# Personalized Course Sequence Recommendations

arXiv: https://arxiv.org/abs/1512.09176 · HTML: https://mlsanity.com/paper/1512.09176/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2015.

## Built on by (most cited first, 9 of 9)

- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)
- [Personalized Education in the AI Era: What to Expect Next?](https://mlsanity.com/paper/2101.10074/) (2021)
- [Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments](https://mlsanity.com/paper/2103.12198/) (2021)
- [Asymmetric Graph Error Control with Low Complexity in Causal Bandits](https://mlsanity.com/paper/2408.11240/) (2024)
- [SmartCourse: A Contextual AI-Powered Course Advising System for Undergraduates](https://mlsanity.com/paper/2507.22946/) (2025)
- [MOOCRep: A Unified Pre-trained Embedding of MOOC Entities](https://mlsanity.com/paper/2107.05154/) (2021)
- [Course Difficulty Estimation Based on Mapping of Bloom's Taxonomy and ABET Criteria](https://mlsanity.com/paper/2112.01241/) (2021)
- [Skill-based Explanations for Serendipitous Course Recommendation](https://mlsanity.com/paper/2508.19569/) (2025)
- [KNOWPLAN: Knowledge-Driven AI Agents for Smart Degree Pathway Planning](https://mlsanity.com/paper/2608.06530/) (2026)

## Abstract (arXiv)

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.