Author: cuspbeib

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Effective Teaching Approach Secondary School Education

When AR Makes Learning Enjoyable but Not Necessarily More Effective

Olea-Ibarra et al. (2025) examine how augmented reality (AR) affects middle school students’ enjoyment, epistemic emotions, and knowledge acquisition in science learning. Although AR is often promoted as an immersive tool that can make abstract content more engaging, the study addresses an important unresolved issue: whether greater enjoyment and emotional stimulation during AR learning actually translate into stronger learning outcomes, especially for younger learners. The study focuses on seventh-grade students learning about the solar system and compares AR-based learning with a non-immersive static multimedia condition.

Using a quasi-experimental field design, the study involved 47 seventh-grade students from an international middle school in Mexico. Students were assigned to either an AR condition or a control condition by class group. In the AR condition, students used the Jigspace app on tablets to spatially explore solar system content, including planets’ positions and characteristics. In the control condition, students learned the same content through an interactive PowerPoint presentation with static images and text on tablets. Knowledge was assessed through pre- and post-tests, while enjoyment and epistemic emotions were measured repeatedly before, during, and after the learning activity.

Results show a mixed and somewhat counterintuitive pattern. Students in the AR condition reported significantly higher enjoyment than those in the control condition. The averaged enjoyment scores during and after learning were higher in the AR group (M = 3.86, SD = 0.84) than in the control group (M = 2.84, SD = 0.66), with a large effect size (d = 1.37). AR also elicited stronger positive epistemic emotions, including surprise (d = 1.09), curiosity (d = 1.05), and excitement (d = 1.07). However, the control group achieved greater knowledge gains than the AR group. Knowledge gain was substantially higher in the control condition (M = 12.20%, SD = 6.90) than in the AR condition (M = 4.66%, SD = 6.41), with a large effect size (d = 1.13). A repeated measures ANOVA further showed a significant interaction between condition and measurement time, indicating that knowledge gain reached statistical significance only in the control condition, F (1,45) = 15.049, p < .001. The mediation analysis also indicated that enjoyment did not significantly mediate the relationship between AR use and learning gains (b = 1.857, SE = 1.356, p = .171). These findings suggest that while AR can make learning more emotionally engaging, this affective advantage does not automatically lead to better knowledge acquisition.

Overall, the study provides a valuable cautionary perspective on immersive technologies in school science learning. AR may enhance enjoyment and stimulate positive epistemic emotions, but it may also distract learners or create affective overload when emotional engagement competes with task-focused cognitive processing. The findings highlight the need to design AR learning environments that carefully balance immersion, emotion, and instructional focus. Rather than assuming that more engaging media will automatically improve learning, the study suggests that educators should use AR with clear pedagogical guidance, especially when working with younger learners.

Source (Open Access): Olea‐Ibarra, D., Hartmann, C., & Bannert, M. (2025). The role of enjoyment and epistemic emotions in middle school AR learning: A quasi‐experimental field study. Journal of Computer Assisted Learning, 41(2), e70016.

https://doi.org/10.1111/jcal.70016… Read the rest

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Higher Education Social and Motivational Outcomes

Unveiling the hidden key: can help-seeking behaviors bridge the gap between doctoral supervision relationship and research self-efficacy of PhD candidates?

A recent cross-sectional study by Jeanne Boisselier examines the relationship between the Doctoral Supervision Relationship (DSR) and Research Self-Efficacy (RSE) among PhD candidates, with a particular focus on the mediating role of Help-seeking behaviors. Specifically, the study investigates whether adaptive and non-adaptive help-seeking act as mechanisms that bridge the gap between supervisory support and students’ confidence in their research abilities. The sample comprised 178 PhD candidates enrolled in French universities and research institutes.

The authors employed ANOVAs, linear regressions, and mediation analyses to examine the associations among the study variables. Key constructs were measured using multi-dimensional 5-point Likert scales. The DSR was assessed across five dimensions: Autonomy Support, Competence Support, Relatedness Support, Communication Quality, and Supervisor Involvement. Help-seeking behaviors were categorized as Adaptive Help-seeking, reflecting proactive strategies to gain mastery, and Non-adaptive Help-seeking, reflecting avoidance of support due to perceived threats to self-esteem. Research Self-Efficacy was operationalized as students’ subjective belief in their ability to perform research-related tasks and processes successfully.

Results indicate that all five dimensions of the DSR significantly and positively predict RSE: autonomy support (β = .20, p < .01), competence support (β = .27, p < .01), relatedness support (β = .29, p < .01), communication quality (β = .31, p < .01), and supervisor involvement (β = .28, p < .01). Mediation analyses reveal that Adaptive Help-seeking partially mediates the relationship between DSR and RSE and fully mediates the effect of Autonomy Support. In contrast, Non-adaptive Help-seeking functions as a negative mediator: effective supervision reduces avoidant behaviors, thereby preventing a decline in research confidence. Despite the significance of these behavioral pathways, the direct influence of supervisory support on RSE remains stronger, emphasizing the primary importance of the quality of the supervision relationship itself.

The findings highlight the need for supervisors and institutions to attend to the multifaceted nature of doctoral guidance, particularly by enhancing communication quality and autonomy support to encourage adaptive help-seeking among students.

Source (Open Access): Boisselier, J. (2026). Unveiling the hidden key: can help-seeking behaviors bridge the gap between doctoral supervision relationship and research self-efficacy of PhD candidates? Higher Education Research & Development, 45(1), 33-48.

https://doi.org/10.1080/07294360.2025.2526816… Read the rest

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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