MOOCDR-VSI: A MOOC Resource Dynamic Recommendation Model Fusing Video Subtitle Information

Wu Shuixiu; Luo Xianzeng; Zhong Maosheng; Wu Ruping; Luo Wei

doi:10.7544/issn1000-1239.202220652

Journal of Computer Research and Development > 2024 > 61(2): 470-480. > DOI: 10.7544/issn1000-1239.202220652 CSTR: 32373.14.issn1000-1239.202220652

Wu Shuixiu, Luo Xianzeng, Zhong Maosheng, Wu Ruping, Luo Wei. MOOCDR-VSI: A MOOC Resource Dynamic Recommendation Model Fusing Video Subtitle Information[J]. Journal of Computer Research and Development, 2024, 61(2): 470-480. DOI: 10.7544/issn1000-1239.202220652

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MOOCDR-VSI: A MOOC Resource Dynamic Recommendation Model Fusing Video Subtitle Information

1.
School of Computer Information Engineering, Jiangxi Normal University, Nanchang 330022
2.
Jiangxi Education Digitizing Transformation Research Center (Jiangxi Normal University), Nanchang 330022

Funds: This work was supported by the National Natural Science Foundation of China (61877031) and the Science and Technology Projects in Jiangxi Provincial Department of Education（GJJ210324）.

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Author Bio:
Wu Shuixiu: born in 1975. Master，associate professor. Her current research interests include online education, information retrieval, and machine learning

Luo Xianzeng: born in 1996. Master candidate. His main research interests include machine learning, natural language processing, and knowledge tracking

Zhong Maosheng: born in 1974. PhD, professor. Senior member of CCF. His main research interests include machine learning and data mining, natural language processing, and intelligent education and software

Wu Ruping: born in 1998. Master candidate. Her main research interests include machine learning and natural language processing

Luo Wei: born in 1979. Master，lecturer. Her main research interests include online education and intelligent education
Received Date: July 23, 2022
Revised Date: April 23, 2023
Available Online: November 13, 2023

Graphical Abstract

Abstract

Abstract

Learners often have problems such as “information overload” and “information trek” when facing the vast online learning course resources. Based on learners’ learning records, it is increasingly important to recommend MOOC courses to learners that are consistent with their knowledge preferences and learning needs. Aiming at the problems that the existing MOOC recommendation methods do not make full use of the implicit information contained in MOOC videos, which are easy to form a “cocoon effect”, and it is difficult to capture the dynamic learning needs and interests of learners, a dynamic MOOC recommendation model integrating video subtitle information MOOCDR-VSI is proposed, which uses BERT as the encoder to deeply mine the semantic information of MOOC video subtitle text by integrating the multi-head attention mechanism. The network based on LSTM architecture is used to dynamically capture the changing knowledge preference state of learners with learning, introduce the attention mechanism to mine the personality information and common information between MOOC videos, and finally recommend MOOC videos with Top N recall probability combined with the knowledge preference status of learners. The performance of MOOCDR-VSI is analyzed by MOOCCube in the experimental dataset collected in the real learning scenario, and the results show that the proposed model improves the HR@5, HR@10, NDCG@5, NDCG@10 , NDCG@20 evaluation indexes by 2.35%, 2.79%, 0.69%, 2.2% and 3.32%, respectively, compared with the current most optimal method.
- MOOC recommendation,
- BERT,
- multiple attention mechanism,
- caption information,
- LSTM

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References (37)

References

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