Performance Prediction and Importance Analysis Using Transformer
Abstract
The growth of online education has made it easier to capture learner activity. I t is expected that detailed feedback to learners will lead to better performance. For this purpose, it is important to predict the performance of learners. Methods using classical machine learning and RNNs that take time series information into account have been proposed. In this paper, we propose a Transformer -based performance prediction method that aims to improve accuracy and extract important activity . The proposed method achieves more accurate performance prediction than conventional methods. In addit ion, we found that NEXT, SEARCH_JUMP and LINK_CLICK are important behaviors by analyzing the rationale of the Transformer.Downloads
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Published
2021-11-22
Conference Proceedings Volume
Section
Articles
How to Cite
Performance Prediction and Importance Analysis Using Transformer. (2021). International Conference on Computers in Education. http://library.apsce.net/index.php/ICCE/article/view/4289