Category: Social and Motivational Outcomes

Categories
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

Categories
K-12 Education Social and Motivational Outcomes

Social and Emotional Learning Programs and Students’ Prosocial Behavior: A Meta-Analysis

A recent meta-analysis conducted by Hung and colleagues examined the effectiveness of school-based social and emotional learning (SEL) programs for K–12 students’ prosocial behavior. Prosocial behavior is conceptualized as any voluntary behavior intended to benefit others, such as helping, sharing, comforting, and defending others. The researchers analyzed 66 studies and 157 effect sizes involving 52,914 youth.

Effect sizes were calculated using Hedges’ g, which includes a small sample bias correction to the effect size estimate to account for small studies. Because most studies contributed multiple effect sizes, the authors used a correlated effects (CE) model with robust variance estimation (RVE) to account for within-study dependence among effect sizes. Approach refers to whether an SEL program takes a curricular, interactional, structural, or combined approach. To examine whether the effect of SEL programs on prosocial behavior was moderated by sample, program, methodological, and publication characteristics, the authors conducted a mixed-effects meta-regression analysis with all moderators added to the model simultaneously. In total, they investigated 14 moderating variables such as approach, school level, urbanicity, and dosage.

The remaining moderating variables yielded no statistically significant differences. Results indicated that effects for rural, suburban, and combination areas were not statistically different from samples from urban areas. Results also indicated that effects were not statistically significantly different between samples with higher versus lower proportions of students qualifying for free or reduced-price lunch. Effects of curricular and curricular combined with structural or interactional approaches were not significantly different from SEL programs that only used an interactional approach. Findings indicated that effects of studies delivered at Tier 2 were not statistically different from Tier 1 studies. Studies that used a quasi-experimental design and single-group pre–post design yielded similar effects on prosocial behavior compared to studies that used a randomized controlled trial. Effects were similar across different types of prosocial behavior measures. Effects from studies that did not meet baseline equivalence were not statistically significantly different from studies that met baseline equivalence, and effects from studies that did not report implementation fidelity were not statistically significantly different from studies that reported fidelity of implementation. Results indicated that effects from studies conducted across earlier decades were not statistically significantly different from studies conducted more recently, and effects were similar for peer-reviewed and non-peer-reviewed studies.

The authors further noted that most studies were conducted with elementary school children (56%), the majority implemented universal Tier 1 interventions (89%), and a curricular approach was the most common (77%). Additionally, a considerable proportion of studies did not report key demographic data, with 71% failing to report free or reduced-price lunch rates.

A key implication for practice from this meta-analytic review is that school-based SEL programs are effective in promoting K–12 students’ prosocial behavior, and that “more is not necessarily better” — a moderate dosage and moderate duration may be most ideal. Future policy and practice should take into account this “less is more” finding. At the same time, more research is needed involving secondary schools, rural schools, non-curricular approaches, and diverse student populations in order to fully understand the effectiveness of SEL programs.

Source (Open Access): Hung, C., Brass, N. R., Brockmeier, L., Bergin, C., Imler, M., & Luper, S. B. (2026). Social and Emotional Learning Programs and Students’ Prosocial Behavior: A Meta-Analysis. Review of Educational Research, 00346543261438462.

https://doi.org/10.3102/00346543261438462… Read the rest

Categories
Kindergarten Social and Motivational Outcomes

An experimental study exploring human–AI complementarity in early social-emotional learning

A recent experimental study by Raave and colleagues employed a mixed factorial design to compare a pedagogical conversational agent (PCA) with human educators in facilitating early social-emotional learning (SEL). Eighteen international early childhood and primary teachers and an AI-driven PCA each conducted three standardized story-based SEL activities with a static AI child named “Ali,” designed to exhibit challenging characteristics including distractibility and limited empathy. This controlled comparison yielded 108 total observations across activities focusing on prosocial behavior, perspective-taking, and healthy guilt induction.

Expert raters, blind to facilitator type, coded interactions for three evidence-based pedagogical support techniques—cognitive-conceptual, procedural, and emotional support—using binary scales, and assessed broader teaching quality across six dimensions adapted from the Danielson Framework for Teaching on a 1-4 scale. The study maintained high inter-rater reliability across all measures (Cohen’s κ=.65−.80 for support techniques; quadratic weighted κ=.61−.76 for quality indicators).

Results revealed significant differences in facilitation approaches. Mixed ANOVA showed educators used more support techniques overall (M=7.57 vs. M=5.47) and substantially more cognitive-conceptual support than the PCA (M=15.28 vs. M=6.46, p<.001), while the PCA provided slightly more procedural support (p=.026). Quality ratings demonstrated complementary strengths: the PCA scored higher in relational domains such as respect and rapport (d=1.86) and flexibility (d=1.06), whereas educators excelled in intellectual engagement including questioning techniques (d=0.72) and engaging students in learning (d=0.96).

The findings reveal meaningful pedagogical asymmetry suggesting human-AI complementarity rather than competition. The PCA maintained consistent emotional validation and procedural structure but tended to be “caring but pedagogically shallow,” while educators provided deeper cognitive-conceptual scaffolding and moral reasoning guidance with greater affective variability. The authors propose a strategic division of labor where PCAs manage routine structuring and baseline emotional support, while human educators focus on complex social-emotional reasoning and ethical development. Future research should explore direct co-facilitation models in authentic classroom settings.

Source (Open Access): Raave, D. K., Colasante, T., Roa, E. R., Martinez, J. C. R., Li, H., Mukherjee, S., & Malti, T. (2026). An experimental study exploring human–AI complementarity in early social-emotional learning. Computers and Education Open, 100331.

https://doi.org/10.1016/j.caeo.2026.100331… Read the rest

Categories
K-12 Education Social and Motivational Outcomes

The Gap Between Teachers’ Self-Efficacy, Management Strategies, and Actual Classroom Management Behaviors

Shi and colleagues combined questionnaire survey methods with AI-supported classroom behavior analysis to examine the relationships among teachers’ classroom management self-efficacy, self-reported classroom management strategies, and actual classroom management behaviors observed by AI. The study involved 345 Chinese K-12 in-service teachers, collected questionnaire data on their classroom management self-efficacy and strategies, and analyzed 673 valid classroom video recordings, totaling 461.74 hours. The research team developed an AI-supported multimodal classroom management behavior analysis tool that automatically identified teachers’ praise statements, criticism statements, discipline-related statements, positive tone of voice, proportion of proximity to students, and proportion of visual attention to students through text, audio, and image data. This allowed the researchers to test whether teachers’ beliefs, reported strategies, and actual behaviors were consistent.

The results showed that teachers with higher classroom management self-efficacy reported using all types of classroom management strategies more frequently, and these differences were all statistically significant. For example, in praise strategies, the high self-efficacy group had a mean rank of 214.43, significantly higher than the low self-efficacy group’s 128.84 (Z = –8.74, p < .001). In corrective feedback strategies, the high group had a mean rank of 206.27, compared with 137.54 in the low group (Z = –6.67, p < .001). In preventive management strategies, the high group had a mean rank of 221.84, whereas the low group had 120.95 (Z = –9.81, p < .001). In commands/transition strategies, the high group had a mean rank of 220.69, compared with 122.17 in the low group (Z = –9.68, p < .001). However, when actual classroom videos were analyzed through AI, no significant differences emerged between the high and low self-efficacy groups on most observable classroom management behaviors. The only finding was a marginal tendency for teachers with lower self-efficacy to use more discipline-related statements (p = .07), suggesting that they may rely more on disciplinary language to maintain order.

Regarding the relationship between self-reported strategies and AI-observed behaviors, the results showed that consistency existed only in some domains. Teachers’ self-reported praise strategies were significantly positively related to AI-detected praise statements and positive tone of voice, although the effect sizes were relatively small. In contrast, corrective feedback and preventive management strategies were not significantly associated with their corresponding AI-based behavioral indicators. Notably, self-reported commands/transition strategies were significantly negatively related to AI-observed discipline-related statements, meaning that the more often teachers reported using clear instructions and transition management, the less frequently discipline-related statements appeared in their classrooms. Overall, teachers’ reported use of strategies only partially corresponded to their actual classroom behaviors, and more concrete and observable strategies, such as praise, were more likely to be validated by AI observation.

Overall, this study suggests that teachers’ beliefs, strategies, and behaviors in classroom management are not always highly aligned. Although teachers with higher self-efficacy reported using more effective strategies, they did not necessarily demonstrate clearly different classroom management behaviors in practice. Only specific and easily identifiable strategies, such as praise, were more readily confirmed through AI observation. The study therefore highlights a misalignment between teachers’ beliefs and their actual teaching behaviors, while also demonstrating the considerable potential of AI for large-scale, non-intrusive, and evidence-based research on classroom management. For educational research and teacher development, the study serves as a reminder that relying solely on teachers’ self-report questionnaires may overestimate the consistency between reported strategies and real classroom behavior. Future work should combine self-report data with more objective methods such as AI observation to gain a fuller understanding of actual classroom management practices.

Source (Open Access): Shi, Y., Wang, Z., Chen, Z., Ren, D., Liu, H., & Zhang, J. (2026). Do teachers … Read the rest

Categories
Primary School Education Social and Motivational Outcomes

Effects of virtual reality exercise on social skills and emotional recognition among children with autism spectrum disorder: a meta-analysis of randomized controlled trials

A meta-analysis by Cui and colleagues assessed the effects of virtual reality (VR) exercise on social skills (SS) and emotional recognition (ER) among children with autism spectrum disorder (ASD). Analysing data from randomized controlled trials published between January 2005 and October 2025 across PubMed, Web of Science, Scopus, and EBSCO databases, the authors investigated the relationship between VR exercise interventions and children’s social-emotional development outcomes.

The authors employed standardized mean difference (SMD) as the effect size, utilizing random-effects models to synthesize results across studies. VR exercise interventions were compared with standard treatment approaches. The methodology included comprehensive database searches using keywords: virtual reality, autism spectrum disorder, and children. Quality assessment followed Cochrane Handbook guidelines, with heterogeneity evaluated through I² statistics. Subgroup analyses examined intervention duration effects (< 14 weeks versus ≥ 14 weeks), and secondary outcomes included cognitive function, anxiety, language function, and depression.

The results revealed significant positive effects of VR exercise on SS (SMD = 0.94 [0.71, 1.17], p < 0.05, I² = 74%) and ER (SMD = 0.42 [0.18, 0.65], p < 0.05, I² = 0%). Furthermore, subgroup analysis demonstrated that interventions lasting less than 14 weeks (SMD = 0.63 [0.36, 0.91], p < 0.05, I² = 0%) and those exceeding 14 weeks (SMD = 1.70 [1.27, 2.13], p < 0.05, I² = 44%) both substantially improved SS, with longer interventions showing greater effect sizes. Additionally, VR exercise improved cognitive function (SMD = 0.49 [0.06, 0.93], p < 0.05, I² = 0%) and reduced anxiety (SMD = 0.56 [1.10, 0.02], p < 0.05, I² = 0%). Notably, effects on language function and depression remained unclear due to insufficient evidence.

The findings underscore the effectiveness of VR exercise as a technological intervention modality superior to standard treatment approaches in enhancing social-emotional competencies among children with ASD. Therefore, future clinical practice should consider integrating VR exercise interventions into rehabilitation programs for children with ASD, particularly emphasizing intervention duration optimization to maximize therapeutic benefits. The moderate effect sizes and cautious interpretation regarding cognitive and anxiety outcomes require validation through larger-scale longitudinal studies with standardized outcome measures.

Source (Open Access): Cui, T., Ariffin, R. B., Wang, X., & Wang, X. (2026). Effects of virtual reality exercise on social skills and emotional recognition among children with autism spectrum disorder: a meta-analysis of randomized controlled trials. BMC psychology.

https://doi.org/10.1186/s40359-026-04160-x… Read the rest

Categories
Primary School Education Secondary School Education Social and Motivational Outcomes

Teachers’ Teaching Emotions, Teaching Mindset, and AI Readiness

Ti and colleagues employed a cross-sectional survey design with hierarchical regression and moderation analyses to examine how in-service teachers’ teaching emotions and teaching ability mindset predict their AI readiness, and whether mindset moderates the relationship between emotions and AI readiness. The study included 424 in-service teachers in China (mean age = 38.76 years) from both primary and secondary schools. AI readiness was measured using Wang et al.’s (2023) four-dimensional framework, including cognition, ability, vision, and ethics. Teaching emotions were categorized into positive and negative emotions, and teaching mindset was classified as growth or fixed. Gender and social desirability bias were controlled in the analyses, and interaction effects were tested using the PROCESS macro.

The results showed that positive teaching emotions significantly and positively predicted all four dimensions of AI readiness (B ≥ .48, p < .001), whereas negative emotions did not significantly predict any dimension (|B| ≤ .06, p ≥ .309). Regarding mindset, a growth teaching mindset had significant positive effects on cognition, ability, vision, and ethics (B ≥ .18, p < .01), indicating that teachers who view teaching ability as developable are better prepared to respond to AI-related educational change. Interestingly, a fixed teaching mindset did not uniformly produce negative effects; instead, it positively predicted the cognition and ability dimensions (B ≥ .17, p < .01), although it was not significant for vision and ethics. Overall, the inclusion of emotions and mindset in the models yielded medium to large effect sizes (.28 ≤ f² ≤ .36), suggesting substantial explanatory power.

Moderation analyses further revealed that a growth teaching mindset strengthened the positive relationship between positive emotions and AI cognitive readiness (B = .11, p < .05). In other words, teachers with both high positive emotions and a strong growth mindset demonstrated higher levels of understanding regarding AI roles and functions. In contrast, a fixed teaching mindset moderated the relationship between negative emotions and the cognitive dimension (B = .10, p < .05). When fixed mindset was low, negative emotions significantly reduced cognitive readiness (B = –.18, p < .05), whereas this effect was not significant when fixed mindset was high. Notably, moderation effects were observed only for the cognition dimension, suggesting that the cognitive aspect of AI readiness is particularly sensitive to the interaction between emotional and mindset resources.

Overall, this study indicates that teachers’ readiness for AI integration in education is influenced not only by technical training or institutional support but also by their emotional experiences and beliefs about the malleability of teaching ability. Positive emotions and a growth teaching mindset serve as important psychological resources that enhance AI readiness, especially in shaping teachers’ cognitive understanding of AI. The authors recommend that AI-related professional development initiatives incorporate emotional regulation support and mindset cultivation to foster more comprehensive and sustainable AI readiness among teachers.

Source (Open Access): Ti, Y., Sun, Y., & Li, X. (2026). Predicting in-service teachers’ AI readiness from emotions in teaching and mindsets about teaching ability: Testing the direct and moderating effects. Teaching and Teacher Education, 175, 105433.

https://doi.org/10.1016/j.tate.2026.105433… Read the rest