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International Symposium on Emerging Technologies for Education

SETE 2019: Emerging Technologies for Education pp 133–142Cite as

Topic Detection for Online Course Feedback Using LDA

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11984)

Abstract

In an online course, student feedback is used widely in order to enhance the quality of teaching and learning process by improving the teacher-student relationship. If a lecturer wants to get a summary of these comments, the lecturer has to manually read and summarize all these comments. However, dealing with a very large number of comments is difficult. In this paper, we proposed an approach for topic detection for online course feedback by adopting Latent Dirichlet Allocation (LDA). The course feedback from the website of Coursera (i.e., Machine Learning course) is used to demonstrate the effectiveness of our approach.

Keywords

  • Course feedback
  • Online learning
  • Topic detection
  • LDA

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Notes

  1. 1.

    http://lucene.apache.org.

  2. 2.

    https://www.coursera.org/.

  3. 3.

    https://www.kaggle.com/septa97/100k-courseras-course-reviews-dataset.

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Authors and Affiliations

  1. Information Technology Division, Faculty of Science, Maejo University, Chiang Mai, Thailand

    Sayan Unankard & Wanvimol Nadee

Authors
  1. Sayan Unankard
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  2. Wanvimol Nadee
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Corresponding author

Correspondence to Sayan Unankard .

Editor information

Editors and Affiliations

  1. University of Craiova, Craiova, Romania

    Elvira Popescu

  2. South China Normal University, Guangzhou, China

    Tianyong Hao

  3. National Taiwan Normal University, Taipei, Taiwan

    Dr. Ting-Chia Hsu

  4. Lingnan University, Hong Kong, Hong Kong

    Haoran Xie

  5. Sapienza University of Rome, Rome, Italy

    Marco Temperini

  6. Chinese Academy of Agricultural Sciences, Beijing, China

    Wei Chen

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Unankard, S., Nadee, W. (2020). Topic Detection for Online Course Feedback Using LDA. In: Popescu, E., Hao, T., Hsu, TC., Xie, H., Temperini, M., Chen, W. (eds) Emerging Technologies for Education. SETE 2019. Lecture Notes in Computer Science(), vol 11984. Springer, Cham. https://doi.org/10.1007/978-3-030-38778-5_16

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  • DOI: https://doi.org/10.1007/978-3-030-38778-5_16

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