GritNet: Student Performance Prediction with Deep Learning
This work addresses the problem of timely educational interventions for students in online courses, though it is incremental as it applies an existing deep learning method to a new domain.
The paper tackles student performance prediction by recasting it as a sequential event problem and proposes GritNet, a deep learning algorithm based on bidirectional LSTM, which outperforms logistic regression methods, especially in early weeks when predictions are most difficult.
Student performance prediction - where a machine forecasts the future performance of students as they interact with online coursework - is a challenging problem. Reliable early-stage predictions of a student's future performance could be critical to facilitate timely educational interventions during a course. However, very few prior studies have explored this problem from a deep learning perspective. In this paper, we recast the student performance prediction problem as a sequential event prediction problem and propose a new deep learning based algorithm, termed GritNet, which builds upon the bidirectional long short term memory (BLSTM). Our results, from real Udacity students' graduation predictions, show that the GritNet not only consistently outperforms the standard logistic-regression based method, but that improvements are substantially pronounced in the first few weeks when accurate predictions are most challenging.