# Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

arXiv: https://arxiv.org/abs/2312.10082 · HTML: https://mlsanity.com/paper/2312.10082/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms](https://mlsanity.com/paper/2011.01731/) (2020)
- [Reinforcement Knowledge Graph Reasoning for Explainable Recommendation](https://mlsanity.com/paper/1906.05237/) (2019)
- [Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation](https://mlsanity.com/paper/1805.03352/) (2018)
- [CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation](https://mlsanity.com/paper/2010.15620/) (2020)
- [Goal-based Course Recommendation](https://mlsanity.com/paper/1812.10078/) (2018)
- [Interplay between Upsampling and Regularization for Provider Fairness in Recommender Systems](https://mlsanity.com/paper/2006.04279/) (2020)
- [Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations](https://mlsanity.com/paper/2006.04282/) (2020)
- [Combating the Filter Bubble: Designing for Serendipity in a University Course Recommendation System](https://mlsanity.com/paper/1907.01591/) (2019)
- [SkillNER: Mining and Mapping Soft Skills from any Text](https://mlsanity.com/paper/2101.11431/) (2021)
- [UniWalk: Explainable and Accurate Recommendation for Rating and Network Data](https://mlsanity.com/paper/1710.07134/) (2017)

## Built on by (most cited first, 3 of 3)

- [Intrinsic User-Centric Interpretability through Global Mixture of Experts](https://mlsanity.com/paper/2402.02933/) (2024)
- [Course Recommender Systems Need to Consider the Job Market](https://mlsanity.com/paper/2404.10876/) (2024)
- [Capture-Calibrate-Coach: A Graph-Based Framework for Knowledge Monitoring Estimation and Adaptive Feedback](https://mlsanity.com/paper/2605.25419/) (2026)

## Abstract (arXiv)

The increasing availability of Massive Open Online Courses (MOOCs) has created a necessity for personalized course recommendation systems. These systems often combine neural networks with Knowledge Graphs (KGs) to achieve richer representations of learners and courses. While these enriched representations allow more accurate and personalized recommendations, explainability remains a significant challenge which is especially problematic for certain domains with significant impact such as education and online learning. Recently, a novel class of recommender systems that uses reinforcement learning and graph reasoning over KGs has been proposed to generate explainable recommendations in the form of paths over a KG. Despite their accuracy and interpretability on e-commerce datasets, these approaches have scarcely been applied to the educational domain and their use in practice has not been studied. In this work, we propose an explainable recommendation system for MOOCs that uses graph reasoning. To validate the practical implications of our approach, we conducted a user study examining user perceptions of our new explainable recommendations. We demonstrate the generalizability of our approach by conducting experiments on two educational datasets: COCO and Xuetang.