@inproceedings{be2534caa48540f090674e61d91cea32,
title = "Topic Transitions in MOOCs: An Analysis Study",
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.",
keywords = "Clusters, Hidden Markov Model, Mixture Model, Sequencing Tasks, Topic Transition, Topic Transition Map, Word Distribution",
author = "Fareedah ALSaad and Thomas Reichel and Yuchen Zeng and Abdussalam Alawini",
note = "Publisher Copyright: {\textcopyright} EDM 2021.All rights reserved.; 14th International Conference on Educational Data Mining, EDM 2023 ; Conference date: 29-06-2021 Through 02-07-2021",
year = "2021",
language = "English (US)",
series = "Proceedings of the 14th International Conference on Educational Data Mining, EDM 2021",
publisher = "International Educational Data Mining Society",
pages = "139--149",
editor = "I-Han Hsiao and Shaghayegh Sahebi and Francois Bouchet and Jill-Jenn Vie",
booktitle = "Proceedings of the 14th International Conference on Educational Data Mining, EDM 2021",
}