Authors:
Youngnam Lee
1
;
Dongmin Shin
1
;
HyunBin Loh
1
;
Jaemin Lee
1
;
Piljae Chae
1
;
Junghyun Cho
1
;
Seoyon Park
1
;
Jinhwan Lee
1
;
Jineon Baek
2
;
Byungsoo Kim
1
and
Youngduck Choi
3
Affiliations:
1
Riiid! AI Research, Korea
;
2
Riiid! AI Research, Korea, University of Michigan, U.S.A.
;
3
Riiid! AI Research, Korea, Yale University, U.S.A.
Keyword(s):
Education, Artificial Intelligence, Transformer.
Abstract:
Student dropout prediction provides an opportunity to improve student engagement, which maximizes the overall effectiveness of learning experiences. However, researches on student dropout were mainly conducted on school dropout or course dropout, and study session dropout in a mobile learning environment has not been considered thoroughly. In this paper, we investigate the study session dropout prediction problem in a mobile learning environment. First, we define the concept of the study session, study session dropout and study session dropout prediction task in a mobile learning environment. Based on the definitions, we propose a novel Transformer based model for predicting study session dropout, DAS: Deep Attentive Study Session Dropout Prediction in Mobile Learning Environment. DAS has an encoder-decoder structure which is composed of stacked multi-head attention and point-wise feed-forward networks. The deep attentive computations in DAS are capable of capturing complex relations
among dynamic student interactions. To the best of our knowledge, this is the first attempt to investigate study session dropout in a mobile learning environment. Empirical evaluations on a large-scale dataset show that DAS achieves the best performance with a significant improvement in area under the receiver operating characteristic curve compared to baseline models.
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