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Effective Teaching Approach Programme Evaluation Secondary School Education

Linking Classrooms and Museums for Self-Regulated STEM Learning

Lewalter et al. (2025) examine how school-based factors shape students’ self-regulated STEM learning during and after a museum visit. The study addresses an important challenge in STEM education: how to connect classroom learning with meaningful real-world contexts beyond school. Focusing on a museum exhibition about mobility and traffic, the authors investigate whether students’ learning strategy knowledge, prior knowledge, prior interest, and classroom preparation or follow-up activities predict their learning processes during the visit and their learning outcomes three months later.

The study used a pretest–posttest–follow-up design with 409 Grade 10 students from 21 classes in 12 German high schools. Students visited the Deutsches Museum München and explored a tablet-supported exhibition programme titled Mobility Transition—What Moves Us in the Future? Traffic and Mobility Challenges of the 21st Century. During the 90-minute visit, students worked in small groups and engaged in self-guided exploration of selected exhibits. Questionnaire data were collected one week before the visit, immediately after the visit, and approximately three months later to measure learning strategy knowledge, preparation and follow-up activities, situational interest, basic psychological needs, perceived content relevance, engagement, knowledge, and individual interest.

Results show that most students did not experience systematic classroom integration of the museum visit: 75% reported no classroom preparation, and 60.4% reported no follow-up activities after the visit. Students’ metacognitive and motivational learning strategy knowledge positively predicted several aspects of the learning process, including perceived content relevance, basic need satisfaction, situational interest, and engagement. However, when prior individual interest was included in the model, it became the strongest predictor of learning process variables, suggesting that students’ existing interest plays a central role in shaping museum-based STEM learning.

For medium-term learning outcomes, follow-up activities in the classroom were especially important. Three months after the museum visit, students who experienced classroom follow-up showed higher self-perceived and objective knowledge. Knowledge of learning strategies also contributed to self-perceived knowledge, whereas students’ later individual interest was mainly predicted by their prior individual interest. Overall, the findings highlight that museum visits should not be treated as isolated field trips. Instead, their educational value depends on stronger links between classroom teaching and out-of-school learning, particularly through helping students develop self-regulated learning strategies and providing meaningful follow-up activities after the visit.

Source (Open Access): Lewalter, D., Neubauer, K., & Moser, S. (2025). Self-regulated STEM learning in museums—the role of learner characteristics and visit-related activities in school. International Journal of STEM Education12(1), 56.

https://doi.org/10.1186/s40594-025-00577-9Read the rest

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Educational Administration and Leadership Higher Education K-12 Education

The impact of generative artificial intelligence on students’ higher order thinking: Evidence from a three-level meta-analysis

Given the current trends in AIEd, it is crucial to synthesize the overall impact of Gen-AI on HOT, as Gen-AI has been creatively applied by researchers to assist in teaching and learning. Concerns exist in current discourse that Gen-AI may harm HOT. However, empirical research on the specific effects of Gen-AI on HOT remains scattered, and no consensus has been reached. Nie et al.’s (2025) study synthesized 19 experimental and quasi-experimental studies conducted in different contexts (k=68), involving 2,347 participants. A three-level meta-analysis was employed to account for within- and between-study variability, assessing the impact of Gen-AI on HOT and exploring the effects of moderators.

The results revealed that Gen-AI had a significant positive effect on enhancing students’ HOT (Hedges’s g=0.851, p<0.001). Moderator analyses were conducted based on impact target, Gen-AI elements, study contexts, and methodological characteristics. When the sample size was less than 80, the promotion effect was more significant. Both short-term interventions (less than 4 weeks) and long-term interventions (more than 8 weeks) have the potential to produce significant positive effects.

These findings offer vital policy implications for integrating Gen-AI into education. First, educational authorities should mandate smaller class sizes or optimized student-to-teacher ratios in AI-driven curricula, as large groups dilute Gen-AI’s efficacy in fostering HOT. Second, curriculum frameworks must transition from short-term pilots to extended, long-term intervention timelines, given that 4-8 weeks is insufficient for deep cognitive development. Lastly, policymakers should fund targeted, granular research into AI’s specific impacts on different dimensions of HOT to guide the scaling of intelligent, personalized, and adaptive educational ecosystems.

Source (Open Access): Nie, X., Tian, Y., Liu, M., Wu, D., & Guo, Y. (2025). The impact of generative artificial intelligence on students’ higher order thinking: Evidence from a three-level meta-analysis. Education and Information Technologies30(17), 25359-25390.https://doi.org/10.1007/s10639-025-13735-xRead the rest

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Higher Education Language Development

Task Offloading to GenAI in the Writing Feedback Process and Its Effects on Writing Development

Lai and colleagues used a quasi-experimental design to investigate whether offloading different tasks to GenAI in the “draft–assess–revise” workflow affects English-as-a-foreign-language (EFL) students’ writing development. Conducted over seven weeks, the study involved 101 Chinese university students and compared three conditions: (1) GenAI drafting, student assessment, student revision; (2) student drafting, GenAI assessment, student revision; and (3) student drafting, student self-assessment, student revision.

Results showed that the GenAI drafting group performed best in writing ability (M = 18.21, SD = 2.21), significantly higher than both the GenAI assessment group (M = 16.30, SD = 1.70) and the no GenAI group (M = 15.15, SD = 1.79). This indicates that including GenAI in the feedback workflow generally benefits writing, but offloading the drafting task rather than the assessment task produces the strongest effects.

Regarding cognitive processes, the GenAI drafting group showed significantly higher cognitive engagement, utilizing more total prompts (median 20.50 vs. 9.00, U = 195, p < .001) and more learning-oriented prompts (median 10.50 vs. 8.00, U = 398.50, p < .05) than the assessment group. They also outperformed the other two groups in metacognition in problem identification (M = 4.41), argumentation (M = 2.82), and providing constructive feedback (M = 4.97) during peer review.

In writing self-efficacy, the GenAI drafting group demonstrated the largest improvement (M = 4.74, SD = .55), significantly higher than the other groups, whereas the GenAI assessment group (M = 4.42, SD = .53) and no GenAI group (M = 4.45, SD = .78) did not differ significantly. Interviews revealed these students experienced a stronger sense of control and competence because they could critically evaluate and revise AI-generated drafts. In contrast, some students in the GenAI assessment group worried about overreliance on AI feedback, leading to “false confidence.”

Overall, the study highlights that the educational value of GenAI in writing feedback depends on which tasks are offloaded to AI. Offloading the drafting task to GenAI while having students handle assessment and revision promotes deeper cognitive engagement, stronger metacognitive understanding, and higher self-efficacy. The authors recommend that writing instruction should prioritize AI-generated drafts paired with student evaluation and revision, serving as a catalyst for critique and reflection rather than replacing student thinking.

Source (Open Access): Lai, C., Pan, M., Guo, K., & Cui, Y. (2026). What task to offload to GenAI in the writing feedback process? – Effects of task-offloading approaches on EFL learners’ writing skill development. Computers & Education252, 105675.

https://doi.org/10.1016/j.compedu.2026.105675Read the rest

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Achievement Higher Education

Student agency in Colombian higher education: a dual-pathway model of mediation and moderation between student engagement and academic achievement

A recent quantitative study by Torres Castro and Pineda-Baéz examines the relationship between student engagement and academic achievement, with particular attention to how student agency functions as both a mediating and a moderating mechanism in this relationship.

The study surveyed 1,713 final-year students from six accredited private universities across five regions in Colombia. Grounded in social cognitive theory and the ecological model of student engagement, the study conceptualizes student engagement as a multidimensional construct. It encompasses ten indicators across four domains: academic challenge, learning with peers, experiences with faculty, and campus environment. Student engagement is assessed through the National Survey of Student Engagement, with scores standardized on a 0 to 60 scale. Student agency is operationalized as agentic engagement through the five-item Agentic Engagement Scale. This scale captures proactive behaviors such as expressing preferences, asking questions, and suggesting adjustments to instruction. Academic achievement was measured by self-reported cumulative grade point average on Colombia’s standardized 0.0 to 5.0 scale. The study employed hierarchical regression with robust standard errors, structural equation modeling, and bootstrap-based mediation analysis using R version 4.3.1.

The findings reveal that among the ten engagement indicators, only collaborative learning and student-faculty interaction are positively and independently associated with academic achievement. Among the five agentic engagement behaviors, only expressive voice emerged as a significant mediator, channeling approximately six percent of the total effect of collaborative learning on grade point average. Expressive voice also moderated the relationship between student-faculty interaction and grade point average in a compensatory pattern. The association between faculty interaction and achievement was stronger among students with lower levels of expressive voice, whereas it was attenuated yet remained positive among those with higher levels. Although effect sizes were modest, the findings demonstrate that student agency operates as a context-dependent dual mechanism.

The study further suggests that instructional practices should combine structured guidance with opportunities for student-initiated contribution, and that support systems should prioritize students with lower levels of agentic engagement.

Source (Open Access): Torres Castro, U. E., & Pineda-Baéz, C. (2026). Student agency in Colombian higher education: a dual-pathway model of mediation and moderation between student engagement and academic achievement. Higher Education, 1-20.

https://doi.org/10.1007/s10734-026-01669-3Read the rest

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K-12 Education Programme Evaluation

The Effects of Integrated STEM Education on K12 Students’ Achievements: A Meta-Analysis

Integrated STEM education refers to T&L of scientific, technological, engineering, and mathematical knowledge and skills in integrative ways, emphasizing the connection between abstract knowledge and real-world problems. Integrated STEM education is characterized by four core features: multidisciplinary integration, real-world application, authentic inquiry or design-based practice, and active student learning. Based on 124 extracted and coded studies (2010-2022), Chen et al.’s (2025) meta-analysis reports on the effects of integrated STEM education based on three main types of interventions: (1) adopting integrated STEM education, (2) using extra teaching and learning strategies to enhance integrated STEM education, and (3) using specific learning technologies to support integrated STEM education.

All three types of interventions yielded a medium effect on knowledge acquisition and a small effect on student perceptions. Besides, adopting integrated STEM education had a large effect on cognitive skills; using extra teaching and learning strategies in integrated STEM programs produced a medium effect on cognitive skills and problem-solving task performance; using specific learning technologies had a small effect on problem-solving task performance. Some factors, such as task type (inquiry or design-based task) and program duration, may influence STEM learning outcomes.

To maximize the efficacy of integrated STEM education, practitioners should embed its four core characteristics into curriculum design while favoring short-to-medium duration programs (one month to a semester). Educators must carefully balance hands-on design and minds-on inquiry tasks by providing necessary scaffolding tailored to students’ prior knowledge. Furthermore, deploying targeted instructional strategies and learning technologies can enhance engagement with complex, real-world problems. Ultimately, evaluating these programs requires a multidimensional approach that prioritizes skill development and practical problem-solving performance alongside traditional knowledge acquisition.

Source (Open Access): Chen, B., Chen, J., Wang, M., Tsai, C. C., & Kirschner, P. A. (2026). The effects of integrated STEM education on K12 students’ achievements: A meta-analysis. Review of Educational Research96(2), 619-668.

https://doi.org/10.3102/00346543251318297Read the rest

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Language Development Primary School Education Secondary School Education

The Effects of Interventions for Students With Reading Difficulties in Grades 4–12

Killingly and colleagues conducted a systematic review and meta-analysis of school-based interventions for students with reading difficulties in Grades 4–12 published between 2011 and 2023. The study examined the overall effectiveness of these interventions and further tested whether study characteristics, sample characteristics, and intervention characteristics moderated their effects. A total of 104 publications and 586 effect sizes were included, representing 97,114 participants. Methodologically, the authors used a Correlated and Hierarchical Effects model combined with robust variance estimation to address dependency among multiple outcomes and effect sizes within the same study, while also estimating effects across overall reading performance and specific reading domains.

The results showed that, overall, reading interventions had a small but significant positive effect for students with reading difficulties in Grades 4–12, with an overall effect size of g = 0.212 (95% CI [0.163, 0.261], p < .001). This suggests that although the gains were not large, these interventions did produce reliable improvements in students’ reading performance. Across specific reading domains, the strongest effects were found for vocabulary (g = 0.422), followed by decoding/word recognition (g = 0.199) and reading comprehension (g = 0.187). Fluency showed only a very small but significant effect (g = 0.080), spelling was not significant (g = 0.015), and phonological processing, although showing a larger effect size on the surface (g = 0.531), did not reach significance and was therefore considered unstable. Overall heterogeneity was very high (I² = 89.71%), indicating that differences in study design and sample characteristics had a substantial influence on intervention effectiveness. GRADE assessment further suggested that the overall quality of evidence ranged from moderate to high, with the strongest evidence for fluency, moderate evidence for vocabulary, and moderate-to-low evidence for phonological processing.

Moderator analyses showed that intervention effects varied according to both study and sample conditions. Overall, more recently published studies showed stronger effects (β = 0.015), and journal articles produced significantly larger effects (g = 0.268) than research reports (g = 0.062). In terms of sample characteristics, low socioeconomic status was not significantly related to overall effects, but a higher proportion of students with learning disabilities was associated with slightly stronger effects (β = 0.006). For students from a language background other than English, overall differences were not significant, but in the vocabulary domain, a greater proportion of such students was associated with stronger effects (β = 0.016), suggesting that vocabulary instruction may be particularly important for this group. Regarding intervention design, intervention focus, duration, and measurement type were all significant moderators. Comprehension-focused interventions showed relatively strong overall effects (g = 0.313), multicomponent interventions showed stable effects (g = 0.178), and word study interventions had smaller effects (g = 0.096), whereas vocabulary-focused interventions, though fewer in number, showed the largest effect (g = 0.716). Shorter interventions were actually associated with stronger effects, with effect sizes of g = 0.405 for 0–5 hours and g = 0.409 for 6–15 hours. In addition, researcher-developed measures yielded significantly larger effects (g = 0.542) than standardized measures (g = 0.127). Although there was no significant overall difference between interventions delivered by teachers and those delivered by researchers, in vocabulary interventions teacher-led delivery produced stronger effects (g = 0.733) than researcher-led delivery (g = 0.249), suggesting that classroom teachers may hold particular advantages in providing vocabulary support.

Overall, this study shows that reading interventions for older students with reading difficulties are indeed effective, although the magnitude of their effects depends on the reading domain being targeted and on the design of the intervention. Vocabulary and reading comprehension appear to be the most promising focuses, while multicomponent interventions also demonstrate stable benefits. By … Read the rest