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Modeling Student Discourse in Online Discussion Forums Using Semantic Similarity Based Topic Chains

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13356)

Abstract

Students’ conversations in academic settings evolve over time and can be affected by events such as the COVID-19 pandemic. In this paper, we employ a Contextualized Topic Modeling technique to detect coherent topics from students’ posts in online discussion forums. We construct topic chains by connecting semantically similar topics across months using Word Mover’s Distance. Consistent academic discourse and contemporary events such as the COVID-19 outbreak and the Black Lives Matter movement were found among prominent topics. In later months, new themes around students’ lived experiences emerged and evolved into discussions reflecting the shift in educational experiences. Results revealed a significant increase in more general topics after the onset of pandemic. Our proposed framework can also be applied to other contexts investigating temporal topic trends in large-scale text data.

Keywords

  • Text mining
  • Discourse analysis
  • Topic modeling

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Fig. 1.

Notes

  1. 1.

    github.com/The-Language-and-Learning-Analytics-Lab/topic_trends.

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Correspondence to Harshita Chopra .

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Chopra, H. et al. (2022). Modeling Student Discourse in Online Discussion Forums Using Semantic Similarity Based Topic Chains. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_91

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  • DOI: https://doi.org/10.1007/978-3-031-11647-6_91

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  • Publisher Name: Springer, Cham

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