Using multimodal analytics to systemically investigate online collaborative problem-solving

Tang, Hengtao; Dai, Miao; Yang, Shuoqiu; Du, Xu; Hung, Jui-Long; Li, Hao

doi:10.1080/01587919.2022.2064824

Cited by 13 publications

(2 citation statements)

References 68 publications

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“…Most online collaborative problem-solving is qualitative research, with an empirical analysis of the online collaborative problem-solving process. Case analysis [74], group communication analysis [3], cognitive network analysis [75], social network analysis, and lagged sequence analysis [76] were used to analyze and observe the interaction process from various perspectives [77]. The current effective indicators found include brain-brain synchronization [78], behavioral transition mode [6], and teaching scaffold [79].…”

Section: Discussionmentioning

confidence: 99%

Research on Online Collaborative Problem-Solving in the Last 10 Years: Current Status, Hotspots, and Outlook—A Knowledge Graph Analysis Based on CiteSpace

et al. 2023

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Collaborative problem-solving is a crucial skill to be developed in education. With the development of information technology, collaborative problem-solving in online environments has received increasing attention in recent years. There has been some foundation of research on computer-supported collaborative learning (CSCL) and collaborative problem-solving (CPS). They both have a focus, but there is a trend to combine them. Further understanding of the current state of research on online collaborative problem-solving is much needed. This study reviews 528 relevant papers from the last decade. The researchers summarized the current state of research and hotspots in online collaborative problem-solving based on the visual analysis of CiteSpace. It was found that the number of papers on online collaborative problem-solving is on the rise, with CPS and CSCL as the main research content and ‘feature extraction’, ‘task analysis’, and ‘computation modeling’ as the hot research topics in the past three years. The current research power on CPS and CSCL is mainly distributed in China and the United States, but the inter-institutional collaboration is little. Based on the research results, this paper proposes four future directions to improve the research in online collaborative problem-solving: identity awareness in online collaborative problem-solving, interdisciplinary online collaborative problem-solving, online collaborative problem-solving for teachers, and online collaborative problem-solving and ICT skills.

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Section: Discussionmentioning

confidence: 99%

Research on Online Collaborative Problem-Solving in the Last 10 Years: Current Status, Hotspots, and Outlook—A Knowledge Graph Analysis Based on CiteSpace

et al. 2023

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“…Secondly, a cohesive and supportive faculty team creates a positive environment. MOOC platforms offer interactive tools that assist the faculty team, including instructors and teaching assistants, in problem-solving and facilitating learning inquiry [52], thereby enhancing personalized attention to students. Thirdly, passionate instructors, go beyond the curriculum, and inspire and motivate students.…”

Section: Facultymentioning

confidence: 99%

Leveraging text mining and analytic hierarchy process for the automatic evaluation of online courses

Chen,

Xie,

Tao

et al. 2024

Int. J. Mach. Learn. & Cyber.

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This study introduced a multi-criteria decision-making methodology leveraging text mining and analytic hierarchy process (AHP) for online course quality evaluation based on students’ feedback texts. First, a hierarchical structure of online course evaluation criteria was formulated by integrating topics (sub-criteria) identified through topic modeling and interpreted based on transactional distance and technology acceptance theories. Second, the weights of the criteria in the hierarchical structure were determined based on topic proportions. Third, the AHP was employed to determine the overall relative advantage of online courses and their relative advantage within each criterion based on the hierarchical framework and criterion weights. The proposed approach was implemented on the datasets of 6940 reviews for knowledge-seeking courses in Art, Design, and Humanities (D1) and 44,697 reviews for skill-seeking courses in Computer Science, Engineering, and Programming (D2) from Class Central to determine ranking positions of nine courses from both D1 and D2 as alternatives. Results revealed common concerns among knowledge and skill-seeking course learners, encompassing “assessment”, “content”, “effort”, “usefulness”, “enjoyment”, “faculty”, “interaction”, and “structure”. The article provides valuable insights into the online course evaluation and selection processes for learners in D1 and D2 groups. Notably, both groups prioritize “effort” and “faculty”, while D2 learners value “assessment” and “enjoyment”, and D1 learners value “usefulness” more. This study demonstrates the efficacy of leveraging online learner reviews and topic modeling for automating MOOC evaluation and informing learners’ decision-making processes.

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How Learning Process Data Can Inform Regulation in Collaborative Learning Practice

Järvelä

Vuorenmaa²,

Çini³

et al. 2023

Advances in Analytics for Learning and Teaching

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Using multimodal analytics to systemically investigate online collaborative problem-solving

Cited by 13 publications

References 68 publications

Research on Online Collaborative Problem-Solving in the Last 10 Years: Current Status, Hotspots, and Outlook—A Knowledge Graph Analysis Based on CiteSpace

Research on Online Collaborative Problem-Solving in the Last 10 Years: Current Status, Hotspots, and Outlook—A Knowledge Graph Analysis Based on CiteSpace

Leveraging text mining and analytic hierarchy process for the automatic evaluation of online courses

How Learning Process Data Can Inform Regulation in Collaborative Learning Practice

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