GritNet: Student Performance Prediction with Deep Learning

Understand

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).