Explore the vast range of titles available.

Burnout is understood to have many adverse consequences for students. However, several equivocal findings in the literature mean that it is currently unclear to what extent burnout affects academic achievement. To address this lack of clarity, the aim of the present study was to provide a first meta-analysis of the relationship between burnout and academic achievement. A literature search returned 29 studies (N = 109,396) and 89 effect sizes. Robust variance meta-analyses indicated that total burnout had a significant negative relationship with academic achievement (r c ⁺ = – .24). A similar pattern of relationships was found for each of the three symptoms of burnout (exhaustion [r c ⁺ = – .15], cynicism [r c ⁺ = – .24], and reduced efficacy [r c ⁺ = – .39]). There was some evidence that the instrument used to measure burnout moderated the relationship between reduced efficacy and achievement. Taken together, the findings suggest that burnout leads to worse academic achievement in school, college, and university.

Evaluating teacher AI competence levels and building effective, safe, and healthy learning environment are crucial steps in transitioning to AI-based education. Current established digital competence frameworks may indirectly address AI competence but often overlook the impact of AI on society, ethics, and assessment. Research on teacher AI competence is at its first stage, primarily focusing on theoretical and professional discussions, along with qualitative investigations. This study aims to propose and confirm the reliability and validity of a scale measuring teacher AI competence self-efficacy (TAICS) in K-12 education. The scale was developed using a Delphi method, and includes six dimensions: AI knowledge, AI pedagogy, AI assessments, AI ethics, human-centered education, and professional engagement. Each dimension contains four items. The scale was evaluated on a sample of 434 K-12 teachers through confirmatory factor analysis and model comparisons. The analyses showed that the scale is consistent across male and female teachers, as well as scientific and non-science teachers. The completed TAICS scale consists of 24 items and encompasses six dimensions of AI competence. It can be used to examine interventions and correlational research, as well as to inform the creation of new strategies and policies for AI in relation to teacher AI competence development.

Recent research on the professional competencies of mathematics teachers, which has been carried out during the last decade, is characterized by different theoretical approaches on the conceptualization and evaluation of teachers' professional competencies, namely cognitive versus situated approaches. Building on the international IEA Teacher Education and Development Study in Mathematics (TEDS-M) and its follow-up study, TEDS-FU, the paper compares cognitive and situated approaches on professional competencies of teachers. In TEDS-FU, the cognitive oriented framework of TEDS-M has been enriched by a situated orientation including the novice-expert framework and the noticing concept as theoretical approaches on the analyses of classroom situations. Correspondingly, the evaluation instruments were extended by using video vignettes for assessing teachers' perception, interpretation, and decision-making competencies in addition to cognitive oriented knowledge tests. The paper discusses the different kinds of theoretical frameworks and the consequences for the evaluation methods, the strengths, and weaknesses of both approaches. Furthermore, connecting the results of TEDS-FU with TEDS-M allows comprehensive insight into the structure and development of the professional competencies of mathematics teachers, the complex interplay between the different facets of teachers' competencies, and the high relevance of teaching practice for the development of these competencies. The analyses show on the one hand that both approaches—cognitive and situated—are needed for a comprehensive description of teachers' professional competencies. On the other hand, it is shown that both approaches can be integrated in a productive way. The paper closes with prospects on further studies coming even closer to the real classroom situation.

I exploit the random assignment of class rosters in the MET Project to estimate teacher effects on students’ performance on complex open-ended tasks in math and reading, as well as their growth mindset, grit, and effort in class. I find large teacher effects across this expanded set of outcomes, but weak relationships between these effects and performance measures used in current teacher evaluation systems including value-added to state standardized tests. These findings suggest teacher effectiveness is multidimensional, and high-stakes evaluation decisions are only weakly informed by the degree to which teachers are developing students’ complex cognitive skills and social-emotional competencies.

The classroom represents a complex socio-cultural environment where emotions emerge as a result of instruction, learning, and interpersonal transactions. Teachers’ ability to recognize, regulate, and respond to emotions in the classroom has powerful consequences for students’ behavior, learning, and the teacher’s own well-being. In order to be effective in instruction and classroom management and to prevent emotional burnout, teachers have to be emotionally competent. Furthermore, fostering teachers’ emotional competence helps with teacher retention. Several studies documented the positive effects of emotional training of aspiring and practicing teachers. However, there is a need for a better understanding of specific emotional skills pertinent to the teaching profession. We propose eight competencies essential for teachers to be effective in using emotions for instructional and relational purposes and managing their own and students’ emotions. These competencies include (1) understanding teachers’ own emotions; (2) effective emotion regulation; (3) effective emotional communication; (4) maintaining teachers’ emotional well-being; (5) understanding and responding to students’ emotions; (6) utilizing emotions to promote learning; (7) utilizing emotions to foster positive relationships and emotional climate in the classroom; and (8) promoting students’ social-emotional learning. We further identified discrete emotional skills within each competence and their implication for instruction, classroom management, social-emotional learning in the classroom, and teachers’ well-being. The proposed model of teacher emotional competence can be used as a springboard to develop emotional training programs and guide assessment practices aimed at the evaluation of teachers’ emotional skills.

In recent years, there has been a growing emphasis on integrating Artificial Intelligence (AI) applications in educational settings. As a result, it is essential to assess teachers’ competencies in Technological, Pedagogical, and Content Knowledge (TPACK) as it pertains to AI and examine the factors that influence these competencies. This study aims to analyze the impact of digital proficiency on teachers’ AI-TPACK competencies. The study utilized a correlational survey model and involved 401 teachers from various provinces and departments in Turkey. The data collection tools included a personal information form, an AI-TPACK scale, and a digital proficiency scale. The collected data were analyzed using structural equation modeling. The research findings revealed that teachers’ AI-TPACK competencies were below average, whereas their levels of digital proficiency were above average. Furthermore, a significant relationship between teachers’ AI-TPACK and digital proficiency levels was identified, with digital proficiency as a significant predictor of AI-TPACK competencies. Based on the research findings, recommendations for future studies are provided.

It is widely assumed that teachers play a key role in providing high-quality learning opportunities to students and fostering students’learning. Yet it is still unclear how specific teacher knowledge facets as part of their professional competence contribute to classroom processes and learning outcomes. Focusing on mathematics education at the secondary level, this study investigates the links between teachers’pedagogical competence (i.e., cognitive pedagogical facets of their professional competence), instructional quality, and students’mathematics achievement. The sample comprises mathematics teacher and student data from 59 classrooms in Germany. Student mathematics achievement was measured across two time points (grade 7 and 8). Teachers’ pedagogical competence was tested using two tests measuring their general pedagogical knowledge (GPK) and situation-specific classroom management expertise (CME). Instructional quality was measured using observational rating data from in vivo rating in mathematics classrooms. Research questions on the relation of teachers’ competence and students’ mathematics achievement were answered using multilevel models. Results from multilevel regression analyses indicate that both GPK and CME predict instructional quality. Direct statistical effects on students’mathematical progress were identified, whereas no indirect statistical effects via instructional quality could be identified. Although teachers’ measured pedagogical competence is not subject-specific, it serves as a significant predictor for cognitive activation as an indispensable part of quality-oriented mathematical teaching and learning processes in the lower secondary mathematics classroom, and it contributes to students’ mathematical progress.

The article deals with the pedagogical content knowledge of mathematical modelling as part of the professional competence of pre-service teachers. With the help of a test developed for this purpose from a conceptual model, we examine whether this pedagogical content knowledge can be promoted in its different facets—especially knowledge about modelling tasks and about interventions—by suitable university seminars. For this purpose, the test was administered to three groups in a seminar for the teaching of mathematical modelling: (1) to those respondents who created their own modelling tasks for use with students, (2) to those trained to intervene in mathematical modelling processes, and (3) participating students who are not required to address mathematical modelling. The findings of the study—based on variance analysis—indicate that certain facets (knowledge of modelling tasks, modelling processes, and interventions) have increased significantly in both experimental groups but to varying degrees. By contrast, pre-service teachers in the control group demonstrated no significant change to their level of pedagogical content knowledge.

Background: Competency-based nursing education necessitates effective instructional methods and assessment tools for evaluating students' knowledge, skills, and attitudes. Clinical simulation has emerged as a valuable approach, but creating well-crafted simulations traditionally requires substantial time and effort. The advent of artificial intelligence (AI), exemplified by ChatGPT (OpenAI), offers promising advancements in streamlining scenario creation. Method: This article explores the application of ChatGPT-3, version GPT-3, created by OpenAI in generating clinical simulation scenarios for nursing education. The focus is on the convenience, speed, and creativity provided by ChatGPT, enabling nurse educators to save time while developing intricate and thought-provoking scenarios. Results: ChatGPT generates intricate scenarios that stimulate critical thinking, significantly reducing the time required for nurse educators to create simulations. This AI tool's ability to produce clinical simulations quickly demonstrates its potential to enhance educational experiences in nursing. Conclusion: ChatGPT's convenience, speed, and innovative capabilities make it invaluable for constructing dynamic clinical simulations, opening new avenues for innovative instruction in nursing education. This article highlights the transformative role of AI in empowering educators and enhancing educational experiences, showcasing ChatGPT's potential to revolutionize nursing education despite ongoing discussions about its potential negative impacts. [J Nurs Educ. 2024;63(X):XXX–XXX.]

In both the United States and Europe, concerns have been raised about whether preservice and in-service training succeeds in equipping teachers with the professional knowledge they need to deliver consistently high-quality instruction. This article investigates the significance of teachers' content knowledge and pedagogical content knowledge for high-quality instruction and student progress in secondary-level mathematics. It reports findings from a 1-year study conducted in Germany with a representative sample of Grade 10 classes and their mathematics teachers. Teachers' pedagogical content knowledge was theoretically and empirically distinguishable from their content knowledge. Multilevel structural equation models revealed a substantial positive effect of pedagogical content knowledge on students' learning gains that was mediated by the provision of cognitive activation and individual learning support.