Learning Analytics in MOOCs: Can Data Improve Students Retention and Learning?

ID: 48652 Type: Full Paper: Conceptual & Empirical Study
  1. Mohammad Khalil and Martin Ebner, Graz University of Technology, Austria

Tuesday, June 28 1:30 PM-2:00 PM

No presider for this session.

In order to study learners’ behaviors and activities in online learning environments such as MOOCs, the demanding for a framework of practices and procedures to collect, analyze and optimize their data emerged in the educational learning horizon. Learning Analytics is the field that arose to comply with such needs and was denominated as a “technological fix to the long-standing problems” of online learning platforms (Knox, 2014). This paper discusses the significance of applying Learning Analytics in MOOCs to overcome some of its issues. We will mainly focus on improving students’ retention and learning using an algorithm prototype based on divergent MOOC indicators, and propose a scheme to reflect the results on MOOC students.

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