Category: Educational Stage

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Effective Teaching Approach Higher Education

Supporting self-regulated learning through generative AI feedback in online higher education

A recent mixed-methods study by Yilmaz and colleagues examined whether feedback generated by artificial intelligence can help university students develop self-regulated learning skills (SRLs) in an online course, and how students’ perceptions of the feedback source shape their attitudes. The study was conducted within a nine-week distance-learning “Basic Statistics” module and involved 46 higher education students who were randomly and blindly assigned to either a GenAI feedback group (n = 23) or a tutor feedback group (n = 23), with participants unaware of which type of feedback they were receiving during the intervention itself.

The researchers drew on Nazaretsky and colleagues’ (2024) four-dimension feedback perception framework, covering objectivity, usefulness, genuineness, and provider credibility, together with Barnard and colleagues’ (2009) six-dimension SRL model, comprising goal setting, task strategies, environment structuring, time management, help-seeking, and self-evaluation. SRL was measured using trace-based indicators adapted from Ye and Pennisi’s (2022) framework, derived from roughly 48,000 MOODLE log records that were mined, standardised, and mapped onto SRL proxy scores before and after two feedback interventions. GenAI feedback was produced using GPT-4 through structured prompts fed with each student’s individual proxy z-scores, while tutor feedback was generated using a purpose-built support tool named RefleXED, based on the same underlying data.

The results showed that students rated GenAI-generated feedback more favourably than tutor-generated feedback across every perception dimension, with a statistically significant advantage found specifically for Genuineness (p = .036, r = .36). In terms of SRL development, the GenAI feedback group demonstrated a significant improvement in the Task Strategies dimension (p = .044, r = .35), and a near-significant trend emerged for Time Management (p = .057, r = .33), while no significant between-group differences were found for the remaining SRL dimensions. Qualitative analysis of open-ended responses from 17 treatment-group students revealed considerable variation in awareness of the feedback source: students who recognised the feedback as AI-generated generally reported no change in attitude, prioritising content quality over provenance, whereas a smaller number of unaware students indicated their views might have shifted had they known the source in advance.

The findings suggest that carefully designed, learning-analytics-informed GenAI feedback holds real potential to scale personalised support for self-regulation in online higher education, but the researchers caution against viewing GenAI as a replacement for tutors. Instead, they argue for a complementary model in which GenAI’s scalability and adaptability work alongside tutors’ pedagogical judgement, while institutions remain attentive to how students’ awareness and perceptions of the feedback source can influence its ultimate impact. The authors note that the modest sample size (n = 46) drawn from a single course limits generalisability, and they call for future research involving larger, more diverse cohorts, no-feedback control conditions, and longer-term tracking of SRL outcomes.

Source (Open Access): Yilmaz, M., Temur, H. B., Emmungil, L., Çelik, E., Gauthier, A., & Cukurova, M. (2026). Supporting self-regulated learning through generative AI feedback in online higher education: the importance of student perceptions of the source of feedback. International Journal of Educational Technology in Higher Education, 23(1), 16.

https://doi.org/10.1186/s41239-026-00592-y… Read the rest

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Higher Education Programme Evaluation

Unequal Access, Equal Outcomes? Gender Differences in University-Led STEM Programs

Guo et al. (2025) investigate whether university-led STEM programs function as gender equalizers in shaping undergraduates’ career commitment in STEM. Grounded in Social Cognitive Career Theory, the study examines how four program factors (i.e., course resources, research opportunities, mentoring quality, and peer interactions) relate to students’ STEM career commitment, and whether these relationships differ by gender through the mediating roles of STEM professional self-efficacy and perceptions of STEM professionals. Data were drawn from a nationwide survey of 19,108 undergraduates from 39 leading Chinese universities participating in the Strong Foundation Program, a large-scale initiative designed to cultivate elite STEM talent.

Using t-tests, regression analyses, and multi-group structural equation modeling, the authors compared gender differences among both program participants and non-participants. Results show that although men reported higher overall STEM career commitment in the full sample, this gender gap disappeared among students enrolled in the university-led STEM program. Women participants even reported more favorable experiences in course resources, research opportunities, and mentoring quality, whereas men reported stronger peer interactions. These findings suggest that structured university programs may substantially reduce gender disparities in STEM career commitment.

Regression results further indicate that all four program factors positively predicted STEM career commitment. Course resources and research opportunities were particularly influential for women students, while peer interactions exerted a stronger effect for men students. Mentoring quality showed comparable effects across genders. These patterns highlight gender-differentiated sensitivities to specific institutional supports within STEM programs.

Multi-group mediation analyses revealed distinct gender-specific pathways. For women students, course resources and research opportunities enhanced STEM career commitment primarily through STEM professional self-efficacy and positive perceptions of STEM professionals. In contrast, men students’ career commitment was shaped more strongly by peer interactions via perceptions of STEM professionals. Mentoring quality demonstrated similar indirect effects for both groups. Together, these findings underscore that equal access does not imply identical developmental mechanisms.

Overall, the study demonstrates that thoughtfully structured university-led STEM programs can act as effective gender equalizers in career commitment outcomes, even when women remain numerically under-represented in participation. By revealing gender-specific psychological and contextual pathways, the research extends Social Cognitive Career Theory and provides important implications for designing targeted STEM policies and support systems in higher education.

Source (Open Access): Guo, C., Wu, W., Hu, T., & Gao, T. (2025). Unequal access, equal outcomes? Gender differences in the relationship between university-led STEM program factors and undergraduates’ career commitment in STEM. International Journal of STEM Education, 12(1), 46.

https://doi.org/10.1186/s40594-025-00569-9… Read the rest

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Effective Teaching Approach Secondary School Education

Enhancing Middle School Science Learning with AR-Supported Intelligent Tutoring

Ateş (2025) examines whether combining augmented reality (AR) with an intelligent tutoring system (ITS) can improve middle school students’ science learning outcomes and learning experiences. The study focuses on the teaching of the periodic table, a topic that is often difficult for students because it involves abstract concepts and complex relationships among elements. By integrating AR-based visualizations with ITS-supported personalized feedback, adaptive learning paths, and individualized support, the research explores whether this technology-enhanced approach can outperform conventional science instruction.

Using a quasi-experimental pretest–posttest control group design, the study involved 58 eighth-grade students from a public middle school. The experimental group learned through the ITS-AR system, while the control group received traditional textbook-based instruction, lectures, demonstrations, practice exercises, and assessments. The intervention lasted eight weeks: students first received an introduction to the learning system, then completed pretests and questionnaires, participated in four weeks of instruction, and finally completed posttests and post-questionnaires. Measures included academic achievement, student engagement, science motivation, and self-efficacy.

Results indicate that students in the ITS-AR group significantly outperformed those in the control group across all measured outcomes. After controlling for pretest differences, the experimental group showed higher science learning outcomes, stronger engagement, greater motivation to learn science, and higher self-efficacy. The AR component helped students visualize and interact with periodic table content in more dynamic ways, while the ITS component provided feedback on students’ project ideas and supported more individualized learning. Together, these features appeared to make abstract scientific ideas more concrete, interactive, and accessible.

Overall, the findings suggest that AR-supported intelligent tutoring can be an effective instructional approach for improving science learning and supporting students’ motivational and affective development. The study highlights the value of integrating artificial intelligence and immersive visualization tools into science education, particularly when students need to understand abstract or visually demanding concepts. At the same time, because the study was conducted with a relatively small sample from one school, the findings should be interpreted as promising evidence that requires further validation across broader and more diverse educational contexts.

Source (Open Access): Ateş, H. (2025). Integrating augmented reality into intelligent tutoring systems to enhance science education outcomes. Education and Information Technologies, 30(4), 4435-4470.

https://doi.org/10.1007/s10639-024-12970-y… Read the rest

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K-12 Education Social and Motivational Outcomes

The Relationship Between Teacher Burnout, Absenteeism, Teacher–Student Interactions, and Student Motivation and Achievement

Wartenberg and colleagues used meta-analysis and systematic review methods to comprehensively examine the relationship between teacher burnout, teachers’ professional functioning, and student outcomes. The study integrated 86 studies, 90 independent samples, and a total of 38,457 teachers, systematically analyzed the associations between three core burnout symptoms—emotional exhaustion, depersonalization, and reduced personal accomplishment—and teacher absenteeism, the quality of teacher–student interactions, and student motivation and academic achievement.

The results showed that teacher burnout was consistently related to multiple professional and student outcomes, although the strength of the associations varied. First, in terms of teacher absenteeism, emotional exhaustion was positively but weakly related to absenteeism (r = .18, 95% CI [.08, .29]), depersonalization was also weakly positively related to absenteeism (r = .14, 95% CI [.04, .24]), and reduced personal accomplishment showed an association close to zero but still significant (r = .08, 95% CI [.03, .12]). Second, regarding teacher–student interactions, all three burnout symptoms were negatively related to interaction quality, with depersonalization (r = −.21, 95% CI [−.33, −.09]) and reduced personal accomplishment (r = −.21, 95% CI [−.37, −.05]) showing stronger links than emotional exhaustion (r = −.12, 95% CI [−.17, −.07]). Third, regarding student outcomes, emotional exhaustion was weakly negatively associated with student motivation (r = –.23, 95% CI [–.28, –.17]), but its overall association with student academic achievement was close to zero (r = –.05, 95% CI [–.11, .01]). Although fewer studies examined depersonalization and reduced personal accomplishment in relation to student motivation and achievement, the overall pattern was also negative.

Further meta-regression analyses showed that the source of ratings was an important moderator, and teacher self-reports usually produced stronger negative associations than external ratings, with the association between reduced personal accomplishment and instructional support was r = –.41 in teacher self-reports, but no longer significant in external reports (r = –.03; β = –.35, p < .001). This suggests that relying only on teacher self-ratings may overestimate the relationship between teacher burnout and teaching performance.

Overall, this study shows that teacher burnout is related not only to teachers’ own risk of absenteeism, but also consistently to poorer teacher–student interaction quality and lower student motivation, while its direct association with student academic achievement is relatively weak. The study also highlights that the different dimensions of burnout do not operate in the same way, with depersonalization and reduced personal accomplishment often showing stronger links to teacher–student interactions than emotional exhaustion. Based on these findings, the authors suggest that future teacher support and intervention programs should not focus only on reducing stress or emotional exhaustion, but should also pay attention to maintaining the quality of teacher–student interactions and teachers’ sense of professional competence. Methodologically, future research should combine observations, student reports, and longitudinal designs to capture more accurately the actual effects of teacher burnout on teaching and student development.

Source (Open Access): Wartenberg, G., Aldrup, K., Grund, S., & Klusmann, U. (2026). How strongly is teachers’ burnout related to teacher absenteeism, teacher–student interactions, and student motivation and achievement: A meta-analysis and systematic review. Review of Educational Research, 00346543261455273.

https://doi.org/10.3102/00346543261455273… Read the rest

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Effective Teaching Approach Primary School Education

Support for self-regulated learning in immersive virtual reality – An experimental design for science learning

Immersive virtual reality (IVR) enables users to engage with virtual environments in a highly immersive way. Past studies have found that immersive virtual reality (IVR) may increase students’ cognitive load, distract them from learning tasks, and hinder their knowledge acquisition. To address these challenges, Hsu and Lee (2026) explored the integration of self-regulated learning (SRL) strategies into IVR, and examined their impact on students’ cognitive load. An experimental design was employed, involving 105 students randomly assigned to either an SRL group (n=52) or a non-SRL (NSRL) group (n = 53). A two-way mixed-design ANOVA was conducted to identify differences in the science learning outcomes of the two groups. In addition, partial least squares structural equation modeling (PLS-SEM) was applied to examine the structural relationships among affective factors in IVR environments, cognitive load, and students’ learning outcomes within both the SRL and NSRL conditions.

The findings confirm that, while learning with IVR, extraneous cognitive load exerted negative effects on both SRL gains and science knowledge, whereas germane cognitive load positively influenced these outcomes. First, interactive extraneous cognitive load predicted both lower- and higher-level science knowledge in the NSRL group. Second, environmental extraneous cognitive load negatively predicted SRL gains across both IVR conditions.  Third, a finding common to both the SRL and NSRL groups was that increased student control and active learning significantly enhanced perceived germane cognitive load.

As IVR technology becomes increasingly accessible and user-friendly, it is no longer confined to professional instructional designers; teachers now need the capacity to independently create VR-based learning materials. This shift highlights the importance of preparing teachers not only to use VR tools but also to design pedagogically meaningful IVR content, such as the incorporation of SRL strategies. Accordingly, teachers must recognize the benefits of presence and interaction while remaining cautious of their limitations and potential pitfalls. They should also be mindful that less optimal instructional designs may impose extraneous cognitive load, which can undermine learning outcomes. Most importantly, teachers should foster learners’ autonomy and agency, as these are critical for effective IVR-based learning.

Source (Open Access): Hsu, Y. T., & Lee, S. W. Y. (2026). Support for self-regulated learning in immersive virtual reality–An experimental design for science learning. Education and Information Technologies, 31(1), 269-300.

https://doi.org/10.1007/s10639-025-13807-y… Read the rest

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Achievement Higher Education Primary School Education Secondary School Education

The impact of test preparation on performance in large-scale educational tests: A meta-analysis of experimental studies

A recent meta-analysis examines whether test preparation improves performance in large-scale educational tests. Although schools, commercial institutions, and students invest heavily in preparation courses and materials, existing studies have reported inconsistent effects. Earlier reviews were also concentrated on US college admission and cognitive ability tests and often relied on evidence published before 2000. This study therefore estimates the overall effect of test preparation and examines how it varies across intervention, test, student, and research-design characteristics.

The authors synthesized 28 experimental and quasi-experimental studies, including 92 effect sizes and 16,741 participants across primary, secondary, and tertiary education. The studies covered college admission tests, English proficiency tests, and other large-scale assessments. A hierarchical random-effects model with robust variance estimation was used, while moderator analyses examined intervention breadth, strategy and material use, test characteristics, previous test experience, and study design.

Test preparation produced a significant positive overall effect on test performance (g=.26, 95% CI [.10,.42], p<.001), but heterogeneity was high (I^2=82.44%). Test-specific and narrowly focused interventions were more effective (g=.33) and (g=.32) than broad programs aimed at general knowledge or transferable skills (g=-.04). Interventions teaching test-taking strategies also showed stronger effects (g=.35) than those without such instruction (g=.05). Students with previous testing experience benefited substantially more (g=.69) than those without it (g=.06). However, preparation effects did not transfer significantly to other tests in the same domain (g=.06, p=.502).

Taken together, the findings suggest that test preparation can improve large-scale test scores, but its effectiveness mainly reflects alignment with the target test rather than broader learning gains. The limited transfer effect raises concerns about whether score improvement represents genuine development in the abilities being assessed. Future research should use stronger experimental designs, report intervention processes more clearly, and distinguish test-score gains from transferable learning.

Source (Open Access): Hao, Z., Baird, J. A., Masri, Y. E., & Double, K. (2025). The impact of test preparation on performance of large-scale educational tests: A meta-analysis of experimental studies. Review of Educational Research, 00346543251360775.

https://doi.org/10.3102/00346543251360775… Read the rest