For the planning and implementation of lessons with digital technologies, a subject-specific technology-related professional competence of teachers is of central importance. However, the competency frameworks developed so far remain in a general perspective and do not explicitly address subject-specific issues. Furthermore, digital competencies are predominantly measured with subject-unspecific self-assessment instruments, as subject-specific operationalizations for this area are not yet available in a differentiated form. In this article, the framework for Digital Competencies for Teaching in Science Education (DiKoLAN), a subject-specific framework for pre-service science teachers, is introduced, on the one hand, and, on the other hand, first results of a self-assessment tool based on the framework are described. DiKoLAN defines competency areas highly specific to science, as well as more general competency areas that include aspects common to all subjects. Each competency area is described by competency expectations, which, in turn, are structured with reference to the four technology-related dimensions of the TPACK framework (i.e., Technological and Pedagogical Content Knowledge) and three levels of performance (Name, Describe, Use/Apply). Derived from DiKoLAN, a corresponding self-assessment instrument (DiKoLAN-Grid) was developed and empirically tested for the two competency areas, (n = 118) and Information Search and Evaluation (n = 90), in biology student teachers. By means of path models, tendencies regarding structural correlations of the four components Special Tools (TK), Content-specific Context (TCK), Methods and Digitality (TPK), and Teaching (TPACK) are presented for both competency areas and discussed, as well as in comparison to previously conducted, subject-unspecific surveys.

This work explores the integration of artificial intelligence (AI) custom chatbots in educational settings, with a particular focus on their applicability in the context of mathematics and physics. In view of the increasing deployment of AI tools such as ChatGPT in educational contexts, the present study explores their potential in generating topic-related learning material. The study assesses the impact of learning with AI-generated explanations as Supplemental Material on the learning experiences and performance of sixth-grade students, with a particular focus on proportional relationships in mathematical and physical contexts. The randomized controlled study with N = 2 1 4 students compared supplementary learning material in the form of traditional textbook material with explanations previously generated by an AI custom chatbot. The results demonstrated that while the AI-generated materials had an indefinite impact on learning outcomes, they significantly enhanced positive-activating emotions, situational interest, and self-efficacy while reducing intrinsic and extrinsic cognitive load. These findings underscore the potential of AI to transform educational practices by fostering a superior learning experience. However, further research is required to clarify its impact on learning performance and long-term learning outcomes. The study highlights the importance of careful integration and customization of AI tools to maximize their benefits in physics education.

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This contribution presents the results of a replication study on the learning effect of tablet-supported video analysis compared to traditional teaching sequences using non-digital experimental materials in the subject areas of uniform and accelerated motion in high school physics lessons. In addition to the replication of the preliminary study results recently published in this journal (Becker et al 2018, 2019), the investigation of the effect on the cognitive load as well as the emotional state of the students is another focal point. Compared to the preliminary study, the sample size was significantly increased from $$N=109$$N=109 to $$N=294$$N=294 . The individual effects of the preliminary study could be replicated in this way. For both topics, a significant reduction of extraneous cognitive load and a positive effect on intervention-induced emotions could be demonstrated. Moreover, the theoretically founded causal relationship between emotion, cognitive load, and learning achievement could be empirically verified by means of structural equation modeling.

The test of understanding graphs in kinematics (TUG-K) has widely been used to assess students’ understanding of this subject. The TUG-K poses different objectives to the test takers such as (1) the selection of a graph from a textual description, (2) the selection of corresponding graphs, and (3) the selection of a textual description from a graph. Whether test takers follow these task requirements is usually inferred from evaluating the test scores as correct or incorrect, yet the process of how students actually interact with the different tasks remains unknown. Recent studies have shown that eye tracking can provide rich insight into student’s interaction with multiple-choice tasks. In the current work, we analyzed the eye movement patterns of $N=115$ high school students while solving the TUG-K. Each question was divided into a question area (Q) and an option area (O), then gaze transitions between Q and O and between different options were calculated. A cluster analysis using the transition metrics revealed three item groups, containing the aforementioned objectives of the items. The clusters remain stable for different subsamples of our dataset, for instance, considering only the correct or only the incorrect responses, or considering high- or low-confidence responses. We conclude that eye movements can reflect task demands on a procedural level well beyond the classical methods of evaluating test scores, eventually making eye tracking an additional method for item analysis that can be utilized to confirm or explore test and item structures.