Deep Learning to Predict Student Outcomes
Project Overview
The document discusses the innovative application of generative AI in education through GritNet, a deep learning architecture specifically developed to predict student outcomes in Massive Open Online Courses (MOOCs) in real time. GritNet employs a domain adaptation framework that enables it to transfer knowledge from one course to another without needing labeled data from the target course, thereby enhancing its applicability across various educational contexts. By capturing sequential student actions, GritNet significantly improves prediction accuracy, particularly during the critical early weeks of a course when predicting student success is typically more challenging. The findings suggest that this technology holds considerable promise for enhancing student engagement and improving success rates in online learning environments, ultimately contributing to more personalized and effective educational experiences.
Key Applications
GritNet for predicting student outcomes
Context: MOOCs, targeting students enrolled in various Nanodegree programs on platforms like Udacity.
Implementation: The GritNet architecture was trained on historical student performance data from past courses and was adapted to new courses using a domain adaptation method without needing labeled data from the new courses.
Outcomes: Significantly improved prediction accuracy for student graduation rates, particularly in the early weeks of courses, enhancing real-time predictions and enabling timely educational interventions.
Challenges: The need for effective transfer learning techniques to handle different course structures and student demographics, as well as the lack of labeled data in new courses.
Implementation Barriers
Data limitation
The challenge of working with unlabeled data from new courses, which complicates the training and adaptation processes.
Proposed Solutions: Using pseudo-labels generated from the trained model on source courses to guide the adaptation process for target courses.
Project Team
Byung-Hak Kim
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Byung-Hak Kim
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai