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      A Social Network Analysis on Blended Courses

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          Abstract

          The large-scale online management systems (e.g. Moodle), online web forums (e.g. Piazza), and online homework systems (e.g. WebAssign) have been widely used in the blended courses recently. Instructors can use these systems to deliver class content and materials. Students can communicate with the classmates, share the course materials, and discuss the course questions via the online forums. With the increased use of the online systems, a large amount of students' interaction data has been collected. This data can be used to analyze students' learning behaviors and predict students' learning outcomes. In this work, we collected students' interaction data in three different blended courses. We represented the data as directed graphs and investigated the correlation between the social graph properties and students' final grades. Our results showed that in all these classes, students who asked more answers and received more feedbacks on the forum tend to obtain higher grades. The significance of this work is that we can use the results to encourage students to participate more in forums to learn the class materials better; we can also build a predictive model based on the social metrics to show us low performing students early in the semester.

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          Predicting Student Exam’s Scores by Analyzing Social Network Data

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            Exploring the Effect of Confusion in Discussion Forums of Massive Open Online Courses

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              Deconstructing the Discussion Forum

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                Author and article information

                Journal
                28 September 2017
                Article
                1709.10215

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                GEDM 2017 proceedings(p. 22-26)
                In: EDM 2017 Extended Proceedings: Workshop Proceedings of the 10th International Conference on Educational Data Mining. Wuhan (China)
                cs.SI cs.CY

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