  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.
