Personalized Course Sequence Recommendations

TL;DR

This paper introduces a personalized course sequence recommendation system that uses algorithms to optimize graduation time and GPA by considering student backgrounds, prerequisites, and course availability, demonstrated with real UCLA data.

Contribution

It develops a novel forward-search backward-induction algorithm and a multi-armed bandit approach for personalized course sequence recommendations considering student context.

Findings

Algorithms outperform non-personalized methods

Personalization reduces graduation time

Improves student GPA

Abstract

Given the variability in student learning it is becoming increasingly important to tailor courses as well as course sequences to student needs. This paper presents a systematic methodology for offering personalized course sequence recommendations to students. First, a forward-search backward-induction algorithm is developed that can optimally select course sequences to decrease the time required for a student to graduate. The algorithm accounts for prerequisite requirements (typically present in higher level education) and course availability. Second, using the tools of multi-armed bandits, an algorithm is developed that can optimally recommend a course sequence that both reduces the time to graduate while also increasing the overall GPA of the student. The algorithm dynamically learns how students with different contextual backgrounds perform for given course sequences and then recommends an optimal course sequence for new students. Using real-world student data from the UCLA Mechanical and Aerospace Engineering department, we illustrate how the proposed algorithms outperform other methods that do not include student contextual information when making course sequence recommendations.