Deep Learning to Predict Student Outcomes
This addresses the need for accurate real-time outcome predictions in online education, though it is incremental as it builds on existing domain adaptation methods.
The paper tackles real-time student performance prediction in ongoing online courses using a domain adaptation framework, achieving generalization across different courses and enhancing early-week predictions.
The increasingly fast development cycle for online course contents, along with the diverse student demographics in each online classroom, make real-time student outcomes prediction an interesting topic for both industrial research and practical needs. In this paper, we tackle the problem of real-time student performance prediction in an on-going course using a domain adaptation framework. This framework is a system trained on labeled student outcome data from previous coursework but is meant to be deployed on another course. In particular, we introduce a GritNet architecture, and develop an unsupervised domain adaptation method to transfer a GritNet trained on a past course to a new course without any student outcome label. Our results for real Udacity student graduation predictions show that the GritNet not only generalizes well from one course to another across different Nanodegree programs, but also enhances real-time predictions explicitly in the first few weeks when accurate predictions are most challenging.