How Good Are Large Language Models for Course Recommendation in MOOCs?
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910908981182464 |
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| author | Ma, Boxuan Khan, Md Akib Zabed Yang, Tianyuan Polyzou, Agoritsa Konomi, Shin'ichi |
| author_facet | Ma, Boxuan Khan, Md Akib Zabed Yang, Tianyuan Polyzou, Agoritsa Konomi, Shin'ichi |
| contents | Large Language Models (LLMs) have made significant strides in natural language processing and are increasingly being integrated into recommendation systems. However, their potential in educational recommendation systems has yet to be fully explored. This paper investigates the use of LLMs as a general-purpose recommendation model, leveraging their vast knowledge derived from large-scale corpora for course recommendation tasks. We explore a variety of approaches, ranging from prompt-based methods to more advanced fine-tuning techniques, and compare their performance against traditional recommendation models. Extensive experiments were conducted on a real-world MOOC dataset, evaluating using LLMs as course recommendation systems across key dimensions such as accuracy, diversity, and novelty. Our results demonstrate that LLMs can achieve good performance comparable to traditional models, highlighting their potential to enhance educational recommendation systems. These findings pave the way for further exploration and development of LLM-based approaches in the context of educational recommendations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08208 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How Good Are Large Language Models for Course Recommendation in MOOCs? Ma, Boxuan Khan, Md Akib Zabed Yang, Tianyuan Polyzou, Agoritsa Konomi, Shin'ichi Information Retrieval Artificial Intelligence Large Language Models (LLMs) have made significant strides in natural language processing and are increasingly being integrated into recommendation systems. However, their potential in educational recommendation systems has yet to be fully explored. This paper investigates the use of LLMs as a general-purpose recommendation model, leveraging their vast knowledge derived from large-scale corpora for course recommendation tasks. We explore a variety of approaches, ranging from prompt-based methods to more advanced fine-tuning techniques, and compare their performance against traditional recommendation models. Extensive experiments were conducted on a real-world MOOC dataset, evaluating using LLMs as course recommendation systems across key dimensions such as accuracy, diversity, and novelty. Our results demonstrate that LLMs can achieve good performance comparable to traditional models, highlighting their potential to enhance educational recommendation systems. These findings pave the way for further exploration and development of LLM-based approaches in the context of educational recommendations. |
| title | How Good Are Large Language Models for Course Recommendation in MOOCs? |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2504.08208 |