Untangling chaos in discussion forums: A temporal analysis of topic-relevant forum posts in MOOCs

• Published in: Computers and education Vol. 178; p. 104402
• Main Authors: Yang, Bokai, Tang, Hengtao, Hao, Ling, Rose, John R.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2022
• Subjects: Chaos; Discussion forum; Machine learning; Temporal dimension; Topic-related posts; Chaos; Discussion forum; Topic-related posts; Temporal dimension; Machine learning
• ISSN: 0360-1315; 1873-782X
• DOI: 10.1016/j.compedu.2021.104402

An effective experience in discussion forums is important for online learners to maintain their persistence in a MOOC. The purpose of this research is to identify learners’ meaningful participation patterns of topic-related forum posts through the temporal dimension and investigate how the longitudinal trajectory of online meaningful participation is associated with learner performance. Specifically, latent semantic analysis (LSA) and machine learning approaches were used to classify forum posts. Inferential statistic methods were then used to quantify the effect of the temporal dimension of meaningful forum participation on learner performance in MOOCs. The findings of this research provided significant implications on facilitating effective forum discussions and supporting learner performance in MOOCs. •Classified topic-relevant forum posts in MOOCs using latent semantic analysis machine learning algorithm.•Identified five clusters of learners with distinct longitudinal profiles of topic-relevant forum posting.•Confirmed the importance of temporal dimension of learners' meaningful forum participation in MOOCs.