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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.

The proliferation of network technology has led to the focus on large-scale group decision-making (LSGDM).This study establishes a consensus model by constructing dynamic trust networks to address LSGDM problems in an incomplete intuitionistic fuzzy environment. More specifically, an approach to measuring the influence of decision-makers (DMs) is presented by combining opinion similarity and trust relationships among DMs. Subsequently, an estimation method for missing values in incomplete intuitionistic fuzzy preference relations (IFPRs) is given by considering the reference risk. With these discussions, a dynamic update method of the trust network is proposed to determine the real-time weights of DMs and subgroups. In addition, a two-stage feedback adjustment mechanism for individuals and subgroups is designed. In the end, a numerical example and comparative analysis are provided to illustrate the feasibility and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

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]

Under the mainstream development trend of energy conservation, environmental protection and sustainability, green supplier selection has become a common concern of many enterprises and scholars. Meanwhile, more and more attention has been paid to the problem of large-scale group decision making (LSGDM) which requires the participation of multiple decision makers (DMs). As the factors to be considered in selecting the optimal green supplier become increasingly complex, DMs with different professional backgrounds may give different forms of assessment. Therefore, it is of great significance to study the application of heterogeneous LSGDM in green supplier selection (GSS). In this article, a new heterogeneous LSGDM method based on subgroup leaders (SLs) is proposed. First, a novel determination method of SLs is presented based on the interaction between DMs. Then, according to the heterogeneous preference information of SLs, the solution method of the collective priority vector is given, and then an approach to determine the overall consensus level is presented by considering the consensus degree within and between subgroups. Subsequently, a consensus building method is proposed to manage the non-cooperative behaviour of DMs and reduce the divergences among DMs. Finally, an example of GSS is given to demonstrate the applicability and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]