Predicting learners' performance using MOOC clickstream
Paper
Paper/Presentation Title | Predicting learners' performance using MOOC clickstream |
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Presentation Type | Paper |
Authors | Xiao, Kui, Pan, Xueyan, Zhang, Yan, Tao, Xiaohui and Huang, Zhifang |
Journal or Proceedings Title | Proceedings of the 19th International Conference on Advanced Data Mining and Applications (ADMA'23) |
Journal Citation | pp. 607-619 |
Number of Pages | 13 |
Year | 2023 |
Publisher | Springer |
Place of Publication | Switzerland |
ISBN | 9783031466731 |
9783031466748 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-031-46674-8_42 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-031-46674-8_42 |
Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-3-031-46674-8 |
Conference/Event | 19th International Conference on Advanced Data Mining and Applications (ADMA'23) |
Event Details | 19th International Conference on Advanced Data Mining and Applications (ADMA'23) Parent International Conference on Advanced Data Mining and Applications Delivery In person Event Date 21 to end of 23 Aug 2023 Event Location Shenyang, China Rank B B B B B B |
Abstract | Massive Open Online Courses (MOOCs) have gradually become a dominant trend in online education. However, due to the large number of learners participating in MOOCs, teachers usually cannot accurately know the learning outcomes of each MOOC user. In addition, many learners did not take the corresponding quiz after watching the MOOCs’ videos, and some MOOC videos even did not contain a quiz, which makes it difficult to evaluate the learners’ performance. In the absence of learners’ test scores, how to evaluate learners’ performance has become a huge challenge. In this paper, we build a MOOC platform and collect user clickstream data in course videos, and propose a novel approach for predicting learners’ performance based on MOOC clickstream. We use MOOC clickstream data to define handcrafted features and embedding features of user learning behavior, which are used to infer learners’ performance. Experimental results show that the performance of the proposed method exceeds that of the state-of-the-art methods. |
Keywords | Learners’ performance; Learning outcome; Clickstream; MOOC; E-learning |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460502. Data mining and knowledge discovery |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Series | Lecture Notes in Computer Science |
Byline Affiliations | Hubei University, China |
University of Southern Queensland | |
Hubei Normal University, China |
https://research.usq.edu.au/item/z9w7w/predicting-learners-performance-using-mooc-clickstream
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