CHƯƠNG 7 KẾT LUẬN VÀ HƯỚNG NGHIÊN CỨU TIẾP THEO
7.2. Hướng nghiên cứu tiếp theo
Ngồi các kết quả đạt được như trên, luận án cịn một số hạn chế nhất định cần tiếp tục nghiên cứu trong thời gian tới.
Luận án đã đề xuất hai các tiếp cận tìm kiếm tài nguyên học tập dựa trên độ tương đồng văn bản và dựa trên mạng ngữ nghĩa. Trong thời gian tới, việc nghiên cứu phương pháp đánh giá hiệu quả của mơ hình tìm kiếm dựa trên mạng ngữ nghĩa ontology là cần thiết. Ngồi ra, việc so sánh hiệu quả tìm kiếm giữa hai cách tiếp cận này cũng cần được xem xét. Một giải pháp nhằm tăng khả năng xử lý dữ liệu tìm kiếm cũng cần được nghiên cứu sâu hơn trong bối cảnh dữ liệu tìm kiếm ngày đa dạng và khơng ngừng gia tăng.
Cần triển khai thực nghiệm trên nhiều tập dữ liệu khác nhau đối với các mơ hình dự đốn xếp hạng kết quả học tập, gợi ý tài nguyên học tập để cĩ đánh giá tồn diện, khách quan hơn đối với các kỹ thuật đề xuất, đặc biệt là kỹ thuật học sâu. Ngồi ra, cần nghiên cứu bổ sung nhiều thuộc tính (đa thuộc tính) cĩ yếu tố thời gian, chọn lọc các thuộc tính cĩ ảnh hưởng tính cực đến kết quả dự đốn để cải thiện hiệu quả của mơ hình học sâu.
Trong luận án này, các mơ hình tìm kiếm, dự đốn và gợi ý tập trung ở tài nguyên học tập dạng văn bản. Một vấn đề cĩ thể nghiên cứu trong thời gian tới là nghiên cứu cải tiến các mơ hình này cho dạng tài nguyên học tập khác, chẳng hạn video.
Do các nghiên cứu của luận án được thực hiện song song, thực nghiệm trên nhiều tập dữ liệu, vì vậy trong thời gian tới cần tạo sự gắn kết các nghiên cứu này một cách cĩ hệ thống hơn. Ngồi ra, cĩ thể nghiên cứu tích hợp các mơ hình tìm kiếm, dự đốn xếp hạng và gợi ý tài nguyên học tập thành một hệ thống quản lý tài nguyên học tập để áp dụng vào trường hợp thực tiễn của cơ sở giáo dục, đặc biệt là giáo dục đại học.
CÁC CƠNG TRÌNH ĐÃ CƠNG BỐ
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Advances in Science, Technology and Engineering Systems Journal, Vol. 5, No. 4,
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Computer Science. Springer Nature. Vol. 11814, pp. 722-730. E-ISSN: 1611-3349,
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CT4. Tran Thanh Dien, Le Van Trung, Nguyen Thai-Nghe, 2020. An approach for semantic-based searching in learning resources. The 12th IEEE International Conference on Knowledge and Systems Engineering (KSE 2020), pp 183-188.
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CT5. Trần Thanh Điện, Nguyễn Ngọc Tuấn, Nguyễn Thanh Hải, Nguyễn Thái Nghe, 2020. Tăng tốc tìm kiếm tài nguyên học tập theo nội dung bằng kỹ thuật xử lý dữ liệu lớn. Kỷ yếu Hội thảo khoa học Quốc Gia lần thứ 9: Cơng nghệ Thơng
tin và Ứng dụng trong các lĩnh vực (CITA 2020). Trang 171-178. ISBN: 978-
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CT6. Tran Thanh Dien, Luu Hoai Sang, Thanh Hai Nguyen, Nguyen Thai-Nghe, 2020. Deep Learning with Data Transformation and Factor Analysis for Student Performance Prediction. International Journal of Advanced Computer Science and
Applications (IJACSA), Vol. 11, No. 8, pp. 711-721. E-ISSN: 2156-5570, P-ISSN:
2158-107X. DOI: 10.14569/IJACSA.2020.0110886 (Scopus Q3; ESCI)
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CT8. Tran Thanh Dien, Le Duy-Anh, Nguyen Hong-Phat, Nguyen Van-Tuan, Trinh Thanh-Chanh, Le Minh-Bang, Nguyen Thanh-Hai, and Nguyen Thai-Nghe,
2021. Four Grade Levels-based Models with Random Forest for Student Performance Prediction at a Multidisciplinary University. The 15th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS- 2021). Lecture Notes in Networks and Systems. Springer Nature. Vol. 278, pp. 1- 12. E-ISSN: 2367-3389, P-ISSN: 2367-3370. DOI: 10.1007/978-3-030-79725- 6_1 (Scopus Q4)
CT9. Tran Thanh Dien, Pham Huu Phuoc, Nguyen Thanh-Hai, Nguyen Thai-Nghe, 2021. Personalized student performance prediction using multivariate long short-term memory. The 8th International Conference on Future Data and Security Engineering (FDSE 2021). Communications in Computer and
Information Science. Springer Nature. Vol. 1500, pp. 238-247. E-ISSN: 1865-
0937, P-ISSN: 1865-0929. DOI: 10.1007/978-981-16-8062-5_16 (Scopus Q4) CT10. Tran Thanh Dien, Nguyen Thanh-Hai and Nguyen Thai-Nghe, 2021. Deep Matrix
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