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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]

This research constructs a disruptive technology identification framework based on attributes and functions and selects intelligent logistics robot technology for empirical analysis in the artificial intelligence & Internet of Things (AIoT) industry. We take the three attributes of technological novelty, breakthrough, and influence as measurement indicators to identify disruptive technologies and perform functional analysis of the technology to find potential markets for disruptive technologies. The research demonstrates that two‐dimensional code technology, sensing technology, intelligent control technology, artificial intelligence technology, and wireless communication technology are disruptive technologies in intelligent logistics robots. The future development directions of intelligent logistics robots include efficient, intelligent control operations, good human‐computer interaction, accurate safety obstacle avoidance, and accurate and efficient position detection. This research has put forward the future development direction of disruptive technology in AIoT and provided a reference for the R&D and manufacture of intelligent logistics robots. [ABSTRACT FROM AUTHOR]