Using the theory of planned behavior, we investigated whether attitudes, subjective norms, and self-efficacy facilitate pre-service teachers’ engagement in evidence-informed reasoning about classroom problems. N = 157 pre-service teachers were asked about these motivationally relevant antecedents to engaging in evidence-informed reasoning about classroom-related challenges and analyzed case scenarios of problematic teaching situations. Results revealed that self-reported evidence-informed reasoning was directly predicted by intention to engage in evidence-informed reasoning, self-efficacy, and attitude toward evidence-informed reasoning. However, the objectively coded quality of teachers’ evidence-informed reasoning was seemingly negatively predicted by perceived costs and self-efficacy. Thus, the theory of planned behavior partly explained self-reported evidence-informed reasoning, but not objectively observed reasoning. Pre-service teachers might not be skilled enough to assess their own competency accurately and might be unaware of external conditions facilitating or hindering evidence-informed reasoning. Thus, interventions aiming to foster pre-service teachers’ motivation to engage in evidence-informed reasoning might not be effective until such teachers gain the necessary skills.

Very often, university students deliberately form self-organized study groups, e.g. to study collaboratively for an upcoming exam. Yet, very little is known about what regulation problems such self-organized study groups encounter during their learning process and how they try to cope with these problems. Therefore, this study investigates how completely self-organized groups (i.e., non-guided groups outside the classroom that form without external impulse) regulate their collaborative learning process when faced with different kinds of regulation problems. More specifically, we tested the hypotheses that members of self-organized study groups are more satisfied with their group learning experience (a) the more homogeneous their problem perceptions are within their group, (b) the more they apply immediate (rather than non-immediate) strategies to remedy their regulation problems, and (c) the more frequently they apply regulation strategies. In a longitudinal study, N = 122 students, voluntarily studying for their exams in N = 52 groups, were asked to indicate the types of problems they experienced, the types of strategies they used to tackle those problems, and their satisfaction with their group learning experience after each of their self-organized study meetings. Hierarchical linear modeling confirmed all hypotheses. Qualitative analysis of two selected groups' selfreported situational data provided additional insights about the mechanisms that may have contributed to the results. Our study provides important directions for future research, including the recommendation to identify the processes by which groups (a) can reach homogeneity of problem perceptions and (b) coordinate the choice of appropriate strategies within the group.

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Advancements in artificial intelligence are rapidly increasing. The new‐generation large language models, such as ChatGPT and GPT‐4, bear the potential to transform educational approaches, such as peer‐feedback. To investigate peer‐feedback at the intersection of natural language processing (NLP) and educational research, this paper suggests a cross‐disciplinary framework that aims to facilitate the development of NLP‐based adaptive measures for supporting peer‐feedback processes in digital learning environments. To conceptualize this process, we introduce a peer‐feedback process model, which describes learners' activities and textual products. Further, we introduce a terminological and procedural scheme that facilitates systematically deriving measures to foster the peer‐feedback process and how NLP may enhance the adaptivity of such learning support. Building on prior research on education and NLP, we apply this scheme to all learner activities of the peer‐feedback process model to exemplify a range of NLP‐based adaptive support measures. We also discuss the current challenges and suggest directions for future cross‐disciplinary research on the effectiveness and other dimensions of NLP‐based adaptive support for peer‐feedback. Building on our suggested framework, future research and collaborations at the intersection of education and NLP can innovate peer‐feedback in digital learning environments. Practitioner notesWhat is already known about this topic There is considerable research in educational science on peer‐feedback processes. Natural language processing facilitates the analysis of students' textual data. There is a lack of systematic orientation regarding which NLP techniques can be applied to which data to effectively support the peer‐feedback process. What this paper adds A comprehensive overview model that describes the relevant activities and products in the peer‐feedback process. A terminological and procedural scheme for designing NLP‐based adaptive support measures. An application of this scheme to the peer‐feedback process results in exemplifying the use cases of how NLP may be employed to support each learner activity during peer‐feedback. Implications for practice and/or policy To boost the effectiveness of their peer‐feedback scenarios, instructors and instructional designers should identify relevant leverage points, corresponding support measures, adaptation targets and automation goals based on theory and empirical findings. Management and IT departments of higher education institutions should strive to provide digital tools based on modern NLP models and integrate them into the respective learning management systems; those tools should help in translating the automation goals requested by their instructors into prediction targets, take relevant data as input and allow for evaluating the predictions.

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