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Reflective practice holds critical importance, for example, in higher education and teacher education, yet promoting students' reflective skills has been a persistent challenge. The emergence of revolutionary artificial intelligence technologies, notably in machine learning and large language models, heralds potential breakthroughs in this domain. The current research on analyzing reflective writing hinges on sentence-level classification. Such an approach, however, may fall short of providing a holistic grasp of written reflection. Therefore, this study employs shallow machine learning algorithms and pre-trained language models, namely BERT, RoBERTa, BigBird, and Longformer, with the intention of enhancing the document-level classification accuracy of reflective writings. A dataset of 1,043 reflective writings was collected in a teacher education program at a German university (M = 251.38 words, SD = 143.08 words). Our findings indicated that BigBird and Longformer models significantly outperformed BERT and RoBERTa, achieving classification accuracies of 76.26% and 77.22%, respectively, with less than 60% accuracy observed in shallow machine learning models. The outcomes of this study contribute to refining document-level classification of reflective writings and have implications for augmenting automated feedback mechanisms in teacher education. [ABSTRACT FROM AUTHOR]

Research indicates that students' interest in science decreases during their school years. To counteract this, a quasi-experimental instructional intervention was designed in which we addressed students' leisure interests to foster their (situational) interest in physics. Students in the intervention group got the opportunity to choose topics matching their leisure interests (e.g. sports, music) and were encouraged to reflect on and discuss the application of principles of semiconductor physics to those topics throughout the intervention with a duration over 11 lessons. The intervention group consisted of 60 students from German high-track schools; another 55 students comprised the control group. The results of a MANCOVA analysis indicate that the instructional intervention succeeded in triggering students' catch and hold component of situational interest during all four phases of the lesson series. Furthermore, behavioural disengagement developed more positively for students in the intervention group, while no significant effects were found for their behavioural engagement, their interest in physics-related activities in leisure or their general interest in physics classes in school. The results suggest that addressing students' leisure interests in physics classes can promote their situational interest and reduce their behavioural disengagement in physics. [ABSTRACT FROM AUTHOR]

Over the past few years, there has been a significant increase in the utilization of artificial intelligence (AI)-based educational applications in education. As pre-service teachers' attitudes towards educational technology that utilizes AI have a potential impact on the learning outcomes of their future students, it is essential to know more about pre-service teachers' acceptance of AI. The aims of this study are (1) to discover what factors determine pre-service teachers' intentions to utilize AI-based educational applications and (2) to determine whether gender differences exist within determinants that affect those behavioral intentions. A sample of 452 pre-service teachers (325 female) participated in a survey at one German university. Based on a prominent technology acceptance model, structural equation modeling, measurement invariance, and multigroup analysis were carried out. The results demonstrated that eight out of nine hypotheses were supported; perceived ease of use (β = 0.297***) and perceived usefulness (β = 0.501***) were identified as primary factors predicting pre-service teachers' intention to use AI. Furthermore, the latent mean differences results indicated that two constructs, AI anxiety (z = − 3.217**) and perceived enjoyment (z = 2.556*), were significantly different by gender. In addition, it is noteworthy that the paths from AI anxiety to perceived ease of use (p = 0.018*) and from perceived ease of use to perceived usefulness (p = 0.002**) are moderated by gender. This study confirms the determinants influencing the behavioral intention based on the Technology Acceptance Model 3 of German pre-service teachers to use AI-based applications in education. Furthermore, the results demonstrate how essential it is to address gender-specific aspects in teacher education because there is a high percentage of female pre-service teachers, in general. This study contributes to state of the art in AI-powered education and teacher education. [ABSTRACT FROM AUTHOR]

Due to their connections with positive educational outcomes, the maintenance of good health and scholastic well-being are highly relevant topics. However, to date, no studies have investigated developmental changes in scholastic well-being in the light of achievement emotions and instructional quality—although these predictors offer good approaches for intervention. A sample of N = 667 (age: M = 10.16; SD = 0.46; 81.7% female) students was questioned three times over one year, from the beginning of their fifth grade into sixth grade, using reliable questionnaires. The results of multilevel linear growth curve modelling calculations show that a decrease in scholastic well-being during the fifth grade was induced by changes in the predictors (achievement emotions and instructional quality). Since the highest effects are evident regarding achievement emotions, support programs should focus on facilitating these aspects in secondary school children. [ABSTRACT FROM AUTHOR]

Given that video conferencing serves as a crucial means for remote teaching, the current study investigated higher education students' (non)use of webcams and engagement in synchronous online courses. Three phases were studied: (1) A state of engagement; (2) antecedents that influence it; and (3) consequences of engagement. The cross-sectional online survey encompassed 3,610 students. Results indicated that visual and verbal engagement were only slightly related to each other. Structural equation modelling revealed different direct and indirect influences on either visual or verbal engagement in synchronous online higher education courses. Due to the novelty of the research scope, results of this study provide a foundation for further investigation. [ABSTRACT FROM AUTHOR]

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Copyright of Zeitschrift für Bildungsforschung is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Zeitschrift für Erziehungswissenschaft is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Gruppendynamik und Organisationsberatung is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)