Skip to main content

A Machine Learning Approach to Personalizing Education: Improving Individual Learning through Tracking and Course Recommendation

Supervisors

Suitable for

MSc in Computer Science
Mathematics and Computer Science, Part C
Computer Science and Philosophy, Part C
Computer Science, Part C

Abstract

More students are enrolling in college and professional degree programs than ever before. However, current degree programs are often “one size fits all”; such programs ignore the heterogeneity of students in terms of backgrounds, abilities, learning styles and career goals. Moreover, because of ever-increasing student/teacher ratios, students are often left struggling to find their own pathways through degree programs. The combination leads to poor learning outcomes, low engagement, dissatisfaction and high dropout rates. In this project, an interactive electronic system will be built that is personalized for each student, is able to continuously track progress and goals, capitalize on the knowledge accumulated, and recommend suitable courses and activities in order to build skills, enhance interest and promote  long-term goals. In effect, our personalized interactive system operates as “if” there is a dedicated mentor for each student. To build this system, the following modules will need to be developed: (1) student and course similarity discovery methods; (2) student performance prediction algorithms; (3) personalized course recommendation algorithms.

To read more about the role of machine learning in education – see medianetlab.ee.ucla.edu/EduAdvance

Prerequisites: This project is suitable for someone with at least basic knowledge of machine learning