Search

Online collaborative learning has been broadly applied in higher education. However, learners face many challenges in collaborating with one another and coregulating their learning, leading to low group performance. To address the gaps, this study proposed an artificial intelligence (AI)-enabled feedback and feedforward approach that not only provide feedback but also offer recommendations for future actions to support online collaborative learning. In total, 153 college students participated in this study, and they were divided into three conditions. Fifty-one students conducted online collaborative learning with the AI-enabled feedback and feedforward approach, another 51 students carried out online collaborative learning with the AI-enabled feedback approach, and the remaining 51 students participated in traditional online collaborative learning without either type of support. The results indicated that the AI-enabled feedback and feedforward approach could significantly boost the level of collaborative knowledge building, coregulated behaviours, and group performance. Research and practical implications of the findings are discussed in depth. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]