Name
Abstract | ||
|---|---|---|
While cognitive behaviors and social network structure in Online Learning Community (OLC) have been studied in the past, few research has proposed a model linking the two important factors to analyze students’ cognitive learning gains, even though it has been widely acknowledged that interaction is a significant way for students to exchange knowledge and obtain learning gains. In this paper, for a better indication of cognitive gains, we introduce an analytic model to quantify the students’ learning gains by using a redesigned taxonomy of cognitive behaviors while considering the flow of knowledge among students in discussion forums. And further, we implement a learning analytics system to streamline the data analysis pipeline of social network analysis, cognition classification and learning gain calculation and visualize the analytic results from multiple-level views including student, discussion thread and forum. We demonstrate the results on a MOOC course and confirm the effectiveness of our model. Our model and analytic system enable instructors and TAs to take active mediation among online discussions of students to improve their cognitive gains through OLCs. |
Year | Venue | Field |
|---|---|---|
2018 | APWeb/WAIM Workshops | Data science,Learning gain,Social network,Learning analytics,Computer science,Social network analysis,Mediation (Marxist theory and media studies),Artificial intelligence,Cognition,Analytic model,Machine learning,Online learning community |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
7 | 2 | |