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Conference paper Open Access

Semantic Topic Chains for Modeling Temporality of Themes in Online Student Discussion Forums

Harshita Chopra; Yiwen Lin; Mohammad Amin Samadi; Jacqueline G. Cavazos; Renzhe Yu; Spencer Jaquay; Nia Nixon

Mingyu Feng; Tanja Käser; Partha Talukdar

Exploring students' discourse in academic settings over time can provide valuable insight into the evolution of learner engagement and participation in online learning. In this study, we propose an analytical framework to capture topics and the temporal progression of learner discourse. We employed a Contextualized Topic Modeling technique on messages posted by undergraduates in online discussion forums from Fall 2019 to Spring 2020. We further evaluated if topics were originating from specific courses or more generally distributed across multiple courses. Our results suggested a significant increase in the number of general topics after the onset of the pandemic, suggesting emergent topics being discussed in a range of courses. In addition, using Word Mover's Distance, we examined the semantic similarity of topics in adjacent months and constructed topic chains. Our findings indicated that previously course-centric topics such as public health developed into more general discussions that emphasize inequities and healthcare during the pandemic. Furthermore, emergent topics around students� lived experiences underscored the role of discussion forums in capturing educational experiences temporally. Finally, we discuss the implications of current findings for post-pandemic higher education and the effectiveness of our framework in exploring unstructured large-scale educational data.

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