Comparison of learning analytics and educational data mining: A topic modeling approach

Abstract

Educational data mining and learning analytics, although experiencing an upsurge in exploration and use, continue to elude precise definition; the two terms are often interchangeably used. This could be owing to the fact that the two fields exhibit common thematic elements. One avenue to provide clarity, uniformity, and consistency around the two fields, is to identify similarities and differences in topics between the two evolving fields. This paper conducted a topic modeling analysis of articles related to educational data mining and learning analytics to reveal thematic features of the two fields. Specifically, we employed structural topic modeling to identify the topics of the two fields from the abstracts. We apply structural topic modeling on N=192 articles for educational data mining and N=489 articles for learning analytics. We infer five-topic models for both educational data mining and learning analytics. We find that while there appears to be disciplinary differences in terms of research focus, there is little support for a clear distinction between the two disciplines, beyond their different lineage. The trend points to a convergence within the field of educational research on the applications of advanced statistical learning techniques to extract actionable insights from large data streams for optimizing teaching and learning. Both fields have converged on an increasing focus on student behaviors over the last five years.

Keywords

• Educational data mining
• Learning analytics
• Topic modeling
• Structural topic modeling (stm)