Category: Social and Motivational Outcomes

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Primary School Education Secondary School Education Social and Motivational Outcomes

The Relationship Between Teachers’ Character Virtues, Engagement, and Well-Being

Angelini and colleagues employed a cross-sectional survey design combined with path analysis to examine how three teacher character virtues—caring, inquisitiveness, and self-control—influence teachers’ work engagement and overall well-being, and to further test the mediating roles of burnout and teacher self-efficacy. The study involved 339 in-service teachers in Italy from both primary and secondary education, and collected data on character virtues, burnout, self-efficacy, work engagement, and psychological well-being to examine both direct and indirect relationships among these variables.

The results showed that the three character virtues exerted significant overall positive effects on teachers’ engagement and well-being. Correlational analyses indicated that inquisitiveness, caring, and self-control were all positively associated with self-efficacy, work engagement, and well-being, and negatively associated with burnout. Path analysis further revealed that inquisitiveness and self-control significantly reduced burnout (β = –.142, p < .05; β = –.235, p < .001, respectively) and enhanced teacher self-efficacy (β = .206, p < .01; β = .191, p < .01). Caring, by contrast, mainly influenced outcomes through increasing self-efficacy (β = .171, p < .01) and did not directly reduce burnout. Burnout had strong negative effects on work engagement (β = –.528, p < .001) and well-being (β = –.324, p < .001), whereas self-efficacy significantly increased engagement (β = .212, p < .001) and well-being (β = .219, p < .001), highlighting their central mediating roles in the model. Overall, the model explained 35.6% of the variance in work engagement and 45.7% of the variance in well-being.

Notably, the mechanisms through which different character virtues operated were not identical. Inquisitiveness had direct effects on both work engagement (β = .095, p < .05) and well-being (β = .122, p < .05), as well as significant indirect effects through burnout and self-efficacy. Caring primarily affected well-being (β = .184, p < .001), with its influence on work engagement largely mediated by self-efficacy. Self-control did not directly predict engagement or well-being, but indirectly promoted both outcomes by reducing burnout and enhancing self-efficacy. These findings suggest that teacher character virtues influence professional functioning through multiple psychological and occupational pathways rather than a single uniform mechanism.

Overall, this study demonstrates that teachers’ character virtues constitute important personal resources for fostering professional engagement and psychological well-being, with burnout and self-efficacy serving as key mechanisms linking character to well-being. The authors emphasize that teacher well-being and burnout should be viewed as two ends of the same continuum, and recommend that future teacher support and professional development programs incorporate character-based interventions to simultaneously reduce burnout risk and enhance teachers’ professional vitality and overall well-being.

Source (Open Access): Angelini, G., Mamprin, C., Buonomo, I., Benevene, P., & Fiorilli, C. (2026). Virtues, engagement, and well-being in teachers: Associations with burnout and self-efficacy in a path analysis model. Teaching and Teacher Education, 169, 105284.

https://doi.org/10.1016/j.tate.2025.105284… Read the rest

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Social and Motivational Outcomes

Teacher professional development of digital pedagogy for inclusive education in post-pandemic era

Shi and colleagues adopted a sequential mixed-methods design to examine how teachers’ digital teaching competence and digital self-efficacy influence their work engagement and emotional exhaustion in inclusive education settings. The first phase surveyed 478 teachers and used structural equation modeling to test the relationships among four core constructs. This was followed by a two-week professional development experiment based on the TPACK framework to evaluate whether strengthening teachers’ digital competence could effectively enhance their professional well-being.

The findings showed that teachers’ digital teaching competence was a strong predictor of self-efficacy (β = .848, p < .001), and significantly increased work engagement (β = .455, p < .001) while reducing emotional exhaustion (β = –.339, p < .001). Self-efficacy also significantly improved engagement (β = .300, p < .001) and reduced exhaustion (β = –.390, p < .001), indicating a chain mechanism from digital teaching competence → self-efficacy → teacher well-being.

The professional development experiment further supported these results. Compared to the control group, the experimental group showed significant gains in digital teaching competence (F = 22.085, ηp² = .290), self-efficacy (F = 32.296, ηp² = .374), work engagement (F = 14.764, ηp² = .215), and emotional exhaustion (F = 15.208, ηp² = .220). All pre- to post-test improvements in the experimental group reached high levels of significance, whereas no significant changes were observed in the control group.

This study highlights digital teaching competence as a key factor supporting teachers’ professional well-being. Structured, TPACK-informed short-term professional development can effectively strengthen teachers’ self-efficacy, enhance work engagement, and reduce emotional exhaustion. The authors recommend that educational institutions treat digital teaching competence as an essential component of teacher professional development, particularly to support sustained growth and psychological well-being in inclusive education contexts.

 

Source (Open Access): Shi, Y. R., Sin, K. F. K., & Wang, Y. Q. (2025). Teacher professional development of digital pedagogy for inclusive education in post-pandemic era: Effects on teacher competence, self-efficacy, and work well-being. Teaching and Teacher Education, 168, 105230.

https://doi.org/10.1016/j.tate.2025.105230… Read the rest

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Primary School Education Social and Motivational Outcomes

More Than Just Grades: Why Social Emotional Learning Matters in Early Hong Kong Education

Educational transition from kindergarten to primary school in Hong Kong is a key milestone in children’s lives. While this milestone is known to shape long-term academic and social success, existing literature is mostly in Western contexts, leaving a gap in understanding the experience in Eastern educational systems characterized by high academic pressure and distinctive cultural values. Zhoc, Tse & King (2025) aim to examine the multifaceted transition experiences of children, specifically focusing on their academic and social challenges. By identifying these factors, Zhoc, Tse & King (2025) seek to highlight the necessity of Social and Emotional Learning (SEL) in facilitating smoother adjustments and optimal functioning for young students entering a more formal, assessment-driven primary school environment.

To capture a holistic view of the transition process, Zhoc, Tse & King (2025) conducted a qualitative research design involving multiple stakeholders from four government-aided, co-educational primary schools in Hong Kong. The primary data was collected through semi-structured interviews with 38 children from Primary 1 and Primary 2, aged 6-8. To ensure the reliability of the children’s perspectives and provide triangulation, the study also conducted focus group discussions with 15 class teachers and 17 parents from the four schools. The data was analyzed using thematic analysis to identify recurring patterns and core themes regarding the children’s transition experiences.

The analysis revealed three major themes characterizing the transition: positive experiences, academic problems, and social problems. Positively, many children enjoyed making friends, engaging in new learning activities, and receiving support from adults. However, significant challenges were evident. Academic struggles included immense pressure from high self-expectations and parental demands, frustration over test results, and a heavy workload of homework and dictations. Socially, some children reported difficulties in forming friendships, feelings of loneliness, and involvement in hostile interactions or conflicts. The findings paint a picture of a “drilling to learn” culture where academic stress is prevalent, and social skills are often underdeveloped.

Zhoc, Tse & King (2025) conclude that early Social and Emotional Learning (SEL) is indispensable for navigating the complex demands of the Hong Kong education system. They interpret that academic stress from cultural values could equate to the achievement with family pride, leading to the necessity of interventions that foster a “growth mindset” to help children view failure as part of learning. Socially, the study highlights a critical need to teach prosocial behaviors, conflict resolution, and emotion regulation. Ultimately, Zhoc, Tse & King (2025) argue that successful transitions depend not just on academic readiness, but on equipping children with the psychological resources to manage stress and build supportive relationships.

 

Source (Open Access): Zhoc, K. C., Tse, J. K., & King, R. B. (2025). The importance of social and emotional learning in facilitating positive transitions from kindergarten to primary school in Hong Kong. Journal of Early Childhood Research, 1476718X251349938.

https://doi.org/10.1177/1476718X251349938… Read the rest

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Higher Education Social and Motivational Outcomes

Learners’ Preferences for Feedback from AI and Human Instructors

Le and his team examined whether learners’ preferences for feedback from human instructors versus generative artificial intelligence (AI) would change after receiving feedback from different sources and interface types in an academic English writing task. The study recruited 114 university students who were non-native English speakers and randomly assigned them to four groups: no feedback (control), human instructor feedback, ChatGPT 4.0 in a free-conversation interface, and a structured writing analysis tool powered by ChatGPT. Learners’ preferences were measured both before and after the task using rating scales and binary-choice questions, and the four groups were compared in terms of post-task preference and preference change.

The results showed that learners already had a clear preference for human instructors before the task (87.2% chose human), and this preference remained stable after the task (86.0% chose human), reflecting a phenomenon of algorithm aversion in educational settings. However, post-test preference scores differed significantly among the four groups: the human instructor group rated significantly higher than both the free-conversation AI group and the control group. On the binary human/AI choice measure, significant differences were also found — the human instructor and structured AI tool groups both scored higher than the free-conversation AI group. Regarding preference change, the overall mean shift was close to zero, but the differences among groups were significant: the free-conversation AI group showed a slight increase in preference for AI, whereas the human instructor and structured AI tool groups remained more favorable toward humans. In other words, although all three feedback types were effective, the free-conversation interface was the only one that reduced algorithm aversion and increased learners’ acceptance of AI, while the structured, one-time feedback tool further reinforced their preference for human instructors.

Based on these findings, the authors argue that enhancing the interactivity and dialogic nature of AI-based learning tools may influence learners’ preferences more effectively than purely improving their technical performance. Interactive dialogue allows for clarification and correction, which reduces learners’ unrealistic expectations that algorithms must be perfect and mitigates distrust. Overall, the study situates human preference within the context of interface design, providing both empirical insights and cautions for the adoption, product design, and pedagogical integration of AI in education.

 

Source (Open Access): Le, H., Shen, Y., Li, Z., Xia, M., Tang, L., Li, X., … & Fan, Y. (2025). Breaking human dominance: Investigating learners’ preferences for learning feedback from generative AI and human tutors. British Journal of Educational Technology.

https://doi.org/10.1111/bjet.13614… Read the rest

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Educational Administration and Leadership Kindergarten Primary School Education Social and Motivational Outcomes

To whom do results of SEL programs apply?

For decades, education experiments and meta-analyses have focused on how effective an intervention is on average. However, this assumes that the same effect is expected with all students and in all contexts, which educators know is unrealistic. The effectiveness of an intervention can vary depending on factors such as student characteristics, how the program is delivered, or its duration. More recently, there has been a shift toward designing experiments that clearly identify which populations results can be generalized to, helping practitioners better understand what effects to expect.

A recent review by Tiffany Jones and colleagues explored to what extent ethnic diversity of students was represented in studies evaluating SEL programs and which SEL programs benefitted ethnic minorities. They analyzed 97 experimental studies on school-based interventions listed in the CASEL framework, focusing on U.S. students aged 3-11.

The review found that ethnicity was not reported for 18% of students in these studies. Among those with reported data, White students were the most represented group (35%), followed by African American (28%) and Hispanic (23%), with less than 5% belonging to other ethnicities. Of the 69 trials that included a mix of ethnicities, only 13 investigated effects by ethnic group. Results showed that seven SEL programs had proven benefits for Black students, while four benefitted Hispanic students. These findings were based on studies that either focused on a single racial group or showed positive effects for a specific subgroup.

The authors concluded that most of the trials did not adequately consider the role of ethnicity in their evaluations, and that more research is needed to understand how SEL programs impact racial minority groups.

 

Source (Open Access): Jones, T. M., Kim, B.-K. E., Fleming, C. B., Deng, J., Duane, A., Gavin, A. R., & Shapiro, V. B. (2025). To whom do these results apply? Assessing evidence for the generalizability of social and emotional learning programs among specific racial and ethnic groups. Review of Educational Research, 00346543241310184. https://doi.org/10.3102/00346543241310184… Read the rest

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Educational Administration and Leadership Higher Education Social and Motivational Outcomes

Low academic self-efficacy may lead to AI dependency through stress

Generative AI tools are emerging in classrooms and on student laptops across the globe. Policymakers, educators, and other influencers must understand the negative consequences of becoming unhealthily dependent on AI. Using the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, which examines how psychological factors contribute to problematic technology use, researchers studied AI dependency among 300 university students in Seoul. While they hypothesized a link between self-efficacy (students’ belief in their abilities) and AI dependency, they found no direct association. Instead, academic stress emerged as the key driver in AI dependency among students. In other words, the more academically stressed a student is, the more likely they are to become dependent on AI tools.

Notably, 84% of the students surveyed used ChatGPT for academic help, though not all showed signs of dependency. The researchers also sought to discover the negative consequences of AI dependency. Students reported that AI dependency led to increased laziness, the spread of misinformation, a lower level of creativity, and reduced critical and independent thinking. The researchers also examined the role of performance expectations: students who perceived that AI would help their performance were more likely to become dependent.

The emergency of AI tools in schools is a conundrum that generates many different opinions and policy recommendations. Rather than focusing primarily on AI restrictions and teaching AI literacy, schools may need to prioritize stress management and low-stakes practice opportunities to reduce AI dependency.

 

Source (Open Access): Zhang, S., Zhao, X., Zhou, T., & Kim, J. H. (2024). Do you have AI dependency? The roles of academic self-efficacy, academic stress, and performance expectations on problematic AI usage behavior. International Journal of Educational Technology in Higher Education, 21(1), 34. https://doi.org/10.1186/s41239-024-00467-0… Read the rest