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In-depth Exploration of Engagement Patterns in MOOCs

  • Lei Shi
  • Alexandra I. Cristea
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11234)

Abstract

With the advent of ‘big data’, various new methods have been proposed, to explore data in several domains. In the domain of learning (and e-learning, in particular), the outcomes lag somewhat behind. This is not unexpected, as e-learning has the additional dimensions of learning and engagement, as well as other psychological aspects, to name but a few, beyond ‘simple’ data crunching. This means that the goals of data exploration for e-learning are somewhat different to the goals for practically all other domains: finding out what students do is not enough, it is the means to the end of supporting student learning and increasing their engagement. This paper focuses specifically on student engagement, a crucial issue especially for MOOCs, by studying in much greater detail than previous work, the engagement of students based on clustering students according to three fundamental (and, arguably, comprehensive) dimensions: learning, social and assessment. The study’s value lies also in the fact that it is among the few studies using real-world longitudinal data (6 runs of a course, over 3 years) from a large number of students.

Keywords

E-learning Learning analysis Behavioral analysis Clustering analysis K-means MOOCs 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of LiverpoolLiverpoolUK
  2. 2.Durham UniversityDurhamUK

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