Personalized Course Sequence Recommendations

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.

References in corpus (2)

• Efficient Optimal Learning for Contextual Bandits
• Predicting Grades

Cited by in corpus (17)

• Personalized Education in the AI Era: What to Expect Next?
• Internet of Intelligence: A Survey on the Enabling Technologies, Applications, and Challenges
• Predicting Temporal Sets with Deep Neural Networks
• Analysis of the apparent nuclear modification in peripheral Pb-Pb collisions at 5.02 TeV
• Combining Difficulty Ranking with Multi-Armed Bandits to Sequence Educational Content
• Multi-objective Contextual Multi-armed Bandit with a Dominant Objective
• EdNet: A Large-Scale Hierarchical Dataset in Education
• Will this Course Increase or Decrease Your GPA? Towards Grade-aware Course Recommendation
• Sequence-Aware Recommender Systems
• Deep Reinforcement Learning for Adaptive Learning Systems
• Community College Articulation Agreement Websites: Students' Suggestions for New Academic Advising Software Features
• Asymmetric Graph Error Control with Low Complexity in Causal Bandits
• MOOCRep: A Unified Pre-trained Embedding of MOOC Entities
• Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments
• Course Difficulty Estimation Based on Mapping of Bloom's Taxonomy and ABET Criteria
• Optimal Academic Plan Derived from Articulation Agreements: A Preliminary Experiment on Human-Generated and (Hypothetical) Algorithm-Generated Academic Plans
• Core Course Analysis for Undergraduate Students in Mathematics