Topic Transitions in MOOCs: An Analysis Study

Fareedah ALSaad, Thomas Reichel, Yuchen Zeng, Abdussalam Alawini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the emergence of MOOCs, it becomes crucial to automate the process of a course design to accommodate the diverse learning demands of students. Modeling the relationships among educational topics is a fundamental first step for automating curriculum planning and course design. In this paper, we introduce Topic Transition Map (TTM), a general structure that models the content of MOOCs at the topic level. TTMs capture the various ways instructors organize topics in their courses by modeling the transitions between topics. We investigate and analyze four different methods that can be exploited to learn the Topic Transition Map: 1) Pairwise Constrained K-Means, 2) Mixture of Unigram Language Model, 3) Hidden Markov Mixture Model, and 4) Structural Topic Model. To evaluated the effectiveness of these methods, we qualitatively compare the topic transition maps generated by each model and investigate how the Topic Transition Map can be used in three sequencing tasks: 1) determining the correct sequence, 2) predicting the next lecture, and 3) predicting the sequence of lectures. Our evaluation revealed that PCK-Means has the highest performance in the first task, HMMULM outperforms other methods in task 2, while there is no winning in task 3.

Original languageEnglish (US)
Title of host publicationProceedings of the 14th International Conference on Educational Data Mining, EDM 2021
EditorsI-Han Hsiao, Shaghayegh Sahebi, Francois Bouchet, Jill-Jenn Vie
PublisherInternational Educational Data Mining Society
Pages139-149
Number of pages11
ISBN (Electronic)9781733673624
StatePublished - 2021
Event14th International Conference on Educational Data Mining, EDM 2023 - Paris, France
Duration: Jun 29 2021Jul 2 2021

Publication series

NameProceedings of the 14th International Conference on Educational Data Mining, EDM 2021

Conference

Conference14th International Conference on Educational Data Mining, EDM 2023
Country/TerritoryFrance
CityParis
Period6/29/217/2/21

Keywords

  • Clusters
  • Hidden Markov Model
  • Mixture Model
  • Sequencing Tasks
  • Topic Transition
  • Topic Transition Map
  • Word Distribution

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

Fingerprint

Dive into the research topics of 'Topic Transitions in MOOCs: An Analysis Study'. Together they form a unique fingerprint.

Cite this