USING NEURAL COLLABORATIVE FILTERING TO PERSONALIZE ONLINE LEARNING CONTENT
Keywords:
Neural Collaborative Filtering, online learning contentAbstract
Personalization of learning is a strategy used to determine the characteristics of learners so they can learn effectively. There are many approaches that can be taken to personalize learning. This system for personalizing learning content in the form of recommendations for online learning content was built using the Neural Collaborative Filtering method and utilizes a collection of implicit feedback data taken from student activity records when interacting with online learning content as reference data to produce recommendations. The design of a learning content personalization system in the form of recommendations on online learning content for students using the Neural Collaborative Filtering method has been successfully built and can run well in online learning content. The literature study approach was used to conduct the research. Data and relevant information were gathered through a review of the literature using Neural Collaborative Filtering to personalize online learning content. This research discusses traditional methods in personalizing online learning content, Neural Collaborative Filtering, relevant previous research, and the implementation of Neural Collaborative Filtering in online learning content.
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A. Qaffas, A., Kaabi, K., Shadiev, R., & Essalmi, F. (2020). Towards an optimal personalization strategy in MOOCs. Smart Learning Environments, 7(1), 14.
Andakke I. Pengembangan Sistem Informasi Bank Judul Skripsi Terintegrasi Sistem Pengajuan Judul Pada Jurusan Pendidikan Teknik Elektro Fakultas Teknik Universitas Negeri Makassar (Doctoral dissertation, Universitas Negeri Makassar).
Bai, T., Wen, J. R., Zhang, J., & Zhao, W. X. (2017, November). A neural collaborative filtering model with interaction-based neighborhood. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 1979-1982).
Blazheska-Tabakovska, N., Ivanovic, M., Klasnja-Milicevic, A., & Ivkovic, J. (2017). Comparison of E-learning personalization systems: Protus and PLeMSys. International Journal of Emerging Technologies in Learning (iJET), 12(1), 57-70.
Chen, W., Cai, F., Chen, H., & Rijke, M. D. (2019). Joint neural collaborative filtering for recommender systems. ACM Transactions on Information Systems (TOIS), 37(4), 1-30.
Curtin, A. L., & Sarju, J. P. (2021). Students as partners: Co-creation of online learning to deliver high quality, personalized content. In Advances in Online Chemistry Education (pp. 135-163). American Chemical Society.
Er-Radi, H., Aammou, S., & Jdidou, A. (2023). PERSONALIZED LEARNING THROUGH ADAPTIVE CONTENT MODIFICATION: EXPLORING THE IMPACT OF CONTENT DIFFICULTY ADJUSTMENT ON LEARNER PERFORMANCE. Conhecimento & Diversidade, 15(39), 263-275.
Garrido, A., & Morales, L. (2014). E-learning and intelligent planning: Improving content personalization. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 9(1), 1-7.
Ghazali, A. S. M., Noor, S. F. M., & Saad, S. (2015, August). Review of personalized learning approaches and methods in e-learning environment. In 2015 International Conference on Electrical Engineering and Informatics (ICEEI) (pp. 624-627). IEEE.
Guo, Y., & Yan, Z. (2020). Recommended system: attentive neural collaborative filtering. IEEE access, 8, 125953-125960.
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017, April). Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web (pp. 173-182).
He, X., Du, X., Wang, X., Tian, F., Tang, J., & Chua, T. S. (2018). Outer product-based neural collaborative filtering. arXiv preprint arXiv:1808.03912.
Lee, D. H., Cho, S. H., & Kim, Y. (2018). A design and development of the learning contents management based on the personalized online learning. International Journal on Advanced Science, Engineering and Information Technology, 8(4), 1321-1326.
Ling, G., Yang, H., King, I., & Lyu, M. R. (2012, June). Online learning for collaborative filtering. In The 2012 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
Mawanta I, Gunawan TS, Wanayumini W. Uji Kemiripan Kalimat Judul Tugas Akhir dengan Metode Cosine Similarity dan Pembobotan TFIDF. JURNAL MEDIA INFORMATIKA BUDIDARMA. 2021 Apr 25;5(2):726-38.
Mirzaqon. T, A dan Budi Purwoko. (2017). Studi Kepustakaan Mengenai Landasan Teori dan Praktik Konseling Expressive Writing. Jurnal BK Unesa, 8 (1).
Alfian Fathurrahman., Kusuma Ayu L., Dawam D. (2022). Penerapan Metode Collaborative Filtering untuk Personalized Learning Content pada Learning Management System (LMS). JURIKOM (Jurnal Riset Komputer), Vol. 9 No. 2.
Murad, D. F., Heryadi, Y., Isa, S. M., & Budiharto, W. (2020). Personalization of study material based on predicted final grades using multi-criteria user-collaborative filtering recommender system. Education and Information Technologies, 25(6), 5655-5668.
Pratama NP, Mustaqiem M, Minarni M. Pengembangan Sistem Informasi Pengelolaan Judul Skripsi dan Tugas Akhir dengan Fitur Deteksi Kemiripan Menggunakan Algoritma Winnowing. TIN: Terapan Informatika Nusantara. 2021 Oct 15;2(5):271-8.
Rendle, S., Krichene, W., Zhang, L., & Anderson, J. (2020, September). Neural collaborative filtering vs. matrix factorization revisited. In Proceedings of the 14th ACM Conference on Recommender Systems (pp. 240-248).
Taylor, D. L., Yeung, M., & Bashet, A. Z. (2021). Personalized and adaptive learning. Innovative learning environments in STEM higher education: Opportunities, Challenges, and Looking Forward, 17-34.
Wang, X., He, X., Wang, M., Feng, F., & Chua, T. S. (2019, July). Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval (pp. 165-174).
Zou, L., Xia, L., Gu, Y., Zhao, X., Liu, W., Huang, J. X., & Yin, D. (2020, July). Neural interactive collaborative filtering. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 749-758).
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