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Topic Classification on Short Reflective Writings for Monitoring Students’ Progress

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Blended Learning. New Challenges and Innovative Practices (ICBL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10309))

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Abstract

Reflection has been widely considered as an important element in student learning in higher education. Among different forms of reflective writing, one-minute papers can quickly and easily get students to reflect on their learning. Unlike short quizzes, the responses to one-minute papers could cover a wide open range and could require more time to review and summarize. When one-minute papers are administrated online, their responses are available in electronic form and this facilitates a computational approach for analysis. In this paper, we propose a machine learning approach to analyzing the students’ responses to one-minute papers. We build a text classifier to identify the topics discussed in the responses. Our results of a preliminary study conducted in a blended learning course demonstrate that the classifier can effectively detect the topics and the proposed method can be used to monitor student progress based on the detected topics.

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Notes

  1. 1.

    http://www.nltk.org .

  2. 2.

    http://scikit-learn.org .

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Author information

Authors and Affiliations

  1. Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, China

    Leonard K. M. Poon & Gary Cheng

  2. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China

    Zichao Li

Authors
  1. Leonard K. M. Poon
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  2. Zichao Li
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  3. Gary Cheng
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Corresponding author

Correspondence to Leonard K. M. Poon .

Editor information

Editors and Affiliations

  1. The Open University of Hong Kong , Hong Kong, Hong Kong

    Simon K.S. Cheung

  2. City University of Hong Kong , Hong Kong, China

    Lam-for Kwok

  3. Hong Kong Shue Yan University , Hong Kong, Hong Kong

    Will W.K. Ma

  4. The Open University of Hong Kong , Hong Kong, Hong Kong

    Lap-Kei Lee

  5. State University of New York at Oswego , Oswego, New York, USA

    Harrison Yang

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Poon, L.K.M., Li, Z., Cheng, G. (2017). Topic Classification on Short Reflective Writings for Monitoring Students’ Progress. In: Cheung, S., Kwok, Lf., Ma, W., Lee, LK., Yang, H. (eds) Blended Learning. New Challenges and Innovative Practices. ICBL 2017. Lecture Notes in Computer Science(), vol 10309. Springer, Cham. https://doi.org/10.1007/978-3-319-59360-9_21

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  • DOI: https://doi.org/10.1007/978-3-319-59360-9_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59359-3

  • Online ISBN: 978-3-319-59360-9

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