Frank Fischer

Collaborative learning is widely recognized in educational research as an effective pedagogical approach for facilitating the development of a broad range of skills. However, this approach also faces several challenges in ensuring optimal learning outcomes. Adaptive instructional support has emerged as a promising solution for addressing these problems by enhancing both individual- and group-level learning. This meta-analysis of 83 empirical studies builds on an integrated theoretical framework to examine the effects of adaptive instructional support on different learning dimensions as well as the moderating role of design features, learner characteristics, and contextual conditions in collaborative learning. Overall, adaptive instructional support exhibited a positive medium effect ( g = .73, 95% CI [.56, .90], p < .001) on collaborative learning with varying effects across learning dimensions. Design features proved the most influential moderator: Adaptations based on cognitive variables, implemented through cognitive models or data-science approaches, targeted at collaborative activities, and applied over short-term durations yielded the strongest effects; however, the role of teacher and learner involvement in decision-making around adaptivity requires more careful consideration. Learners’ prior knowledge, domains, and learning context also significantly moderated the effectiveness of adaptive support.

The concept of professional learning community (PLC) has received considerable academic interest since the late 1980s (S. Hord, 1997; Louis et al., 1996; Rosenholtz, 1989). As a core concept, PLCs ...

Students’ difficulties with mathematical argumentation, conjecturing, and proving, as well as with collaborating effectively, have long been documented in the literature. While there is some research on what constitutes successful proving processes, systematic investigations of proving process quality are rare. We investigate how individual-mathematical and social-discursive process characteristics during a collaborative conjecturing-and-proving activity relate to students’ performance in conjecturing and proving, as well as their proof-related resources, such as prior proof skills. Based on a multidimensional high-inferent coding of collaborative conjecturing-and-proving processes of N =98 prospective mathematics students who worked in dyads, we test a range of hypotheses on the overall role of process characteristics for students’ performance. Moreover, we investigate which single process characteristics best predict conjecturing-and-proving performance. We find that individual-mathematical process quality mediates the effect of students’ resources on their performance, whereas the investigated social-discursive characteristics were largely unrelated to students’ proof performance. Mathematical argument structure was particularly predictive for performance. Results further indicate that process quality is predicted by the partners’ resources, but not by the partners’ contributions earlier in the process. However, students within a dyad converge in terms of process quality. We derive implications for the conceptualization, measurement, and investigation of process characteristics.

Background: Self‐explaining is an engagement activity that supports learners' active use of instructional scaffolds such as worked examples. Self‐explanation quality is assumed to mediate the worked example effect on learning outcomes. However, prior investigations have relied on analytical approaches limited in scope and quality or have yielded inconclusive results Aims: We replicate a previous investigation that examines whether self‐explanation quality mediates the worked example effect in a worked examples – problem‐solving paradigm, while simultaneously considering key cognitive aptitudes (prior knowledge, working memory capacity (WMC), fluid intelligence, shifting ability) as moderating variables Method: We analysed self‐explanations from 115 university students solving six ill‐defined statistics problems with or without worked examples. Self‐explanation quality was defined as using specific case information from problem descriptions to justify statistical claims and coded by trained raters Results: Results showed a moderated mediation of the worked example effect via self‐explanation quality for prior knowledge and WMC, with stronger indirect effects at lower aptitude levels. The indirect effect was not moderated by fluid intelligence. No self‐explanation mediation emerged for shifting ability; shifting ability was confirmed as a moderator of the worked example effect, with stronger scaffold benefits at lower ability levels Conclusions: Whereas the original study found no evidence for self‐explanation quality as a mediator, the replication, drawing on stronger methodology, provides evidence for such mediation. However, this mechanism is not universal. Rather, worked examples exert their benefits either directly or via self‐explanation, contingent on learners' cognitive resources.

Scientific reasoning and argumentation (SRA) skills are crucial in higher education, yet comparing studies on these skills remains challenging due to the scarcity of well-developed SRA-tests with robust psychometric properties. In this paper, the case-based ASSESSRA approach is proposed to evaluate university students’ SRA-skills, focusing specifically on the skills evidence evaluation and drawing conclusions . A prototype constructed using this approach in an educational context demonstrated reliability within an expert panel ( n = 9; ICC = .81). In a subsequent study, the validity of the ASSESSRA approach was examined with 207 students, a partial-credit-model exhibited an acceptable fit, demonstrating no significant outfit and excellent distribution of ability parameters and Thurstonian thresholds. The ASSESSRA-prototype, coupled with provided guidelines, offers a versatile framework for developing comparable SRA-tests across diverse domains. This approach not only addresses the current gap in SRA assessment instruments but also holds promise for enhancing the understanding and promotion of SRA-skills in higher education.

Technology has shown to be beneficial for initiating cognitive engagement. In the present study, cognitive engagement was conceptualized by the ICAP framework, proposing four levels of cognitive engagement (interactive, constructive, active, passive), which can be determined from observable student activities. To initiate cognitive engagement, teachers require diagnostic skills. With this study, we aimed to foster those skills. We designed and validated a simulation with N = 213 pre-service teachers to investigate the validity of the simulation. Moreover, we evaluated the difficulty of diagnosing the levels of cognitive engagement within planning and implementing lessons. We used linear regressions for the validation and confusion matrices for insights into the diagnostic process. The study results show a varying difficulty of diagnosing levels of cognitive engagement due to (a) challenges in inferring the involved cognitive processes and (b) different phases of teaching. Levels of cognitive engagement that require inferential processes to identify them are more difficult to diagnose. This highlights the importance of adding scaffolds to our simulation to help pre-service teachers understand the processes of generating knowledge and co-generating knowledge. More importantly, the study reveals shortcomings of the ICAP framework and presents first suggestions for its further development.

Teachers routinely observe and interpret student behavior to make judgements about whether and how to support their students’ learning. Simulated cases can help pre-service teachers to gain this skill of diagnostic reasoning. With 118 pre-service teachers, we tested whether participants rate simulated cases presented in a serial-cue case format as more authentic and become more involved with the materials compared to cases presented in a whole case format. We further investigated whether participants with varying prior conceptual knowledge (what are symptoms of ADHD and dyslexia) gain more strategic knowledge (how to detect ADHD and dyslexia) with a serial-cue versus whole case format. We found that the case format did not impact authenticity ratings but that learners reported higher involvement in the serial-cue case format condition. Bayes factors provide moderate evidence for the absence of a case format effect on strategic knowledge and strong evidence for the absence of an interaction of case format and prior knowledge. We recommend using serial-cue case formats in simulations as they are a more authentic representation of the diagnostic reasoning process and cognitively involve learners. We call for replications to gather more evidence for the impact of case format on knowledge acquisition. We suggest a further inquiry into the relationship of case format, involvement, and authenticity but think that a productive way forward for designing authentic simulations is attention to aspects that make serial-cue cases effective for diverse learners. For example, adaptive feedback or targeted practice of specific parts of diagnostic reasoning such as weighing evidence.