household income.

Because the decision of stream choice is made in adolescence, parents are likely to influence it ( Alderman and King 1998 ; Dustmann 2004 ). In a patriarchal society like India, parental attitudes toward gender equality in education are likely to affect the study choice of girls vis-à-vis boys. To capture this aspect, we next consider parental educational parity, as defined by the difference in years of education between the mother and father. Because mothers usually have lower levels of education than fathers, a greater parity implied by relatively higher education of the mother may reduce the gender disparity in their children's education. By interacting the female dummy variable with parental educational parity in our model, we find support for this hypothesis. On average, a mother has 1.7 fewer years of education than a father; when educational attainment between the parents is equal, it reduces the gender gap in their children's STEM/commerce stream choice by 2.2 percentage points (column 4, Table 4 ).

Another pertinent question from the supply side of education is whether the gender disparity declines when STEM-related education is made more accessible. Although various government policies over the last few decades have universalized access to education at the elementary levels, access to higher secondary education still varies substantially. In addition, educational institutions that offer higher secondary level grades may not offer all the streams. In many places, students have to travel long distances to study their desired stream, especially science or commerce. 18 Although any such variation in access to education is captured by village/town fixed effects in our model, access may have a differential effect on girls than boys. To estimate the differential effect of access by gender, we interact with gender a variable that measures the total number of science and technical colleges in the district at the time the stream choice was made. 19 The results show that districts with a higher number of colleges providing science or technical education have a smaller gender gap in stream choice (columns 5–6, Table 4 ). A 1 standard deviation increase in the number of science/technical colleges per 1 million population in the district is associated with a reduction of 7 percentage points in the gender gap in higher secondary stream choice.

Gender Gap in the Choice of Individual Streams

We also estimate a linear probability model given by Eq. (1) separately for each stream. Table 5 presents results that include village/town fixed effects (panel A) and household fixed effects (panel B). Girls are 20 percentage points more likely than boys to study humanities, as estimated from both models. Underrepresentation of girls is most prominent in science (8.5–10 percentage points), followed by commerce (6–8 percentage points) and engineering/vocational education (about 3.5 percentage points). Ability sorting is also significant across streams: the humanities stream seems to attract students with worse grades in SSLC exam results, whereas science attracts the best performing students. Nonetheless, the effect of gender remains significant even after we take the effect of ability into account.

Our study provides quantitative evidence on the gender segregation in higher secondary stream choice in an emerging economy. In addition to showing that girls are substantially underrepresented in STEM and commerce streams as compared with the humanities, we shed light on the plausible determinants of the gender gap. Our findings are based on data from India, which accounts for a large share of the world's STEM graduates, thus expanding on the literature, which has focused mostly on developed countries. Also, by reflecting on the underlying processes that cause the gender gap, our findings have implications beyond the Indian setting.

A recent international comparison of test scores in mathematics and science found significant heterogeneity in the relative performance of girls versus boys across different countries ( UNESCO 2017 ). Because of the importance of math skills in STEM fields, one may presume that the extent of the gender gap in math performance would predict an underrepresentation of women in STEM fields. However, we show that variations in cognitive skills, as measured by prior exam performance and math test scores, do not subsume the effect of gender on stream choice. In addition, we show that the gender gap in STEM/commerce participation is equally prevalent among richer and poorer households, in line with the pervasiveness of women's underrepresentation in STEM fields across societies with varying levels of economic prosperity. Our results imply that individual performance or household affluence need not be the main determining factor behind gendered educational choices, and it is necessary to consider other background and societal factors in this context.

Exploring the role of other factors, we show that parental educational parity helps to reduce the gender gap in STEM education. This result underscores the influence of parents, especially in settings where streams are chosen at an early age. Unlike the United States, many European countries require students to choose a field of study in secondary school (e.g., see Dustmann [2004] for Germany; see Dahl et al. [2020] for Sweden). That gender parity in parental education encourages girls to pursue a male-dominated field indicates an intergenerational transmission of gender attitude toward education. However, intergenerational mobility may be limited if parental background determines the education choice of the next generation, as shown by Dustmann (2004) in the context of Germany. Hence, there is scope for policies to play an instrumental role in bridging the gender gap by providing equal opportunities to boys and girls. As our results highlight, one avenue through which policies can be effective is by increasing the number of local educational institutions offering STEM and commerce streams, especially in underserved areas. Such an approach is particularly relevant for developing countries, where girls may be disproportionately affected by the lack of access to STEM education.

It is important to point out that there may be other potential determinants that we have not examined here because appropriate data are not available. These factors include individual preferences or behavioral traits; for instance, gender differences in competitiveness may explain the gender gap in STEM choice ( Buser et al. 2014 ). Teachers may also influence stream choice, but without matched teacher-student data, it is not possible to analyze this aspect. The labor market opportunities for women studying different streams can be another relevant determinant. Investigation of these additional determinants constitutes an agenda for future research.

  • Acknowledgments

We are grateful to the editors and four anonymous reviewers of Demography for their constructive comments that helped us to improve the paper. We thank the participants of GrOW Workshop 2016 at Stellenbosch University, Contemporary Issues in Development Economics Conference 2016 at Jadavpur University, PEGNet Conference 2017 at ETH Zurich, GREThA International Conference on Economic Development 2018 at University of Bordeaux, Sustainability and Development Conference 2018 at University of Michigan, and CSAE Conference 2019 at University of Oxford for helpful comments. We also thank Rahul Lahoti, Abhiroop Mukhopadhyay, Nishith Prakash, Sudipa Sarkar, and Hema Swaminathan for helpful discussions. We gratefully acknowledge funding from the Growth and Economic Opportunities for Women (GrOW) initiative, a multifunder partnership between the United Kingdom's Department for International Development, the Hewlett Foundation, and the International Development Research Centre.

An exception is Sookram and Strobl (2009) , who analyzed this topic for Trinidad and Tobago.

We categorize science and engineering/vocational as STEM. Subjects like accountancy and finance that involve mathematical tools are included in the commerce stream. Hence, some of our comparisons in this paper involve humanities versus nonhumanities, including STEM and commerce. We also report analysis for each stream separately.

Estimates from the data we use suggest that 93% and 85% of students who are currently studying, respectively, engineering and science in college studied a STEM stream at the higher secondary level. Among students studying humanities in college, 85% studied humanities in higher secondary school as well.

The difference in expenditure is mainly driven by higher school fees and private tutoring costs. Compared with humanities students, students in the science and commerce streams pay, respectively, 2.7 and 2.5 times more on school fees and 2.9 and 2.2 times more on private tutoring. Science and commerce students also incur marginally higher expenses on books, school supplies, and transportation, although these expenses are relatively smaller in proportion to the total expenditure.

The IHDS was carried out jointly by the University of Maryland and the National Council of Applied Economic Research, New Delhi. The data set is publicly available. More details can be found online at https://ihds.umd.edu/ .

Strictly speaking, the ages corresponding to the higher secondary level should be 16–17 years. However, we include one year below and one year above this range to allow for the possibility that some children may finish the secondary level earlier or later. The enrollment rate among children aged 16–17 is 74%; however, many of them haven’t yet completed secondary-level schooling. In an alternative specification, we remove the age restriction and estimate the regression for all individuals enrolled in the higher secondary level; the results are unchanged.

Students choose from physics, chemistry, mathematics, and biology/computer science/economics in the science stream; business studies, accountancy, economics, and business mathematics in the commerce stream; and fields such as history, geography, political science, sociology, and philosophy in the humanities stream. In addition, all students study languages at the higher secondary level.

The inclusion of household fixed effects implies that only households with at least two individuals contribute to identification in this regression. To ensure that our estimates are not biased due to the selection of such households, we also present results from analyses excluding household fixed effects. To further address this issue, we check the sensitivity of our estimates using an inverse probability weighting technique in our robustness analysis.

We also check the robustness of the estimates by clustering the standard errors at the level of district and village/town in the earlier specifications. We thereby take into account any potential heteroskedasticity and correlation in the error terms within the clusters ( Angrist and Pischke 2009 : chapter 8).

The sample size is reduced substantially, by about 50%, when we control for past test scores from the previous round of the survey due to many missing values in the variables capturing past test scores (see Table 1 ). Some individuals (about 11% of the sample) could not be found in the 2012 survey, and others may have misreported their age in the previous survey, leading to missing values for test scores among this age group. Later, we show that our estimates are not driven by variations in sample size.

We provide additional estimates based on an intrahousehold comparison in the subsequent section on robustness analysis. The results are also robust to the inclusion of a control variable indicating whether there was a sibling who married and left the household (results not shown).

Although the sample size drops after the inclusion of past test scores, which are not available for the entire sample, a comparison yields no significant difference in key variables between the entire sample and the reduced sample. Also, if we estimate regressions from columns 1–3 ( Table 2 ) on the reduced sample, the estimates are almost unchanged. See Table A1 in the online appendix.

We do not find any significant effect of other measures of cognitive ability (i.e., reading and writing scores) on stream choice. This result is consistent with Arcidiacono’s (2004) finding that math ability was more important than verbal ability in explaining sorting into particular majors in the context of the United States.

In addition, 2% are nephews/nieces of the household head, and the sample reflects very few other relationships (each less than 1%), such as daughter-in-law, brother/sister, or other relatives.

These direct siblings share the same parents. In India, sometimes multiple families coreside in a household, forming an extended or joint family; hence children from multiple parents may be coresiding in a household. A sibling fixed-effects model controls for heterogeneity across different parents within a household; few such cases are found in the sample, however, given that 84% of the sample is formed by children of the household head.

A comparison of the key characteristics between the sample of households with multiple children and the whole sample is provided in Table A3 in the online appendix. For the samples in the stream choice analysis (i.e. 15- to 18-year-old adolescents enrolled in higher secondary schooling), there is no significant difference in the mean of the outcome variable (i.e., stream choice) across these households. However, some of the household characteristics predictably differ across these samples, although the differences are not large.

We find similar results if instead of household income we use household wealth measured by durable assets.

Data from the 2014 NSS show that in rural areas, 43% and 41% of girls in, respectively, the science and commerce streams have to travel more than 5 kilometers to reach their schools; 30% of girls who study humanities travel more than 5 kilometers for school.

We use data from the All India Survey of Higher Education to construct this variable. The measure is lagged with respect to the individual’s stream choice decision and is normalized by the population of the district.

Supplementary data

Data & figures.

Fig. 1 National-level statistics on stream enrollment at the higher secondary level, by gender. Source: Authors' calculations using National Sample Survey (NSS) data 2007–2008 and 2014. All individuals ages 15–18 years who are enrolled at the higher secondary level are considered. The 2007–2008 NSS reported only the main three streams because of very low enrollment in the “other” category, which is included in 2014 data.

National-level statistics on stream enrollment at the higher secondary level, by gender. Source: Authors' calculations using National Sample Survey (NSS) data 2007–2008 and 2014. All individuals ages 15–18 years who are enrolled at the higher secondary level are considered. The 2007–2008 NSS reported only the main three streams because of very low enrollment in the “other” category, which is included in 2014 data.

Fig. 2 Kernel density estimates of log of annual and hourly earnings of individuals aged 25–60 years, by higher secondary stream choice and gender. Source: Authors' calculations using 2011–2012 IHDS data. All working-age individuals (ages 25–60 years) are considered for these plots.

Kernel density estimates of log of annual and hourly earnings of individuals aged 25–60 years, by higher secondary stream choice and gender. Source: Authors' calculations using 2011–2012 IHDS data. All working-age individuals (ages 25–60 years) are considered for these plots.

Fig. 3 Household fixed-effects (FE) estimates addressing potential sample selection using inverse probability weighting (IPW). Household FE Model 1 considers households with multiple children ages 15–18. Household FE Model 2 further restricts the sample to households with multiple children of the opposite gender age 15–18. The coefficient on female is reported from regressions that control for age, birth order, number of siblings, parental education, relationship with household head, and measures for ability. The ability measure includes secondary exam results in one set of regressions, and both secondary exam results and past test scores in another set of estimations. Two types of estimates are reported for comparison: estimates with and those without inverse probability weights (IPW). The table also shows 95% confidence intervals along with the point estimate for the coefficient on gender from the regressions.

Household fixed-effects (FE) estimates addressing potential sample selection using inverse probability weighting (IPW). Household FE Model 1 considers households with multiple children ages 15–18. Household FE Model 2 further restricts the sample to households with multiple children of the opposite gender age 15–18. The coefficient on female is reported from regressions that control for age, birth order, number of siblings, parental education, relationship with household head, and measures for ability. The ability measure includes secondary exam results in one set of regressions, and both secondary exam results and past test scores in another set of estimations. Two types of estimates are reported for comparison: estimates with and those without inverse probability weights (IPW). The table also shows 95% confidence intervals along with the point estimate for the coefficient on gender from the regressions.

Summary statistics of variables from the estimation sample (15- to 18-year-olds)

Sources: IHDS data, 2011–2012. Past scores are obtained from IHDS data, 2004–2005.

Effect of gender on higher secondary stream choice of STEM/commerce (vs. humanities)

Notes: The results are from linear probability models for adolescents ages 15–18. All regressions control for age, birth order, number of siblings, parental education, dummy variables indicating relationship with household head, household size, household wealth, caste dummy variables, religion dummy variables, and whether household lives in a rural area. The regression in column 4 additionally controls for past reading scores (dummy variables are included to denote whether the person can read a word, paragraph, or story) and writing score (whether the person can write). The third division is the omitted category for secondary result. Robust standard errors, clustered at the level of fixed effects (district in column 1, village/town in column 2, and household in columns 3 and 4), are shown in parentheses.

* p  < .05; ** p  < .01; *** p  < .001

Robustness analysis of the effect of gender on higher secondary stream choice of STEM/commerce (vs. humanities)

Notes: The results are from linear probability models taking adolescents ages 15–18. All regressions include control variables, as specified in Table 2 . Regressions in columns 2 and 4 additionally control for past reading scores. Robust standard errors, clustered at the level of fixed effects (sibling in columns 1 and 2 and household in columns 3 and 4), are shown in parentheses.

† p  < .10; * p  < .05; ** p  < .01; *** p  < .001

Heterogeneous effect of gender on higher secondary stream choice of STEM/commerce (vs. humanities)

Notes: The results are from a linear probability model for adolescents ages 15–18. All regressions include control variables, as specified in Table 2 . Robust standard errors, clustered at the level of fixed effects (village/town shown in columns 1, 3, and 5; household shown in columns 2, 4, and 6), are shown in parentheses.

Effect of gender on choice of stream at higher secondary level

Notes: The results are from a linear probability model for adolescents ages 15–18. All regressions include control variables, as specified in Table 2 . Robust standard errors, clustered at the level of fixed effects (village/town in panel A and household in panel B), are shown in parentheses.

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Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: A literature review

Stella timotheou.

1 CYENS Center of Excellence & Cyprus University of Technology (Cyprus Interaction Lab), Cyprus, CYENS Center of Excellence & Cyprus University of Technology, Nicosia-Limassol, Cyprus

Ourania Miliou

Yiannis dimitriadis.

2 Universidad de Valladolid (UVA), Spain, Valladolid, Spain

Sara Villagrá Sobrino

Nikoleta giannoutsou, romina cachia.

3 JRC - Joint Research Centre of the European Commission, Seville, Spain

Alejandra Martínez Monés

Andri ioannou, associated data.

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Digital technologies have brought changes to the nature and scope of education and led education systems worldwide to adopt strategies and policies for ICT integration. The latter brought about issues regarding the quality of teaching and learning with ICTs, especially concerning the understanding, adaptation, and design of the education systems in accordance with current technological trends. These issues were emphasized during the recent COVID-19 pandemic that accelerated the use of digital technologies in education, generating questions regarding digitalization in schools. Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses. Such results have engendered the need for schools to learn and build upon the experience to enhance their digital capacity and preparedness, increase their digitalization levels, and achieve a successful digital transformation. Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem, there is a need to show how these impacts are interconnected and identify the factors that can encourage an effective and efficient change in the school environments. For this purpose, we conducted a non-systematic literature review. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors that affect the schools’ digital capacity and digital transformation. The findings suggest that ICT integration in schools impacts more than just students’ performance; it affects several other school-related aspects and stakeholders, too. Furthermore, various factors affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the digital transformation process. The study results shed light on how ICTs can positively contribute to the digital transformation of schools and which factors should be considered for schools to achieve effective and efficient change.

Introduction

Digital technologies have brought changes to the nature and scope of education. Versatile and disruptive technological innovations, such as smart devices, the Internet of Things (IoT), artificial intelligence (AI), augmented reality (AR) and virtual reality (VR), blockchain, and software applications have opened up new opportunities for advancing teaching and learning (Gaol & Prasolova-Førland, 2021 ; OECD, 2021 ). Hence, in recent years, education systems worldwide have increased their investment in the integration of information and communication technology (ICT) (Fernández-Gutiérrez et al., 2020 ; Lawrence & Tar, 2018 ) and prioritized their educational agendas to adapt strategies or policies around ICT integration (European Commission, 2019 ). The latter brought about issues regarding the quality of teaching and learning with ICTs (Bates, 2015 ), especially concerning the understanding, adaptation, and design of education systems in accordance with current technological trends (Balyer & Öz, 2018 ). Studies have shown that despite the investment made in the integration of technology in schools, the results have not been promising, and the intended outcomes have not yet been achieved (Delgado et al., 2015 ; Lawrence & Tar, 2018 ). These issues were exacerbated during the COVID-19 pandemic, which forced teaching across education levels to move online (Daniel, 2020 ). Online teaching accelerated the use of digital technologies generating questions regarding the process, the nature, the extent, and the effectiveness of digitalization in schools (Cachia et al., 2021 ; König et al., 2020 ). Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses (Blaskó et al., 2021 ; Di Pietro et al, 2020 ). Such results have engendered the need for schools to learn and build upon the experience in order to enhance their digital capacity (European Commission, 2020 ) and increase their digitalization levels (Costa et al., 2021 ). Digitalization offers possibilities for fundamental improvement in schools (OECD, 2021 ; Rott & Marouane, 2018 ) and touches many aspects of a school’s development (Delcker & Ifenthaler, 2021 ) . However, it is a complex process that requires large-scale transformative changes beyond the technical aspects of technology and infrastructure (Pettersson, 2021 ). Namely, digitalization refers to “ a series of deep and coordinated culture, workforce, and technology shifts and operating models ” (Brooks & McCormack, 2020 , p. 3) that brings cultural, organizational, and operational change through the integration of digital technologies (JISC, 2020 ). A successful digital transformation requires that schools increase their digital capacity levels, establishing the necessary “ culture, policies, infrastructure as well as digital competence of students and staff to support the effective integration of technology in teaching and learning practices ” (Costa et al, 2021 , p.163).

Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem (Eng, 2005 ), there is a need to show how the different elements of the impact are interconnected and to identify the factors that can encourage an effective and efficient change in the school environment. To address the issues outlined above, we formulated the following research questions:

a) What is the impact of digital technologies on education?

b) Which factors might affect a school’s digital capacity and transformation?

In the present investigation, we conducted a non-systematic literature review of publications pertaining to the impact of digital technologies on education and the factors that affect a school’s digital capacity and transformation. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors which affect the schools’ digital capacity and digital transformation.

Methodology

The non-systematic literature review presented herein covers the main theories and research published over the past 17 years on the topic. It is based on meta-analyses and review papers found in scholarly, peer-reviewed content databases and other key studies and reports related to the concepts studied (e.g., digitalization, digital capacity) from professional and international bodies (e.g., the OECD). We searched the Scopus database, which indexes various online journals in the education sector with an international scope, to collect peer-reviewed academic papers. Furthermore, we used an all-inclusive Google Scholar search to include relevant key terms or to include studies found in the reference list of the peer-reviewed papers, and other key studies and reports related to the concepts studied by professional and international bodies. Lastly, we gathered sources from the Publications Office of the European Union ( https://op.europa.eu/en/home ); namely, documents that refer to policies related to digital transformation in education.

Regarding search terms, we first searched resources on the impact of digital technologies on education by performing the following search queries: “impact” OR “effects” AND “digital technologies” AND “education”, “impact” OR “effects” AND “ICT” AND “education”. We further refined our results by adding the terms “meta-analysis” and “review” or by adjusting the search options based on the features of each database to avoid collecting individual studies that would provide limited contributions to a particular domain. We relied on meta-analyses and review studies as these consider the findings of multiple studies to offer a more comprehensive view of the research in a given area (Schuele & Justice, 2006 ). Specifically, meta-analysis studies provided quantitative evidence based on statistically verifiable results regarding the impact of educational interventions that integrate digital technologies in school classrooms (Higgins et al., 2012 ; Tolani-Brown et al., 2011 ).

However, quantitative data does not offer explanations for the challenges or difficulties experienced during ICT integration in learning and teaching (Tolani-Brown et al., 2011 ). To fill this gap, we analyzed literature reviews and gathered in-depth qualitative evidence of the benefits and implications of technology integration in schools. In the analysis presented herein, we also included policy documents and reports from professional and international bodies and governmental reports, which offered useful explanations of the key concepts of this study and provided recent evidence on digital capacity and transformation in education along with policy recommendations. The inclusion and exclusion criteria that were considered in this study are presented in Table ​ Table1 1 .

Inclusion and exclusion criteria for the selection of resources on the impact of digital technologies on education

To ensure a reliable extraction of information from each study and assist the research synthesis we selected the study characteristics of interest (impact) and constructed coding forms. First, an overview of the synthesis was provided by the principal investigator who described the processes of coding, data entry, and data management. The coders followed the same set of instructions but worked independently. To ensure a common understanding of the process between coders, a sample of ten studies was tested. The results were compared, and the discrepancies were identified and resolved. Additionally, to ensure an efficient coding process, all coders participated in group meetings to discuss additions, deletions, and modifications (Stock, 1994 ). Due to the methodological diversity of the studied documents we began to synthesize the literature review findings based on similar study designs. Specifically, most of the meta-analysis studies were grouped in one category due to the quantitative nature of the measured impact. These studies tended to refer to student achievement (Hattie et al., 2014 ). Then, we organized the themes of the qualitative studies in several impact categories. Lastly, we synthesized both review and meta-analysis data across the categories. In order to establish a collective understanding of the concept of impact, we referred to a previous impact study by Balanskat ( 2009 ) which investigated the impact of technology in primary schools. In this context, the impact had a more specific ICT-related meaning and was described as “ a significant influence or effect of ICT on the measured or perceived quality of (parts of) education ” (Balanskat, 2009 , p. 9). In the study presented herein, the main impacts are in relation to learning and learners, teaching, and teachers, as well as other key stakeholders who are directly or indirectly connected to the school unit.

The study’s results identified multiple dimensions of the impact of digital technologies on students’ knowledge, skills, and attitudes; on equality, inclusion, and social integration; on teachers’ professional and teaching practices; and on other school-related aspects and stakeholders. The data analysis indicated various factors that might affect the schools’ digital capacity and transformation, such as digital competencies, the teachers’ personal characteristics and professional development, as well as the school’s leadership and management, administration, infrastructure, etc. The impacts and factors found in the literature review are presented below.

Impacts of digital technologies on students’ knowledge, skills, attitudes, and emotions

The impact of ICT use on students’ knowledge, skills, and attitudes has been investigated early in the literature. Eng ( 2005 ) found a small positive effect between ICT use and students' learning. Specifically, the author reported that access to computer-assisted instruction (CAI) programs in simulation or tutorial modes—used to supplement rather than substitute instruction – could enhance student learning. The author reported studies showing that teachers acknowledged the benefits of ICT on pupils with special educational needs; however, the impact of ICT on students' attainment was unclear. Balanskat et al. ( 2006 ) found a statistically significant positive association between ICT use and higher student achievement in primary and secondary education. The authors also reported improvements in the performance of low-achieving pupils. The use of ICT resulted in further positive gains for students, namely increased attention, engagement, motivation, communication and process skills, teamwork, and gains related to their behaviour towards learning. Evidence from qualitative studies showed that teachers, students, and parents recognized the positive impact of ICT on students' learning regardless of their competence level (strong/weak students). Punie et al. ( 2006 ) documented studies that showed positive results of ICT-based learning for supporting low-achieving pupils and young people with complex lives outside the education system. Liao et al. ( 2007 ) reported moderate positive effects of computer application instruction (CAI, computer simulations, and web-based learning) over traditional instruction on primary school student's achievement. Similarly, Tamim et al. ( 2011 ) reported small to moderate positive effects between the use of computer technology (CAI, ICT, simulations, computer-based instruction, digital and hypermedia) and student achievement in formal face-to-face classrooms compared to classrooms that did not use technology. Jewitt et al., ( 2011 ) found that the use of learning platforms (LPs) (virtual learning environments, management information systems, communication technologies, and information- and resource-sharing technologies) in schools allowed primary and secondary students to access a wider variety of quality learning resources, engage in independent and personalized learning, and conduct self- and peer-review; LPs also provide opportunities for teacher assessment and feedback. Similar findings were reported by Fu ( 2013 ), who documented a list of benefits and opportunities of ICT use. According to the author, the use of ICTs helps students access digital information and course content effectively and efficiently, supports student-centered and self-directed learning, as well as the development of a creative learning environment where more opportunities for critical thinking skills are offered, and promotes collaborative learning in a distance-learning environment. Higgins et al. ( 2012 ) found consistent but small positive associations between the use of technology and learning outcomes of school-age learners (5–18-year-olds) in studies linking the provision and use of technology with attainment. Additionally, Chauhan ( 2017 ) reported a medium positive effect of technology on the learning effectiveness of primary school students compared to students who followed traditional learning instruction.

The rise of mobile technologies and hardware devices instigated investigations into their impact on teaching and learning. Sung et al. ( 2016 ) reported a moderate effect on students' performance from the use of mobile devices in the classroom compared to the use of desktop computers or the non-use of mobile devices. Schmid et al. ( 2014 ) reported medium–low to low positive effects of technology integration (e.g., CAI, ICTs) in the classroom on students' achievement and attitude compared to not using technology or using technology to varying degrees. Tamim et al. ( 2015 ) found a low statistically significant effect of the use of tablets and other smart devices in educational contexts on students' achievement outcomes. The authors suggested that tablets offered additional advantages to students; namely, they reported improvements in students’ notetaking, organizational and communication skills, and creativity. Zheng et al. ( 2016 ) reported a small positive effect of one-to-one laptop programs on students’ academic achievement across subject areas. Additional reported benefits included student-centered, individualized, and project-based learning enhanced learner engagement and enthusiasm. Additionally, the authors found that students using one-to-one laptop programs tended to use technology more frequently than in non-laptop classrooms, and as a result, they developed a range of skills (e.g., information skills, media skills, technology skills, organizational skills). Haßler et al. ( 2016 ) found that most interventions that included the use of tablets across the curriculum reported positive learning outcomes. However, from 23 studies, five reported no differences, and two reported a negative effect on students' learning outcomes. Similar results were indicated by Kalati and Kim ( 2022 ) who investigated the effect of touchscreen technologies on young students’ learning. Specifically, from 53 studies, 34 advocated positive effects of touchscreen devices on children’s learning, 17 obtained mixed findings and two studies reported negative effects.

More recently, approaches that refer to the impact of gamification with the use of digital technologies on teaching and learning were also explored. A review by Pan et al. ( 2022 ) that examined the role of learning games in fostering mathematics education in K-12 settings, reported that gameplay improved students’ performance. Integration of digital games in teaching was also found as a promising pedagogical practice in STEM education that could lead to increased learning gains (Martinez et al., 2022 ; Wang et al., 2022 ). However, although Talan et al. ( 2020 ) reported a medium effect of the use of educational games (both digital and non-digital) on academic achievement, the effect of non-digital games was higher.

Over the last two years, the effects of more advanced technologies on teaching and learning were also investigated. Garzón and Acevedo ( 2019 ) found that AR applications had a medium effect on students' learning outcomes compared to traditional lectures. Similarly, Garzón et al. ( 2020 ) showed that AR had a medium impact on students' learning gains. VR applications integrated into various subjects were also found to have a moderate effect on students’ learning compared to control conditions (traditional classes, e.g., lectures, textbooks, and multimedia use, e.g., images, videos, animation, CAI) (Chen et al., 2022b ). Villena-Taranilla et al. ( 2022 ) noted the moderate effect of VR technologies on students’ learning when these were applied in STEM disciplines. In the same meta-analysis, Villena-Taranilla et al. ( 2022 ) highlighted the role of immersive VR, since its effect on students’ learning was greater (at a high level) across educational levels (K-6) compared to semi-immersive and non-immersive integrations. In another meta-analysis study, the effect size of the immersive VR was small and significantly differentiated across educational levels (Coban et al., 2022 ). The impact of AI on education was investigated by Su and Yang ( 2022 ) and Su et al. ( 2022 ), who showed that this technology significantly improved students’ understanding of AI computer science and machine learning concepts.

It is worth noting that the vast majority of studies referred to learning gains in specific subjects. Specifically, several studies examined the impact of digital technologies on students’ literacy skills and reported positive effects on language learning (Balanskat et al., 2006 ; Grgurović et al., 2013 ; Friedel et al., 2013 ; Zheng et al., 2016 ; Chen et al., 2022b ; Savva et al., 2022 ). Also, several studies documented positive effects on specific language learning areas, namely foreign language learning (Kao, 2014 ), writing (Higgins et al., 2012 ; Wen & Walters, 2022 ; Zheng et al., 2016 ), as well as reading and comprehension (Cheung & Slavin, 2011 ; Liao et al., 2007 ; Schwabe et al., 2022 ). ICTs were also found to have a positive impact on students' performance in STEM (science, technology, engineering, and mathematics) disciplines (Arztmann et al., 2022 ; Bado, 2022 ; Villena-Taranilla et al., 2022 ; Wang et al., 2022 ). Specifically, a number of studies reported positive impacts on students’ achievement in mathematics (Balanskat et al., 2006 ; Hillmayr et al., 2020 ; Li & Ma, 2010 ; Pan et al., 2022 ; Ran et al., 2022 ; Verschaffel et al., 2019 ; Zheng et al., 2016 ). Furthermore, studies documented positive effects of ICTs on science learning (Balanskat et al., 2006 ; Liao et al., 2007 ; Zheng et al., 2016 ; Hillmayr et al., 2020 ; Kalemkuş & Kalemkuş, 2022 ; Lei et al., 2022a ). Çelik ( 2022 ) also noted that computer simulations can help students understand learning concepts related to science. Furthermore, some studies documented that the use of ICTs had a positive impact on students’ achievement in other subjects, such as geography, history, music, and arts (Chauhan, 2017 ; Condie & Munro, 2007 ), and design and technology (Balanskat et al., 2006 ).

More specific positive learning gains were reported in a number of skills, e.g., problem-solving skills and pattern exploration skills (Higgins et al., 2012 ), metacognitive learning outcomes (Verschaffel et al., 2019 ), literacy skills, computational thinking skills, emotion control skills, and collaborative inquiry skills (Lu et al., 2022 ; Su & Yang, 2022 ; Su et al., 2022 ). Additionally, several investigations have reported benefits from the use of ICT on students’ creativity (Fielding & Murcia, 2022 ; Liu et al., 2022 ; Quah & Ng, 2022 ). Lastly, digital technologies were also found to be beneficial for enhancing students’ lifelong learning skills (Haleem et al., 2022 ).

Apart from gaining knowledge and skills, studies also reported improvement in motivation and interest in mathematics (Higgins et. al., 2019 ; Fadda et al., 2022 ) and increased positive achievement emotions towards several subjects during interventions using educational games (Lei et al., 2022a ). Chen et al. ( 2022a ) also reported a small but positive effect of digital health approaches in bullying and cyberbullying interventions with K-12 students, demonstrating that technology-based approaches can help reduce bullying and related consequences by providing emotional support, empowerment, and change of attitude. In their meta-review study, Su et al. ( 2022 ) also documented that AI technologies effectively strengthened students’ attitudes towards learning. In another meta-analysis, Arztmann et al. ( 2022 ) reported positive effects of digital games on motivation and behaviour towards STEM subjects.

Impacts of digital technologies on equality, inclusion and social integration

Although most of the reviewed studies focused on the impact of ICTs on students’ knowledge, skills, and attitudes, reports were also made on other aspects in the school context, such as equality, inclusion, and social integration. Condie and Munro ( 2007 ) documented research interventions investigating how ICT can support pupils with additional or special educational needs. While those interventions were relatively small scale and mostly based on qualitative data, their findings indicated that the use of ICTs enabled the development of communication, participation, and self-esteem. A recent meta-analysis (Baragash et al., 2022 ) with 119 participants with different disabilities, reported a significant overall effect size of AR on their functional skills acquisition. Koh’s meta-analysis ( 2022 ) also revealed that students with intellectual and developmental disabilities improved their competence and performance when they used digital games in the lessons.

Istenic Starcic and Bagon ( 2014 ) found that the role of ICT in inclusion and the design of pedagogical and technological interventions was not sufficiently explored in educational interventions with people with special needs; however, some benefits of ICT use were found in students’ social integration. The issue of gender and technology use was mentioned in a small number of studies. Zheng et al. ( 2016 ) reported a statistically significant positive interaction between one-to-one laptop programs and gender. Specifically, the results showed that girls and boys alike benefitted from the laptop program, but the effect on girls’ achievement was smaller than that on boys’. Along the same lines, Arztmann et al. ( 2022 ) reported no difference in the impact of game-based learning between boys and girls, arguing that boys and girls equally benefited from game-based interventions in STEM domains. However, results from a systematic review by Cussó-Calabuig et al. ( 2018 ) found limited and low-quality evidence on the effects of intensive use of computers on gender differences in computer anxiety, self-efficacy, and self-confidence. Based on their view, intensive use of computers can reduce gender differences in some areas and not in others, depending on contextual and implementation factors.

Impacts of digital technologies on teachers’ professional and teaching practices

Various research studies have explored the impact of ICT on teachers’ instructional practices and student assessment. Friedel et al. ( 2013 ) found that the use of mobile devices by students enabled teachers to successfully deliver content (e.g., mobile serious games), provide scaffolding, and facilitate synchronous collaborative learning. The integration of digital games in teaching and learning activities also gave teachers the opportunity to study and apply various pedagogical practices (Bado, 2022 ). Specifically, Bado ( 2022 ) found that teachers who implemented instructional activities in three stages (pre-game, game, and post-game) maximized students’ learning outcomes and engagement. For instance, during the pre-game stage, teachers focused on lectures and gameplay training, at the game stage teachers provided scaffolding on content, addressed technical issues, and managed the classroom activities. During the post-game stage, teachers organized activities for debriefing to ensure that the gameplay had indeed enhanced students’ learning outcomes.

Furthermore, ICT can increase efficiency in lesson planning and preparation by offering possibilities for a more collaborative approach among teachers. The sharing of curriculum plans and the analysis of students’ data led to clearer target settings and improvements in reporting to parents (Balanskat et al., 2006 ).

Additionally, the use and application of digital technologies in teaching and learning were found to enhance teachers’ digital competence. Balanskat et al. ( 2006 ) documented studies that revealed that the use of digital technologies in education had a positive effect on teachers’ basic ICT skills. The greatest impact was found on teachers with enough experience in integrating ICTs in their teaching and/or who had recently participated in development courses for the pedagogical use of technologies in teaching. Punie et al. ( 2006 ) reported that the provision of fully equipped multimedia portable computers and the development of online teacher communities had positive impacts on teachers’ confidence and competence in the use of ICTs.

Moreover, online assessment via ICTs benefits instruction. In particular, online assessments support the digitalization of students’ work and related logistics, allow teachers to gather immediate feedback and readjust to new objectives, and support the improvement of the technical quality of tests by providing more accurate results. Additionally, the capabilities of ICTs (e.g., interactive media, simulations) create new potential methods of testing specific skills, such as problem-solving and problem-processing skills, meta-cognitive skills, creativity and communication skills, and the ability to work productively in groups (Punie et al., 2006 ).

Impacts of digital technologies on other school-related aspects and stakeholders

There is evidence that the effective use of ICTs and the data transmission offered by broadband connections help improve administration (Balanskat et al., 2006 ). Specifically, ICTs have been found to provide better management systems to schools that have data gathering procedures in place. Condie and Munro ( 2007 ) reported impacts from the use of ICTs in schools in the following areas: attendance monitoring, assessment records, reporting to parents, financial management, creation of repositories for learning resources, and sharing of information amongst staff. Such data can be used strategically for self-evaluation and monitoring purposes which in turn can result in school improvements. Additionally, they reported that online access to other people with similar roles helped to reduce headteachers’ isolation by offering them opportunities to share insights into the use of ICT in learning and teaching and how it could be used to support school improvement. Furthermore, ICTs provided more efficient and successful examination management procedures, namely less time-consuming reporting processes compared to paper-based examinations and smooth communications between schools and examination authorities through electronic data exchange (Punie et al., 2006 ).

Zheng et al. ( 2016 ) reported that the use of ICTs improved home-school relationships. Additionally, Escueta et al. ( 2017 ) reported several ICT programs that had improved the flow of information from the school to parents. Particularly, they documented that the use of ICTs (learning management systems, emails, dedicated websites, mobile phones) allowed for personalized and customized information exchange between schools and parents, such as attendance records, upcoming class assignments, school events, and students’ grades, which generated positive results on students’ learning outcomes and attainment. Such information exchange between schools and families prompted parents to encourage their children to put more effort into their schoolwork.

The above findings suggest that the impact of ICT integration in schools goes beyond students’ performance in school subjects. Specifically, it affects a number of school-related aspects, such as equality and social integration, professional and teaching practices, and diverse stakeholders. In Table ​ Table2, 2 , we summarize the different impacts of digital technologies on school stakeholders based on the literature review, while in Table ​ Table3 3 we organized the tools/platforms and practices/policies addressed in the meta-analyses, literature reviews, EU reports, and international bodies included in the manuscript.

The impact of digital technologies on schools’ stakeholders based on the literature review

Tools/platforms and practices/policies addressed in the meta-analyses, literature reviews, EU reports, and international bodies included in the manuscript

Additionally, based on the results of the literature review, there are many types of digital technologies with different affordances (see, for example, studies on VR vs Immersive VR), which evolve over time (e.g. starting from CAIs in 2005 to Augmented and Virtual reality 2020). Furthermore, these technologies are linked to different pedagogies and policy initiatives, which are critical factors in the study of impact. Table ​ Table3 3 summarizes the different tools and practices that have been used to examine the impact of digital technologies on education since 2005 based on the review results.

Factors that affect the integration of digital technologies

Although the analysis of the literature review demonstrated different impacts of the use of digital technology on education, several authors highlighted the importance of various factors, besides the technology itself, that affect this impact. For example, Liao et al. ( 2007 ) suggested that future studies should carefully investigate which factors contribute to positive outcomes by clarifying the exact relationship between computer applications and learning. Additionally, Haßler et al., ( 2016 ) suggested that the neutral findings regarding the impact of tablets on students learning outcomes in some of the studies included in their review should encourage educators, school leaders, and school officials to further investigate the potential of such devices in teaching and learning. Several other researchers suggested that a number of variables play a significant role in the impact of ICTs on students’ learning that could be attributed to the school context, teaching practices and professional development, the curriculum, and learners’ characteristics (Underwood, 2009 ; Tamim et al., 2011 ; Higgins et al., 2012 ; Archer et al., 2014 ; Sung et al., 2016 ; Haßler et al., 2016 ; Chauhan, 2017 ; Lee et al., 2020 ; Tang et al., 2022 ).

Digital competencies

One of the most common challenges reported in studies that utilized digital tools in the classroom was the lack of students’ skills on how to use them. Fu ( 2013 ) found that students’ lack of technical skills is a barrier to the effective use of ICT in the classroom. Tamim et al. ( 2015 ) reported that students faced challenges when using tablets and smart mobile devices, associated with the technical issues or expertise needed for their use and the distracting nature of the devices and highlighted the need for teachers’ professional development. Higgins et al. ( 2012 ) reported that skills training about the use of digital technologies is essential for learners to fully exploit the benefits of instruction.

Delgado et al. ( 2015 ), meanwhile, reported studies that showed a strong positive association between teachers’ computer skills and students’ use of computers. Teachers’ lack of ICT skills and familiarization with technologies can become a constraint to the effective use of technology in the classroom (Balanskat et al., 2006 ; Delgado et al., 2015 ).

It is worth noting that the way teachers are introduced to ICTs affects the impact of digital technologies on education. Previous studies have shown that teachers may avoid using digital technologies due to limited digital skills (Balanskat, 2006 ), or they prefer applying “safe” technologies, namely technologies that their own teachers used and with which they are familiar (Condie & Munro, 2007 ). In this regard, the provision of digital skills training and exposure to new digital tools might encourage teachers to apply various technologies in their lessons (Condie & Munro, 2007 ). Apart from digital competence, technical support in the school setting has also been shown to affect teachers’ use of technology in their classrooms (Delgado et al., 2015 ). Ferrari et al. ( 2011 ) found that while teachers’ use of ICT is high, 75% stated that they needed more institutional support and a shift in the mindset of educational actors to achieve more innovative teaching practices. The provision of support can reduce time and effort as well as cognitive constraints, which could cause limited ICT integration in the school lessons by teachers (Escueta et al., 2017 ).

Teachers’ personal characteristics, training approaches, and professional development

Teachers’ personal characteristics and professional development affect the impact of digital technologies on education. Specifically, Cheok and Wong ( 2015 ) found that teachers’ personal characteristics (e.g., anxiety, self-efficacy) are associated with their satisfaction and engagement with technology. Bingimlas ( 2009 ) reported that lack of confidence, resistance to change, and negative attitudes in using new technologies in teaching are significant determinants of teachers’ levels of engagement in ICT. The same author reported that the provision of technical support, motivation support (e.g., awards, sufficient time for planning), and training on how technologies can benefit teaching and learning can eliminate the above barriers to ICT integration. Archer et al. ( 2014 ) found that comfort levels in using technology are an important predictor of technology integration and argued that it is essential to provide teachers with appropriate training and ongoing support until they are comfortable with using ICTs in the classroom. Hillmayr et al. ( 2020 ) documented that training teachers on ICT had an important effecton students’ learning.

According to Balanskat et al. ( 2006 ), the impact of ICTs on students’ learning is highly dependent on the teachers’ capacity to efficiently exploit their application for pedagogical purposes. Results obtained from the Teaching and Learning International Survey (TALIS) (OECD, 2021 ) revealed that although schools are open to innovative practices and have the capacity to adopt them, only 39% of teachers in the European Union reported that they are well or very well prepared to use digital technologies for teaching. Li and Ma ( 2010 ) and Hardman ( 2019 ) showed that the positive effect of technology on students’ achievement depends on the pedagogical practices used by teachers. Schmid et al. ( 2014 ) reported that learning was best supported when students were engaged in active, meaningful activities with the use of technological tools that provided cognitive support. Tamim et al. ( 2015 ) compared two different pedagogical uses of tablets and found a significant moderate effect when the devices were used in a student-centered context and approach rather than within teacher-led environments. Similarly, Garzón and Acevedo ( 2019 ) and Garzón et al. ( 2020 ) reported that the positive results from the integration of AR applications could be attributed to the existence of different variables which could influence AR interventions (e.g., pedagogical approach, learning environment, and duration of the intervention). Additionally, Garzón et al. ( 2020 ) suggested that the pedagogical resources that teachers used to complement their lectures and the pedagogical approaches they applied were crucial to the effective integration of AR on students’ learning gains. Garzón and Acevedo ( 2019 ) also emphasized that the success of a technology-enhanced intervention is based on both the technology per se and its characteristics and on the pedagogical strategies teachers choose to implement. For instance, their results indicated that the collaborative learning approach had the highest impact on students’ learning gains among other approaches (e.g., inquiry-based learning, situated learning, or project-based learning). Ran et al. ( 2022 ) also found that the use of technology to design collaborative and communicative environments showed the largest moderator effects among the other approaches.

Hattie ( 2008 ) reported that the effective use of computers is associated with training teachers in using computers as a teaching and learning tool. Zheng et al. ( 2016 ) noted that in addition to the strategies teachers adopt in teaching, ongoing professional development is also vital in ensuring the success of technology implementation programs. Sung et al. ( 2016 ) found that research on the use of mobile devices to support learning tends to report that the insufficient preparation of teachers is a major obstacle in implementing effective mobile learning programs in schools. Friedel et al. ( 2013 ) found that providing training and support to teachers increased the positive impact of the interventions on students’ learning gains. Trucano ( 2005 ) argued that positive impacts occur when digital technologies are used to enhance teachers’ existing pedagogical philosophies. Higgins et al. ( 2012 ) found that the types of technologies used and how they are used could also affect students’ learning. The authors suggested that training and professional development of teachers that focuses on the effective pedagogical use of technology to support teaching and learning is an important component of successful instructional approaches (Higgins et al., 2012 ). Archer et al. ( 2014 ) found that studies that reported ICT interventions during which teachers received training and support had moderate positive effects on students’ learning outcomes, which were significantly higher than studies where little or no detail about training and support was mentioned. Fu ( 2013 ) reported that the lack of teachers’ knowledge and skills on the technical and instructional aspects of ICT use in the classroom, in-service training, pedagogy support, technical and financial support, as well as the lack of teachers’ motivation and encouragement to integrate ICT on their teaching were significant barriers to the integration of ICT in education.

School leadership and management

Management and leadership are important cornerstones in the digital transformation process (Pihir et al., 2018 ). Zheng et al. ( 2016 ) documented leadership among the factors positively affecting the successful implementation of technology integration in schools. Strong leadership, strategic planning, and systematic integration of digital technologies are prerequisites for the digital transformation of education systems (Ređep, 2021 ). Management and leadership play a significant role in formulating policies that are translated into practice and ensure that developments in ICT become embedded into the life of the school and in the experiences of staff and pupils (Condie & Munro, 2007 ). Policy support and leadership must include the provision of an overall vision for the use of digital technologies in education, guidance for students and parents, logistical support, as well as teacher training (Conrads et al., 2017 ). Unless there is a commitment throughout the school, with accountability for progress at key points, it is unlikely for ICT integration to be sustained or become part of the culture (Condie & Munro, 2007 ). To achieve this, principals need to adopt and promote a whole-institution strategy and build a strong mutual support system that enables the school’s technological maturity (European Commission, 2019 ). In this context, school culture plays an essential role in shaping the mindsets and beliefs of school actors towards successful technology integration. Condie and Munro ( 2007 ) emphasized the importance of the principal’s enthusiasm and work as a source of inspiration for the school staff and the students to cultivate a culture of innovation and establish sustainable digital change. Specifically, school leaders need to create conditions in which the school staff is empowered to experiment and take risks with technology (Elkordy & Lovinelli, 2020 ).

In order for leaders to achieve the above, it is important to develop capacities for learning and leading, advocating professional learning, and creating support systems and structures (European Commission, 2019 ). Digital technology integration in education systems can be challenging and leadership needs guidance to achieve it. Such guidance can be introduced through the adoption of new methods and techniques in strategic planning for the integration of digital technologies (Ređep, 2021 ). Even though the role of leaders is vital, the relevant training offered to them has so far been inadequate. Specifically, only a third of the education systems in Europe have put in place national strategies that explicitly refer to the training of school principals (European Commission, 2019 , p. 16).

Connectivity, infrastructure, and government and other support

The effective integration of digital technologies across levels of education presupposes the development of infrastructure, the provision of digital content, and the selection of proper resources (Voogt et al., 2013 ). Particularly, a high-quality broadband connection in the school increases the quality and quantity of educational activities. There is evidence that ICT increases and formalizes cooperative planning between teachers and cooperation with managers, which in turn has a positive impact on teaching practices (Balanskat et al., 2006 ). Additionally, ICT resources, including software and hardware, increase the likelihood of teachers integrating technology into the curriculum to enhance their teaching practices (Delgado et al., 2015 ). For example, Zheng et al. ( 2016 ) found that the use of one-on-one laptop programs resulted in positive changes in teaching and learning, which would not have been accomplished without the infrastructure and technical support provided to teachers. Delgado et al. ( 2015 ) reported that limited access to technology (insufficient computers, peripherals, and software) and lack of technical support are important barriers to ICT integration. Access to infrastructure refers not only to the availability of technology in a school but also to the provision of a proper amount and the right types of technology in locations where teachers and students can use them. Effective technical support is a central element of the whole-school strategy for ICT (Underwood, 2009 ). Bingimlas ( 2009 ) reported that lack of technical support in the classroom and whole-school resources (e.g., failing to connect to the Internet, printers not printing, malfunctioning computers, and working on old computers) are significant barriers that discourage the use of ICT by teachers. Moreover, poor quality and inadequate hardware maintenance, and unsuitable educational software may discourage teachers from using ICTs (Balanskat et al., 2006 ; Bingimlas, 2009 ).

Government support can also impact the integration of ICTs in teaching. Specifically, Balanskat et al. ( 2006 ) reported that government interventions and training programs increased teachers’ enthusiasm and positive attitudes towards ICT and led to the routine use of embedded ICT.

Lastly, another important factor affecting digital transformation is the development and quality assurance of digital learning resources. Such resources can be support textbooks and related materials or resources that focus on specific subjects or parts of the curriculum. Policies on the provision of digital learning resources are essential for schools and can be achieved through various actions. For example, some countries are financing web portals that become repositories, enabling teachers to share resources or create their own. Additionally, they may offer e-learning opportunities or other services linked to digital education. In other cases, specific agencies of projects have also been set up to develop digital resources (Eurydice, 2019 ).

Administration and digital data management

The digital transformation of schools involves organizational improvements at the level of internal workflows, communication between the different stakeholders, and potential for collaboration. Vuorikari et al. ( 2020 ) presented evidence that digital technologies supported the automation of administrative practices in schools and reduced the administration’s workload. There is evidence that digital data affects the production of knowledge about schools and has the power to transform how schooling takes place. Specifically, Sellar ( 2015 ) reported that data infrastructure in education is developing due to the demand for “ information about student outcomes, teacher quality, school performance, and adult skills, associated with policy efforts to increase human capital and productivity practices ” (p. 771). In this regard, practices, such as datafication which refers to the “ translation of information about all kinds of things and processes into quantified formats” have become essential for decision-making based on accountability reports about the school’s quality. The data could be turned into deep insights about education or training incorporating ICTs. For example, measuring students’ online engagement with the learning material and drawing meaningful conclusions can allow teachers to improve their educational interventions (Vuorikari et al., 2020 ).

Students’ socioeconomic background and family support

Research show that the active engagement of parents in the school and their support for the school’s work can make a difference to their children’s attitudes towards learning and, as a result, their achievement (Hattie, 2008 ). In recent years, digital technologies have been used for more effective communication between school and family (Escueta et al., 2017 ). The European Commission ( 2020 ) presented data from a Eurostat survey regarding the use of computers by students during the pandemic. The data showed that younger pupils needed additional support and guidance from parents and the challenges were greater for families in which parents had lower levels of education and little to no digital skills.

In this regard, the socio-economic background of the learners and their socio-cultural environment also affect educational achievements (Punie et al., 2006 ). Trucano documented that the use of computers at home positively influenced students’ confidence and resulted in more frequent use at school, compared to students who had no home access (Trucano, 2005 ). In this sense, the socio-economic background affects the access to computers at home (OECD, 2015 ) which in turn influences the experience of ICT, an important factor for school achievement (Punie et al., 2006 ; Underwood, 2009 ). Furthermore, parents from different socio-economic backgrounds may have different abilities and availability to support their children in their learning process (Di Pietro et al., 2020 ).

Schools’ socioeconomic context and emergency situations

The socio-economic context of the school is closely related to a school’s digital transformation. For example, schools in disadvantaged, rural, or deprived areas are likely to lack the digital capacity and infrastructure required to adapt to the use of digital technologies during emergency periods, such as the COVID-19 pandemic (Di Pietro et al., 2020 ). Data collected from school principals confirmed that in several countries, there is a rural/urban divide in connectivity (OECD, 2015 ).

Emergency periods also affect the digitalization of schools. The COVID-19 pandemic led to the closure of schools and forced them to seek appropriate and connective ways to keep working on the curriculum (Di Pietro et al., 2020 ). The sudden large-scale shift to distance and online teaching and learning also presented challenges around quality and equity in education, such as the risk of increased inequalities in learning, digital, and social, as well as teachers facing difficulties coping with this demanding situation (European Commission, 2020 ).

Looking at the findings of the above studies, we can conclude that the impact of digital technologies on education is influenced by various actors and touches many aspects of the school ecosystem. Figure  1 summarizes the factors affecting the digital technologies’ impact on school stakeholders based on the findings from the literature review.

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Factors that affect the impact of ICTs on education

The findings revealed that the use of digital technologies in education affects a variety of actors within a school’s ecosystem. First, we observed that as technologies evolve, so does the interest of the research community to apply them to school settings. Figure  2 summarizes the trends identified in current research around the impact of digital technologies on schools’ digital capacity and transformation as found in the present study. Starting as early as 2005, when computers, simulations, and interactive boards were the most commonly applied tools in school interventions (e.g., Eng, 2005 ; Liao et al., 2007 ; Moran et al., 2008 ; Tamim et al., 2011 ), moving towards the use of learning platforms (Jewitt et al., 2011 ), then to the use of mobile devices and digital games (e.g., Tamim et al., 2015 ; Sung et al., 2016 ; Talan et al., 2020 ), as well as e-books (e.g., Savva et al., 2022 ), to the more recent advanced technologies, such as AR and VR applications (e.g., Garzón & Acevedo, 2019 ; Garzón et al., 2020 ; Kalemkuş & Kalemkuş, 2022 ), or robotics and AI (e.g., Su & Yang, 2022 ; Su et al., 2022 ). As this evolution shows, digital technologies are a concept in flux with different affordances and characteristics. Additionally, from an instructional perspective, there has been a growing interest in different modes and models of content delivery such as online, blended, and hybrid modes (e.g., Cheok & Wong, 2015 ; Kazu & Yalçin, 2022 ; Ulum, 2022 ). This is an indication that the value of technologies to support teaching and learning as well as other school-related practices is increasingly recognized by the research and school community. The impact results from the literature review indicate that ICT integration on students’ learning outcomes has effects that are small (Coban et al., 2022 ; Eng, 2005 ; Higgins et al., 2012 ; Schmid et al., 2014 ; Tamim et al., 2015 ; Zheng et al., 2016 ) to moderate (Garzón & Acevedo, 2019 ; Garzón et al., 2020 ; Liao et al., 2007 ; Sung et al., 2016 ; Talan et al., 2020 ; Wen & Walters, 2022 ). That said, a number of recent studies have reported high effect sizes (e.g., Kazu & Yalçin, 2022 ).

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Current work and trends in the study of the impact of digital technologies on schools’ digital capacity

Based on these findings, several authors have suggested that the impact of technology on education depends on several variables and not on the technology per se (Tamim et al., 2011 ; Higgins et al., 2012 ; Archer et al., 2014 ; Sung et al., 2016 ; Haßler et al., 2016 ; Chauhan, 2017 ; Lee et al., 2020 ; Lei et al., 2022a ). While the impact of ICTs on student achievement has been thoroughly investigated by researchers, other aspects related to school life that are also affected by ICTs, such as equality, inclusion, and social integration have received less attention. Further analysis of the literature review has revealed a greater investment in ICT interventions to support learning and teaching in the core subjects of literacy and STEM disciplines, especially mathematics, and science. These were the most common subjects studied in the reviewed papers often drawing on national testing results, while studies that investigated other subject areas, such as social studies, were limited (Chauhan, 2017 ; Condie & Munro, 2007 ). As such, research is still lacking impact studies that focus on the effects of ICTs on a range of curriculum subjects.

The qualitative research provided additional information about the impact of digital technologies on education, documenting positive effects and giving more details about implications, recommendations, and future research directions. Specifically, the findings regarding the role of ICTs in supporting learning highlight the importance of teachers’ instructional practice and the learning context in the use of technologies and consequently their impact on instruction (Çelik, 2022 ; Schmid et al., 2014 ; Tamim et al., 2015 ). The review also provided useful insights regarding the various factors that affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the transformation process. Specifically, these factors include a) digital competencies; b) teachers’ personal characteristics and professional development; c) school leadership and management; d) connectivity, infrastructure, and government support; e) administration and data management practices; f) students’ socio-economic background and family support and g) the socioeconomic context of the school and emergency situations. It is worth noting that we observed factors that affect the integration of ICTs in education but may also be affected by it. For example, the frequent use of ICTs and the use of laptops by students for instructional purposes positively affect the development of digital competencies (Zheng et al., 2016 ) and at the same time, the digital competencies affect the use of ICTs (Fu, 2013 ; Higgins et al., 2012 ). As a result, the impact of digital technologies should be explored more as an enabler of desirable and new practices and not merely as a catalyst that improves the output of the education process i.e. namely student attainment.

Conclusions

Digital technologies offer immense potential for fundamental improvement in schools. However, investment in ICT infrastructure and professional development to improve school education are yet to provide fruitful results. Digital transformation is a complex process that requires large-scale transformative changes that presuppose digital capacity and preparedness. To achieve such changes, all actors within the school’s ecosystem need to share a common vision regarding the integration of ICTs in education and work towards achieving this goal. Our literature review, which synthesized quantitative and qualitative data from a list of meta-analyses and review studies, provided useful insights into the impact of ICTs on different school stakeholders and showed that the impact of digital technologies touches upon many different aspects of school life, which are often overlooked when the focus is on student achievement as the final output of education. Furthermore, the concept of digital technologies is a concept in flux as technologies are not only different among them calling for different uses in the educational practice but they also change through time. Additionally, we opened a forum for discussion regarding the factors that affect a school’s digital capacity and transformation. We hope that our study will inform policy, practice, and research and result in a paradigm shift towards more holistic approaches in impact and assessment studies.

Study limitations and future directions

We presented a review of the study of digital technologies' impact on education and factors influencing schools’ digital capacity and transformation. The study results were based on a non-systematic literature review grounded on the acquisition of documentation in specific databases. Future studies should investigate more databases to corroborate and enhance our results. Moreover, search queries could be enhanced with key terms that could provide additional insights about the integration of ICTs in education, such as “policies and strategies for ICT integration in education”. Also, the study drew information from meta-analyses and literature reviews to acquire evidence about the effects of ICT integration in schools. Such evidence was mostly based on the general conclusions of the studies. It is worth mentioning that, we located individual studies which showed different, such as negative or neutral results. Thus, further insights are needed about the impact of ICTs on education and the factors influencing the impact. Furthermore, the nature of the studies included in meta-analyses and reviews is different as they are based on different research methodologies and data gathering processes. For instance, in a meta-analysis, the impact among the studies investigated is measured in a particular way, depending on policy or research targets (e.g., results from national examinations, pre-/post-tests). Meanwhile, in literature reviews, qualitative studies offer additional insights and detail based on self-reports and research opinions on several different aspects and stakeholders who could affect and be affected by ICT integration. As a result, it was challenging to draw causal relationships between so many interrelating variables.

Despite the challenges mentioned above, this study envisaged examining school units as ecosystems that consist of several actors by bringing together several variables from different research epistemologies to provide an understanding of the integration of ICTs. However, the use of other tools and methodologies and models for evaluation of the impact of digital technologies on education could give more detailed data and more accurate results. For instance, self-reflection tools, like SELFIE—developed on the DigCompOrg framework- (Kampylis et al., 2015 ; Bocconi & Lightfoot, 2021 ) can help capture a school’s digital capacity and better assess the impact of ICTs on education. Furthermore, the development of a theory of change could be a good approach for documenting the impact of digital technologies on education. Specifically, theories of change are models used for the evaluation of interventions and their impact; they are developed to describe how interventions will work and give the desired outcomes (Mayne, 2015 ). Theory of change as a methodological approach has also been used by researchers to develop models for evaluation in the field of education (e.g., Aromatario et al., 2019 ; Chapman & Sammons, 2013 ; De Silva et al., 2014 ).

We also propose that future studies aim at similar investigations by applying more holistic approaches for impact assessment that can provide in-depth data about the impact of digital technologies on education. For instance, future studies could focus on different research questions about the technologies that are used during the interventions or the way the implementation takes place (e.g., What methodologies are used for documenting impact? How are experimental studies implemented? How can teachers be taken into account and trained on the technology and its functions? What are the elements of an appropriate and successful implementation? How is the whole intervention designed? On which learning theories is the technology implementation based?).

Future research could also focus on assessing the impact of digital technologies on various other subjects since there is a scarcity of research related to particular subjects, such as geography, history, arts, music, and design and technology. More research should also be done about the impact of ICTs on skills, emotions, and attitudes, and on equality, inclusion, social interaction, and special needs education. There is also a need for more research about the impact of ICTs on administration, management, digitalization, and home-school relationships. Additionally, although new forms of teaching and learning with the use of ICTs (e.g., blended, hybrid, and online learning) have initiated several investigations in mainstream classrooms, only a few studies have measured their impact on students’ learning. Additionally, our review did not document any study about the impact of flipped classrooms on K-12 education. Regarding teaching and learning approaches, it is worth noting that studies referred to STEM or STEAM did not investigate the impact of STEM/STEAM as an interdisciplinary approach to learning but only investigated the impact of ICTs on learning in each domain as a separate subject (science, technology, engineering, arts, mathematics). Hence, we propose future research to also investigate the impact of the STEM/STEAM approach on education. The impact of emerging technologies on education, such as AR, VR, robotics, and AI has also been investigated recently, but more work needs to be done.

Finally, we propose that future studies could focus on the way in which specific factors, e.g., infrastructure and government support, school leadership and management, students’ and teachers’ digital competencies, approaches teachers utilize in the teaching and learning (e.g., blended, online and hybrid learning, flipped classrooms, STEM/STEAM approach, project-based learning, inquiry-based learning), affect the impact of digital technologies on education. We hope that future studies will give detailed insights into the concept of schools’ digital transformation through further investigation of impacts and factors which influence digital capacity and transformation based on the results and the recommendations of the present study.

Acknowledgements

This project has received funding under Grant Agreement No Ref Ares (2021) 339036 7483039 as well as funding from the European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement No 739578 and the Government of the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy. The UVa co-authors would like also to acknowledge funding from the European Regional Development Fund and the National Research Agency of the Spanish Ministry of Science and Innovation, under project grant PID2020-112584RB-C32.

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Transforming education systems: Why, what, and how

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Rebecca winthrop and rebecca winthrop director - center for universal education , senior fellow - global economy and development @rebeccawinthrop the hon. minister david sengeh the hon. minister david sengeh minister of education and chief innovation officer - government of sierra leone, chief innovation officer - directorate of science, technology and innovation in sierra leone @dsengeh.

June 23, 2022

Today, the topic of education system transformation is front of mind for many leaders. Ministers of education around the world are seeking to build back better as they emerge from COVID-19-school closures to a new normal of living with a pandemic. The U.N. secretary general is convening the Transforming Education Summit (TES) at this year’s general assembly meeting (United Nations, n.d.). Students around the world continue to demand transformation on climate and not finding voice to do this through their schools are regularly leaving class to test out their civic action skills.      

It is with this moment in mind that we have developed this shared vision of education system transformation. Collectively we offer insights on transformation from the perspective of a global think tank and a national government: the Center for Universal Education (CUE) at Brookings brings years of global research on education change and transformation, and the Ministry of Education of Sierra Leone brings on-the-ground lessons from designing and implementing system-wide educational rebuilding.   

This brief is for any education leader or stakeholder who is interested in charting a transformation journey in their country or education jurisdiction such as a state or district. It is also for civil society organizations, funders, researchers, and anyone interested in the topic of national development through education. In it, we answer the following three questions and argue for a participatory approach to transformation:  

  • Why is education system transformation urgent now? We argue that the world is at an inflection point. Climate change, the changing nature of work, increasing conflict and authoritarianism together with the urgency of COVID recovery has made the transformation agenda more critical than ever. 
  • What is education system transformation? We argue that education system transformation must entail a fresh review of the goals of your system – are they meeting the moment that we are in, are they tackling inequality and building resilience for a changing world, are they fully context aware, are they owned broadly across society – and then fundamentally positioning all components of your education system to coherently contribute to this shared purpose.  
  • How can education system transformation advance in your country or jurisdiction? We argue that three steps are crucial: Purpose (developing a broadly shared vision and purpose), Pedagogy (redesigning the pedagogical core), and Position (positioning and aligning all components of the system to support the pedagogical core and purpose). Deep engagement of educators, families, communities, students, ministry staff, and partners is essential across each of these “3 P” steps.    

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Our aim is not to provide “the answer” — we are also on a journey and continually learning about what it takes to transform systems — but to help others interested in pursuing system transformation benefit from our collective reflections to date. The goal is to complement and put in perspective — not replace — detailed guidance from other actors on education sector on system strengthening, reform, and redesign. In essence, we want to broaden the conversation and debate.

Download the full policy brief»

Download the executive summary»

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    The Effects of the Transition to Secondary Education on Academic Achievement. The first indicator of a successful transition to secondary education is academic achievement (Figure Figure1 1).Academic achievement is essential for individual well-being across the lifespan (Gottfredson, 2004; Fiscella and Kitzman, 2009).The primary-to-secondary transition is a critical period of development in ...

  6. Enhancing senior high school student engagement and academic ...

    The multi-disciplinary nature of science, technology, engineering, and math (STEM) careers often renders difficulty for high school students navigating from classroom knowledge to post-secondary ...

  7. PDF The Problem of Student Absenteeism, Its Impact on Educational

    20 days and over is lower than in secondary education. The rate of 14.80% in primary schools in 2014 decreased to 5.67% in 2018. Absenteeism in secondary schools was approximately 10% in 2015-2018. When the distribution of absenteeism rates throughout the country is examined, it can be said that the ...

  8. PDF Report on the Condition of Education 2021

    Department of Education, Congress, the states, other education policymakers, practitioners, data users, and the ... At the elementary and secondary level (prekindergarten through grade 12), the data show that 50.7 million students were enrolled in public schools fall 2018, the most recent year for which data were available at the ...

  9. Full article: Teaching interdisciplinarity in secondary school: A

    The purposes of this paper are (1) to establish a state of the art of studies focusing on interdisciplinary experiences in secondary schools, (2) to highlight the measured effects on students and teachers, and (3) to identify the conditions that favor the construction of interdisciplinary sequences. 5. Method. 5.1.

  10. Research in Education: Sage Journals

    Research in Education provides a space for fully peer-reviewed, critical, trans-disciplinary, debates on theory, policy and practice in relation to Education. International in scope, we publish challenging, well-written and theoretically innovative contributions that question and explore the concept, practice and institution of Education as an object of study.

  11. PDF The Impact of Covid-19 on Student Experiences and Expectations

    education, choice of major, etc.). Our results underscore the fact that the COVID-19 shock is likely to exacerbate socioeconomic disparities in higher education. This is consistent with ndings regarding the impacts of COVID-19 on K-12 students. Kuhfeld et al.,2020project that school closures are likely to lead to signi cant learning losses in math

  12. (PDF) Secondary data analysis in educational research: opportunities

    The definitions of the SDA are analyzed; the statistics of journals articles with secondary data analysis in the field of sociology, social work and education is discussed; the dynamics of...

  13. The social impact of schooling on students with dyslexia: A systematic

    This systematic review of the qualitative research on the formal school education of children with dyslexia addresses three main questions: 1) What is known about the educational experiences of children with dyslexia? 2) What is known about the role that parents/guardians play in their child's schooling?

  14. Gender Segregation in Education: Evidence From Higher Secondary Stream

    The same pattern is visible in secondary and higher secondary levels: over the last two decades, the enrollment rate increased from 50% to 77%, and the gender gap declined from 16 percentage points to 2 percentage points. The first 10 years of education in India include a common, nonselective curriculum for all students.

  15. Secondary Education Research Papers

    21st-no-frills-refereed-papers-2595.pdf. This paper presents interim findings from two current studies of Australian vocational education and training in schools (VET in Schools). The first examines how schools are implementing vocational programs and how students are using... more. Download. by Kira Clarke. 5.

  16. ERIC

    ERIC is an online library of education research and information, sponsored by the Institute of Education Sciences (IES) of the U.S. Department of Education.

  17. ERIC

    Selectively indexed journals contain an average of 50-79% education-related articles and are critical to topic area coverage; ERIC applies a manual article-by-article selection process and indexes only the articles that conform to the standard and criteria outlined in the ERIC selection policy.

  18. (PDF) Secondary Education in India: Determinants of ...

    Geetha Rani (2007) studied on the Secondary Education in India: Development and Performance. Research found that the development of school education in India reflects an expansionary phase...

  19. Impacts of digital technologies on education and factors influencing

    Published online 2022 Nov 21. doi: 10.1007/s10639-022-11431-8 PMCID: PMC9684747 PMID: 36465416 Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: A literature review

  20. Transforming education systems: Why, what, and how

    Collaborating to transform and improve education systems: A playbook for family-school engagement. Our aim is not to provide "the answer"—we are also on a journey and continually learning ...

  21. PDF Journal of Indian Education

    An Exploration into Mathematics Classroom Processes at Secondary Schools of Bhubaneswar' offers a thorough description of the nuances of mathematics classrooms in terms of execution of content, use of resources, formulation of academic courses, management of classroom and persuasion of evaluation strategy, etc.

  22. Secondary Qualitative Research Methodology Using Online Data within the

    Secondary qualitative data analysis can be a powerful method by which to gain insights that primary data analysis cannot offer. There is much literature using primary interview data, but often, the primary data represent either a small sample size or a limited regional pool. ... such as research papers, policy papers, news, and some personal ...

  23. Teachers' Positive Feedback Practices on Struggling Readers in ...

    Abstract. Feedback is a cornerstone in education, profoundly influencing students' educational journeys and achievements. This study aims to investigate the impact of positive feedback practices among teachers on struggling readers in Sri Lanka's junior secondary education context.

  24. (PDF) Secondary Education in India

    July 2007 Authors: Anugula N Reddy National University of Educational Planning and Administration (NUEPA) Abstract The paper briefly describes current status and challenges in expanding...