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Computational thinking is an important competence for learners in the twenty-first century. As an effective approach for cultivating competence in computational thinking, programming education has been extended from college to elementary school teaching. However, it is challenging to engage beginners in programming in elementary school education. In the current study, a contrasting cases approach for learning programming was designed to facilitate learning outcomes of novice programmers in elementary schools. To evaluate the effectiveness of the proposed approach, a quasi-experiment was conducted during a 3-week programming course with 72 elementary school students. The results revealed that the contrasting cases approach for learning programming was effective for improving learning outcomes in terms of learning performance, learning engagement, and cognitive load. Furthermore, suggestions for promoting the learning of programming by novice learners at the elementary school level using the contrasting cases approach were proposed. In addition, prospects for future research and practice were discussed.

Background: Collaborative learning has become a crucial approach to promoting online in‐service teacher training. Appropriate peer recommendation for group composition is the basis to ensure productive learning outcomes of collaborative learning. However, there is a lack of understanding of the impact of peer recommendation on in‐service teachers' online training. Objectives: Therefore, based on the collaborative filtering approach, a peer recommendation mechanism for in‐service teachers was constructed in this study. We investigated the effects of the constructed mechanism on in‐service teachers' online learning performance, interactive knowledge construction behavioural patterns and social networks. Methods: In this study, 82 in‐service teachers were recruited to participate in the study. Participants under the experimental condition (n = 41) were invited to apply collaborative filtering‐based peer recommendation mechanism for grouping, while participants under the control condition (n = 41) were invited to use random grouping method. Participants' interaction data were collected from online collaborative discussion activities in a 5‐week asynchronous online course. The pre‐post knowledge test, content analysis, lag sequential analysis and social network analysis were used to analyse the differences between groups. Results and Conclusions: The following findings were revealed: (1) the experimental group performed better than the control group in terms of online learning performance; (2) interactive knowledge construction behavioural patterns generated in collaborative activities showed that the experimental group achieved deeper level of knowledge construction in collaboration than the control group; (3) social networks generated in collaborative activities showed that the experimental group had tighter interaction relationships than the control group. Therefore, the peer recommendation mechanism could be useful to improve peer recommendation for group composition and had positive effects on in‐service teachers' online training. Implications: This study can shed light on the construction of peer recommendation mechanism in recommending appropriate peers for in‐service teachers and different learners in online collaborative learning. Lay Description: What is currently known about this topic: Appropriate learning peers are conducive to promoting effective online collaborative learning. What this paper adds: This study constructed a collaborative filtering‐based peer recommendation mechanism for recommending appropriate learning peers to improve online collaborative learning of in‐service teachers. The implications of study findings for practitioners: The study findings provide insights into the construction of mechanisms for recommending learning peers in online teacher training and computer‐supported collaborative learning. [ABSTRACT FROM AUTHOR]