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Computer-supported collaborative learning (CSCL) has been an effective pedagogy in the field of education. However, productive collaborative learning often does not occur spontaneously, and learners often have difficulties with collaborative knowledge building and socially shared regulation. To address this research gap, this study proposes an automatic knowledge graph construction approach based on deep neural network models. In total, 63 groups comprising 189 college students participated in this study and were assigned to three conditions, namely, the automatic activated and unactivated knowledge graph (AAUKG) condition, the automated activated knowledge graph (AAKG) condition, and the traditional online collaborative learning (TOCL) condition. The findings revealed that the AAUKG approach had more significant and positive impacts on collaborative knowledge building, group performance, social interaction, and socially shared regulation than the AAKG and TOCL approaches. This study provides substantial evidence of utilising both automatically activated and unactivated knowledge graphs to improve collaborative learning performance.

Learning engagement has gained increasing attention in the field of education. Previous studies have adopted conventional methods to analyze learning engagement, but these methods cannot provide timely feedback for learners. This study analyzed automated group learning engagement via deep neural network models in a computer-supported collaborative learning (CSCL) context. A quasi-experimental research design was implemented to examine the effects of the automated group learning engagement analysis and feedback approach on collaborative knowledge building, group performance, socially shared regulation, and cognitive load. In total, 120 college students participated in this study; they were assigned to 20 experimental groups and 20 control groups of three students each. The students in the experimental groups adopted the automated group learning engagement analysis and feedback approach, whereas those in the control groups used the traditional online collaborative learning approach. Both quantitative and qualitative data were collected and analyzed in depth. The results indicated significant differences in group learning engagement, group performance, collaborative knowledge building, and socially shared regulation between the experimental and control groups. The proposed approach did not increase the cognitive load for the experimental groups. The implications of the findings can potentially contribute to improving group learning engagement and group performance in CSCL.

Online collaborative learning is implemented extensively in higher education. Nevertheless, it remains challenging to help learners achieve high-level group performance, knowledge elaboration, and socially shared regulation in online collaborative learning. To cope with these challenges, this study proposes and evaluates a novel automated assessment and feedback approach that is based on knowledge graph and artificial intelligence technologies. Following a quasi-experimental design, we assigned a total of 108 college students into two conditions: an experimental group that participated in online collaborative learning and received automated assessment and feedback from the tool, and a control group that participated in the same collaborative learning activities without automated assessment and feedback. Analyses of quantitative and qualitative data indicated that the introduced automated assessment and feedback significantly promoted group performance, knowledge elaboration, and socially shared regulation of collaborative learning. The proposed knowledge graph-based automated assessment and feedback approach shows promise in providing a valuable tool for researchers and practitioners to support online collaborative learning.

Online collaborative learning has been an effective pedagogy in the field of education. However, productive collaborative learning cannot occur spontaneously. Learners often have difficulties in collaborative knowledge building, group performance, coregulated behaviors, learning engagement, and social interaction. To promote productive collaborative learning, this study aims to propose and validate a comprehensive learning analytics approach in an online collaborative learning context. The comprehensive learning analytics can automatically construct knowledge graphs, analyze metacognitive learning engagement and social interaction and provide personalized feedback. A total of 90 college students participated in this study, and they were assigned to the experimental group and control group. The students in the experimental group conducted online collaborative learning with the comprehensive learning analytics approach, while the students in the control group conducted traditional online collaborative learning without any specific approach. The results indicated that the comprehensive learning analytics approach significantly improved collaborative knowledge building, group performance, coregulated behaviors, metacognitive learning engagement, and social interaction compared with traditional online collaborative learning. In this paper, the results of the study together with the implications are discussed.

As a learner-centered approach, technology-facilitated personalized learning has received increasing attention in the field of education. However, it is not clear whether technology-facilitated personalized learning is effective for both learning achievements and learning perceptions. This study aims to close the research gaps and examine the effect sizes of technology-facilitated personalized learning on learning achievements and learning perceptions from 2001 to 2020. In total, 34 empirical studies were synthesized in the present meta-analysis. The findings revealed that technology-facilitated personalized learning had a medium effect size on learning achievements and a small effect size on learning perceptions. Furthermore, the impacts of 10 moderator variables, including the sample levels, sample sizes, learning domains, research design, research settings, intervention duration, learning methods, software and hardware that support personalized learning, and user-oriented personalized parameters, were analyzed in depth. It was found that learning methods and personalized learning software significantly moderated the effect sizes. The novelty and main contribution of this study is threefold. First, this study revealed that technology-facilitated personalized learning was more effective than traditional learning through the comprehensive meta-analysis. Second, this study found that learning methods and personalized learning software could trigger and leverage the effects of technology-facilitated personalized learning. Third, this study proposed a design framework of technology-facilitated personalized learning to facilitate the implementation of personalized learning effectively.

In recent years, the rapid development of artificial intelligence has increased the power of personalized learning. This study aimed to provide personalized intervention for each group participating in computer-supported collaborative learning. The personalized intervention adopted a deep neural network model, Bidirectional Encoder Representations from Transformers (BERT), to automatically classify online discussion transcripts and provide classification results in real time. Personalized feedback and recommendations were automatically generated from the classification results. A quasi-experimental research design was adopted to examine the effects of the proposed personalized intervention approach on collaborative knowledge building, group performance, socially shared metacognitive regulation, and cognitive load. Sixty-six college students participated in this study and were randomly assigned to the experimental and control groups. For online collaborative learning, students in the experimental group adopted the personalized intervention approach, whereas those in the control group used the conventional approach. Both quantitative and qualitative research methods were adopted to analyze data. The results indicated significant differences in the level of collaborative knowledge building and group performance between the experimental and control groups. Furthermore, the experimental group demonstrated more socially shared metacognitive regulation than the control group. There was no significant difference in cognitive load between the experimental and control groups. The results obtained from interviews were consistent with the quantitative data. The main findings together with the implications for practitioners are discussed in depth.

Computer‐supported collaborative learning (CSCL) has been an effective pedagogy in the field of education. However, productive collaborative learning often does not occur spontaneously, and learners often have difficulties with collaborative knowledge building and socially shared regulation. To address this research gap, this study proposes an automatic knowledge graph construction approach based on deep neural network models. In total, 63 groups comprising 189 college students participated in this study and were assigned to three conditions, namely, the automatic activated and unactivated knowledge graph (AAUKG) condition, the automated activated knowledge graph (AAKG) condition, and the traditional online collaborative learning (TOCL) condition. The findings revealed that the AAUKG approach had more significant and positive impacts on collaborative knowledge building, group performance, social interaction, and socially shared regulation than the AAKG and TOCL approaches. This study provides substantial evidence of utilising both automatically activated and unactivated knowledge graphs to improve collaborative learning performance. Practitioner notesWhat is already known about this topic Knowledge graphs have been widely used in many fields for searches, recommendations, analytics, and automation.Researchers have explored how to construct knowledge graphs. However, few studies have automatically constructed knowledge graphs in the CSCL context.What this paper adds An automatic knowledge graph construction approach is proposed and validated in the CSCL context.The findings indicate that the AAUKG approach has more significant and positive impacts on collaborative knowledge building, group performance, social interaction, and socially shared regulation than the AAKG and TOCL approaches.Implications for practice and/or policy The AAUKG approach is an effective method for improving knowledge building and group performance in the CSCL context.Researchers and practitioners are encouraged to consider how knowledge graphs can be applied to facilitate collaborative learning efficiently. [ABSTRACT FROM AUTHOR]

AbstractAs an effective form of pedagogy, online collaborative learning has received increasing application in the field of education. However, learners often feel frustrated with regard to knowledge building, cognitive engagement, and socially shared regulation. To solve these problems, the current study proposed an approach featuring artificial intelligence (AI)-empowered assessments and personalized recommendations. The purpose of this quasi-experimental study was to examine the effect of the proposed approach on collaborative learning performance. In total, 135 college students, who were divided into three conditions, participated in the current study. The results of both quantitative and qualitative analysis indicated that the proposed approach could substantially enhance collaborative knowledge building, cognitive engagement, socially shared regulated behaviors, and group performance. The findings are discussed thoroughly alongside their pedagogical and technological implications. [ABSTRACT FROM AUTHOR]