1 research outputs found
Deep Learning to Predict Student Outcomes
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.Comment: Accepted as oral presentation to ICLR 2019, AI for Social Good
Workshop. arXiv admin note: substantial text overlap with arXiv:1809.06686,
arXiv:1804.0740