Topic Transitions in MOOCs: An Analysis Study [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (14th, Online, Jun 29-Jul 2, 2021).

Autor/inn/en	ALSaad, Fareedah; Reichel, Thomas; Zeng, Yuchen; Alawini, Abdussalam
Titel	Topic Transitions in MOOCs: An Analysis Study [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (14th, Online, Jun 29-Jul 2, 2021).
Quelle	(2021), (11 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Online Courses; Student Diversity; Student Needs; Course Content; Models; Program Effectiveness; Markov Processes; Prediction; Sequential Approach; Lecture Method + Suchen Sie Ihr Suchwort? Online course; Online-Kurs; Kursprogramm; Analogiemodell; Markowscher Prozess; Vorhersage; Schrittfolge
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. [For the full proceedings, see ED615472.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01