Contextual Course Clustering: BERT-Enhanced Text Analytics for Personalized Education
Keywords:
BERTopic Model, Course Recommendation, TF-IDF, Word2Vec, Coursera, Context-aware clustering.Abstract
In the case of online learning platforms, an efficient and effective course recommendation system that provides personalized content is one of the most significant factors in choosing a platform for learning. This research aims to develop a new paradigm incorporating the clustering approach and score-matching technique to guide students in appropriate courses based on their academic records. It uses a stacking technique, namely, bidirectional encoder representations from the transformer (BERT) for topic modeling, singular value decomposition (SVD) for collaborative filtering, term frequency-inverse document frequency (TF-IDF) and word-to-vector (Word2Vec) embeddings for clustering and similarity analysis on the NPTEL and Coursera datasets. These methods are incorporated in the proposed algorithm to recommend courses that are close in academic relatedness to the student's past performance and preferred course offers. Using average similarity metrics in actual performance evaluations shows the ability of the approach to recommend the best courses relevant to each student. The root mean square value and mean absolute error for the BERT-SVD model are 1.3267 and 1.0802, respectively. The findings highlight the potential of advanced clustering and embedding techniques in improving the accuracy and relevance of course recommendation systems in educational settings.
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M. C. Urdaneta-Ponte, A. Mendez-Zorrilla, and I. Oleagordia-Ruiz, “Recommendation Systems for Education: Systematic Review,” Electronics, vol. 10, no. 14, p. 1611, Jul. 2021. [Online]. Available: https://doi.org/10.3390/electronics10141611
H. Ko, S. Lee, Y. Park, and A. Choi, “A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields,” Electronics, vol. 11, no. 1, p. 141, Jan. 2022. [Online]. Available: https://doi.org/10.3390/electronics11010141
B. Li et al., “A personalized recommendation framework based on MOOC system integrating deep learning and big data,” Computers & Electrical Engineering, vol. 106, p. 108571, Mar. 2023. [Online]. Available: https://doi.org/10.1016/j.compeleceng.2022.108571
S. Amin, M. I. Uddin, A. A. Alarood, W. K. Mashwani, A. Alzahrani, and A. O. Alzahrani, “Smart E-Learning Framework for Personalized Adaptive Learning and Sequential Path Recommendations Using Reinforcement Learning,” IEEE Access, vol. 11, pp. 89769–89790, Jan. 2023. [Online]. Available: https://doi.org/10.1109/access.2023.3305584
G. Muzdybayeva, D. Khashimova, A. Amirzhanov, and S. Kadyrov, “A Matrix Factorization-based Collaborative Filtering Framework for Course Recommendations in Higher Education,” Jun. 2023. [Online]. Available: https://doi.org/10.1109/icecco58239.2023.10147152
K. K. Jena et al., “E-Learning Course Recommender System Using Collaborative Filtering Models,” Electronics, vol. 12, no. 1, p. 157, Dec. 2022. [Online]. Available: https://doi.org/10.3390/electronics12010157
M. Premalatha, V. Viswanathan, and L. Čepová, “Application of Semantic Analysis and LSTM-GRU in Developing a Personalized Course Recommendation System,” Applied Sciences, vol. 12, no. 21, p. 10792, Oct. 2022. [Online]. Available: https://doi.org/10.3390/app122110792
G. M. Dhananjaya, R. H. Goudar, A. Kulkarni, V. N. Rathod, and G. S. Hukkeri, “A Digital Recommendation System for Personalized Learning to Enhance Online Education: A Review,” IEEE Access, p. 1, Jan. 2024. [Online]. Available: https://doi.org/10.1109/access.2024.3369901
J. Wang, H. Xie, O. T. S. Au, D. Zou, and F. L. Wang, “Attention-Based CNN for Personalized Course Recommendations for MOOC Learners,” Aug. 2020. [Online]. Available: https://doi.org/10.1109/iset49818.2020.00047
J. Li and Z. Ye, “Course Recommendations in Online Education Based on Collaborative Filtering Recommendation Algorithm,” Complexity, vol. 2020, pp. 1–10, Dec. 2020. [Online]. Available: https://doi.org/10.1155/2020/6619249
B. Mondal, O. Patra, S. Mishra, and P. Patra, “A course recommendation system based on grades,” 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), Mar. 2020. [Online]. Available: https://doi.org/10.1109/iccsea49143.2020.9132845
Y. Lin, S. Feng, F. Lin, W. Zeng, Y. Liu, and P. Wu, “Adaptive course recommendation in MOOCs,” Knowledge-based Systems, vol. 224, p. 107085, Jul. 2021. [Online]. Available: https://doi.org/10.1016/j.knosys.2021.107085
T. Morrow, A. R. Hurson, and S. S. Sarvestani, “Algorithmic Support for Personalized Course Selection and Scheduling,” Jul. 2020. [Online]. Available: https://doi.org/10.1109/compsac48688.2020.00027
D. B. Guruge, R. Kadel, and S. J. Halder, “The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research,” Data, vol. 6, no. 2, p. 18, Feb. 2021. [Online]. Available: https://doi.org/10.3390/data6020018
Q. Li and J. Kim, “A Deep Learning-Based Course Recommender System for Sustainable Development in Education,” Applied Sciences, vol. 11, no. 19, p. 8993, Sep. 2021. [Online]. Available: https://doi.org/10.3390/app11198993
V. A. Nguyen, H.-H. Nguyen, D.-L. Nguyen, and M.-D. Le, “A course recommendation model for students based on learning outcome,” Education and Information Technologies, vol. 26, no. 5, pp. 5389–5415, Apr. 2021. [Online]. Available: https://doi.org/10.1007/s10639-021-10524-0
H. Wang and W. Fu, “Personalized Learning Resource Recommendation Method Based on Dynamic Collaborative Filtering,” Journal on Special Topics in Mobile Networks and Applications/Mobile Networks and Applications, vol. 26, no. 1, pp. 473–487, Oct. 2020. [Online]. Available: https://doi.org/10.1007/s11036-020-01673-6
W.-E. Kong, S.-C. Haw, N. Palanichamy, and S. H. A. Rahman, “An e-Learning Recommendation System Framework,” International Journal on Advanced Science, Engineering and Information Technology/International Journal of Advanced Science, Engineering and Information Technology, vol. 14, no. 1, pp. 10–19, Feb. 2024. [Online]. Available: https://doi.org/10.18517/ijaseit.14.1.19043
M. Liang and T. Niu, “Research on Text Classification Techniques Based on Improved TF-IDF Algorithm and LSTM Inputs,” Procedia Computer Science, vol. 208, pp. 460–470, Jan. 2022. [Online]. Available: https://doi.org/10.1016/j.procs.2022.10.064
R. Wang and Y. Shi, “Research on application of article recommendation algorithm based on Word2Vec and Tfidf,” 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), Feb. 2022. [Online]. Available: https://doi.org/10.1109/eebda53927.2022.9744824
S.-T. Zhong, L. Huang, C.-D. Wang, and J.-H. Lai, “Constrained Matrix Factorization for Course Score Prediction,” Nov. 2019. [Online]. Available: https://doi.org/10.1109/icdm.2019.00199
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