A Review of Educational Data Mining and Analysis of Learner Behavior in MOOCs

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Ghaffari Hossein; Ajoudanian Shohreh

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Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2020 | Issue:6

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Title

A Review of Educational Data Mining and Analysis of Learner Behavior in MOOCs

Author(s)

Ghaffari Hossein | Ajoudanian Shohreh

Keywords

Educational Data Mining

Learning Analysis

MOOC

MOOCs Educational Analysis

Online Learning

E-Learning

Abstract

In recent years, data mining has been widely used in the analysis of educational processes and the analysis of learners' behaviors in MOOCs. Therefore, in this study, we investigate different methods for analyzing learners' behavior in MOOCs as well as investigating factors of satisfaction and dissatisfaction and providing appropriate solutions for them. Through this study the most important methods used for data analysis are classification and clustering methods. Also the most important factors that make the students satisfied and dissatisfied with these courses are the reputation of the universities, the quality of the courses, the flexibility of the course, the time bruise, the lack of motivation, the isolation and the lack of interaction in the course and sufficient scientific background. The results of this study can help to gain a holistic view of the various elements of effective MOOCs and increase the rate of completion of the course.

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APA: Copy

Ghaffari, Hossein, & Ajoudanian, Shohreh. (2020). A Review of Educational Data Mining and Analysis of Learner Behavior in MOOCs. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/949268/en

Vancouver: Copy

Ghaffari Hossein, Ajoudanian Shohreh. A Review of Educational Data Mining and Analysis of Learner Behavior in MOOCs. 2020. Available from: https://sid.ir/paper/949268/en

IEEE: Copy

Hossein Ghaffari, and Shohreh Ajoudanian, “A Review of Educational Data Mining and Analysis of Learner Behavior in MOOCs,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2020, [Online]. Available: https://sid.ir/paper/949268/en

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