Research shows that low motivation, negative emotions, and poor self-regulation are risk factors for poor study success and dropout. Against the backdrop of high dropout rates, these findings underline the need for tailored self-management trainings fostering corresponding skills in undergraduates with a respective at-risk profile. However, it has been suggested that mostly those students who are already academically engaged and motivated enroll in such programs. To clarify whether such tailored trainings actually reach students with high training need, that is, students that are at high risk for low achievement and well-being, as well as study dropout, a representatively stratified quota sample of 890 students at German higher education institutions were surveyed about their motivation, emotions, self-regulation, and their subjective needs assessment for these trainings. Using Latent Profile Analysis of motivational, emotional, and self-regulatory characteristics, three student profiles were identified that differed substantially also in their study success. Particularly, a considerably large group of at-risk students was identified (27
self-efficacy beliefs for intergroup friendships (H3–H4). We also explored the role of perceived color-blind SDMs and whether the effect of self-efficacy varied by out-group
There is growing attention to the importance of teachers’ social-emotional capability beliefs. However, existing research has generally been narrow in how it operationalizes these constructs. This study sought to advance knowledge of a social-emotional capability belief, perceived social-emotional competence (PSEC), comprising five dimensions: perceived competence for assertiveness, tolerance, social regulation, emotion regulation, and emotional awareness. Specifically, the five PSEC dimensions were examined in relation to two hypothesized predictors, autonomy-supportive and autonomy-thwarting leadership, and in turn in relation to two hypothesized outcomes, organizational commitment and emotional exhaustion. Using structural equation modeling involving cross-sectional data from 473 Australian teachers, findings showed that autonomy-supportive leadership was positively associated with all five PSEC dimensions, whereas autonomy-thwarting leadership was not associated with any dimension. In turn, perceived competence for tolerance and emotion regulation were associated with greater organizational commitment. Perceived competence for assertiveness was associated with lower emotional exhaustion, whereas perceived competence for social regulation was associated with greater emotional exhaustion. In addition, autonomy-supportive leadership was directly associated with greater organizational commitment, and autonomy-thwarting leadership was directly associated with greater emotional exhaustion. These findings hold implications for better understanding how to support teachers’ social-emotional experiences at work.
Perceived workplace ethnic discrimination may undermine teachers’ psychological and occupational well-being, yet evidence concerning teachers of refugee background in conflict and crisis contexts remains limited. This study examined whether perceived ethnic discrimination was associated with depressive symptoms, anxiety symptoms, burnout, and teacher work engagement, and whether self-efficacy and autonomous motivation accounted for indirect associations. Participants were 143 teachers of refugee background (23
Artificial intelligence (AI) technologies are increasingly used in education, evolving into cognitive learning partners that restructure preservice teachers’ learning and problem-solving processes. Grounded in Social Cognitive Theory (SCT), this study seeks to identify the AI self-efficacy profiles of 376 preservice elementary mathematics teachers studying at different public universities. It further investigates the connection between these profiles and AI-assisted problem-solving intention and examines whether demographic characteristics are associated with profile membership. AI self-efficacy is treated as a multidimensional construct encompassing assistance, anthropomorphic interaction, comfort with AI, and technological skills. Latent Profile Analysis (LPA) results indicated that preservice teachers’ perceptions clustered into three distinct profiles: Low, Moderate, and High. Preservice teachers in the Moderate and High profiles reported significantly higher AI-assisted problem-solving intention compared to those in the Low profile. However, the Moderate and High profiles did not differ significantly from one another. Furthermore, gender, year of study, and AI usage experience were not significantly associated with profile membership. These findings suggest that teacher education programs may benefit from attending to preservice teachers with lower AI self-efficacy when designing reflective and pedagogically structured AI-related learning experiences.
