International Journal of Soft Computing And Artificial Intelligence (IJSCAI)
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Volume-9,Issue-1  ( May, 2021 )
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  Journal Paper

Paper Title :
Impact of Students' Position in the Online Discussion Network on their Learning Performance

Author :Pramodi De Silva, Chandima Imalika, Thushani Weerasinghe, Amitha Caldera

Article Citation :Pramodi De Silva ,Chandima Imalika ,Thushani Weerasinghe ,Amitha Caldera , (2019 ) " Impact of Students' Position in the Online Discussion Network on their Learning Performance " , International Journal of Soft Computing And Artificial Intelligence (IJSCAI) , pp. 25-31, Volume-7,Issue-1

Abstract : Online learning has become a prominent practice among educational institutions due to its power to overstep time, space and cost constraints. Although it lacks face-to-face physical interactions among students and facilitators, they are persuaded to communicate virtually through online collaborative learning platforms. More precisely, through online discussion forums students can interact with peers and expand their knowledge by building a social network among them. However, with the massive number of students in online courses and the limited capacity of Learning Management Systems, it is hard to get deep insights on students’ social behavior. For instance, even the facilitators feel difficult to gain a broad image of how actually the students interact with each other, whether they are connected properly or isolated, are they gaining the maximum benefit out of discussions to complement the lack of physical interactions and are the discussions really help them in learning achievements etc. Therefore, this study focused on following an analysis of interaction and assignment data in online learning environment to identify how the position of a student in the discussion network impact his/her learning performance. The research followed Social Network Analysis combined with Statistical and Data Mining techniques. Finally, the study highlighted the importance of considering the connectedness among students rather than only considering the number of interaction by each in evaluating students’ performance and productivity of discussions. Keywords - Collaborative Learning, Computer-Supported Collaborative Learning, Social Network Analysis, Learner Analytics, Educational Data Mining, Performance

Type : Research paper

Published : Volume-7,Issue-1


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