# Goal-based Course Recommendation

arXiv: https://arxiv.org/abs/1812.10078 · HTML: https://mlsanity.com/paper/1812.10078/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2018.

## Built on by (most cited first, 17 of 17)

- [From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents](https://mlsanity.com/paper/2409.03512/) (2024)
- [Towards Equity and Algorithmic Fairness in Student Grade Prediction](https://mlsanity.com/paper/2105.06604/) (2021)
- [PEEK: A Large Dataset of Learner Engagement with Educational Videos](https://mlsanity.com/paper/2109.03154/) (2021)
- [Towards Goal-oriented Intelligent Tutoring Systems in Online Education](https://mlsanity.com/paper/2312.10053/) (2023)
- [Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolution](https://mlsanity.com/paper/2112.02034/) (2021)
- [A Review of Data Mining in Personalized Education: Current Trends and Future Prospects](https://mlsanity.com/paper/2402.17236/) (2024)
- [Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems](https://mlsanity.com/paper/2112.04368/) (2021)
- [Finding Paths for Explainable MOOC Recommendation: A Learner Perspective](https://mlsanity.com/paper/2312.10082/) (2023)
- [FaiREO: User Group Fairness for Equality of Opportunity in Course Recommendation](https://mlsanity.com/paper/2109.05931/) (2021)
- [Towards Proactive Information Retrieval in Noisy Text with Wikipedia Concepts](https://mlsanity.com/paper/2210.09877/) (2022)
- [Reinforced Approximate Exploratory Data Analysis](https://mlsanity.com/paper/2212.06225/) (2022)
- [Course Recommender Systems Need to Consider the Job Market](https://mlsanity.com/paper/2404.10876/) (2024)
- [Deep Learning for Educational Data Science](https://mlsanity.com/paper/2404.19675/) (2024)
- [Gaining Insights into Group-Level Course Difficulty via Differential Course Functioning](https://mlsanity.com/paper/2406.04348/) (2024)
- [Investigating Health-Aware Smart-Nudging with Machine Learning to Help People Pursue Healthier Eating-Habits](https://mlsanity.com/paper/2110.07045/) (2021)
- [How Good Are Large Language Models for Course Recommendation in MOOCs?](https://mlsanity.com/paper/2504.08208/) (2025)
- [Skill-based Explanations for Serendipitous Course Recommendation](https://mlsanity.com/paper/2508.19569/) (2025)

## Abstract (arXiv)

With cross-disciplinary academic interests increasing and academic advising resources over capacity, the importance of exploring data-assisted methods to support student decision making has never been higher. We build on the findings and methodologies of a quickly developing literature around prediction and recommendation in higher education and develop a novel recurrent neural network-based recommendation system for suggesting courses to help students prepare for target courses of interest, personalized to their estimated prior knowledge background and zone of proximal development. We validate the model using tests of grade prediction and the ability to recover prerequisite relationships articulated by the university. In the third validation, we run the fully personalized recommendation for students the semester before taking a historically difficult course and observe differential overlap with our would-be suggestions. While not proof of causal effectiveness, these three evaluation perspectives on the performance of the goal-based model build confidence and bring us one step closer to deployment of this personalized course preparation affordance in the wild.