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.
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Acknowledgement
This work is supported by the Ministry of Education’s Youth Fund for Humanities and Social Sciences Project (No.19YJC880036),the National Natural Science Foundation of China (Nos.62102136, 61902114, 61977021), the Key R & D projects in Hubei Province (Nos.2021BAA188, 2021BAA184, 2022BAA044), the Science and Technology Innovation Program of Hubei Province (No.2020AEA008).
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Xiao, K., Pan, X., Zhang, Y., Tao, X., Huang, Z. (2023). Predicting Learners’ Performance Using MOOC Clickstream. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14179. Springer, Cham. https://doi.org/10.1007/978-3-031-46674-8_42
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