| Abstract: | The open nature of Massive Open Online Courses (MOOCs) attracts a large number of learners with different backgrounds, skills, motivations, and goals. This has brought a need to understand such heterogeneity in populations of MOOC learners. Categorizing these learners based upon their interaction with the course can help address this need and suggest possible improvements in course design and delivery. In this article, the K-means clustering technique with careful seeding is used to obtain clusters of learners having similar interaction in the course. Learners are grouped based on their interaction with course material, video lectures, discussion forums, and assessments. In the analysis of thirteen courses, the proposed method identified learners’ classes as Uninterested, Casuals, Performers, Explorers and Achievers. Each class of learners had distinct interaction with the course and followed a certain learning approach. The learners’ classes were mapped to the standard surface, deep, and strategic learning approaches.This article also highlights the data preparation phase and its importance in data mining. |
