Computational thinking (CT), the ability to devise computational solutions for real‐life problems, has received growing attention from both educators and researchers. To better improve university students' CT competence, collaborative programming is regarded as an effective learning approach. However, how novice programmers develop CT competence through collaborative problem solving remains unclear. This study adopted an innovative approach, quantitative ethnography, to analyze the collaborative programming activities of a high‐performing and a low‐performing team. Both the discourse analysis and epistemic network models revealed that across concepts, practices, and identity, the high‐performing team exhibited CT that was systematic, whereas the CT of the low‐performing team was characterized by tinkering or guess‐and‐check approaches. However, the low‐performing group's CT development trajectory ultimately converged towards the high‐performing group's. This study thus improves understanding of how novices learn CT, and it illustrates a useful method for modeling CT based in authentic problem‐solving contexts.

Practical experience with clinical cases has played an important role in supporting the learning of clinical reasoning. However, learning through practical experience involves complex processes difficult to be captured by students. This study aimed to examine the effects of a computer-based cognitive-mapping approach that helps students to externalize the reasoning process and the knowledge underlying the reasoning process when they work with clinical cases. A comparison between the cognitive-mapping approach and the verbal-text approach was made by analyzing their effects on learning outcomes. Fifty-two third-year or higher students from two medical schools participated in the study. Students in the experimental group used the computer-base cognitive-mapping approach, while the control group used the verbal-text approach, to make sense of their thinking and actions when they worked with four simulated cases over 4 weeks. For each case, students in both groups reported their reasoning process (involving data capture, hypotheses formulation, and reasoning with justifications) and the underlying knowledge (involving identified concepts and the relationships between the concepts) using the given approach. The learning products (cognitive maps or verbal text) revealed that students in the cognitive-mapping group outperformed those in the verbal-text group in the reasoning process, but not in making sense of the knowledge underlying the reasoning process. No significant differences were found in a knowledge posttest between the two groups. The computer-based cognitive-mapping approach has shown a promising advantage over the verbal-text approach in improving students’ reasoning performance. Further studies are needed to examine the effects of the cognitive-mapping approach in improving the construction of subject-matter knowledge on the basis of practical experience.

Recent years have seen an increase number of studies on spherical video-based virtual reality (SVVR) in education.However, the impact of SVVR on learning performance remains unclear and cannot be simply generalized from traditional VR-based education. The purpose of this study was to examine the overall effects of SVVR on cognitive and non-cognitive learning outcomes and to explore a set of moderating variables in student, technology, pedagogy, and methodology aspects. The meta-analysis examined 33 studies from 31 articles that compare SVVR to other instructional conditions. All analyzed articles were published between 2018 and 2021, and were sourced from ERIC, the Web of Science, Scopus, and Google scholar. This resulted in 27 independent effect sizes on cognitive outcomes and 23 independent effect sizes on non-cognitive outcomes. Overall, we found SVVR had a low-medium and a medium effect on cognitive and non-cognitive learning outcomes, respectively. For cognitive learning outcomes, the effect sizes were significantly moderated by scaffolding type and learning duration. For non-cognitive learning outcomes, the effect sizes were significantly moderated by interaction functionality, learning duration, learner’s stage, research setting, source of SVVR, and scaffolding type. A detailed discussion of the findings and implications for teaching and learning with SVVR were provided.