Title of the paper: "Community Detection for Personalized Learning Pathway Recommendations on IT E-Learning System"
Paper Link: https://link.springer.com/chapter/10.1007/978-981-99-8296-7_45
Students involved:
- Trần Mẫn Quân – 19520873 – HTLC2019.1 – Lead Author.
- Đặng Nguyễn Phước An – 19521171 – HTLC2019.1– Co-author.
- Nguyễn Minh Nhựt – 220104018 – Class K17.2 – Co-author.
Supervisor: Assoc. Prof. Nguyễn Đình Thuân
Paper Summary:
The COVID-19 crisis has emerged as a "disruptive force with constructive implications" for the field of education, causing disruptions to traditional classroom teaching while simultaneously elevating the role of technology in learning. With the rapid development of technology, self-learning has become a popular learning method, especially in the field of Information Technology, helping learners enhance knowledge and skills to meet market demands. E-Learning systems have consequently evolved into effective and promising self-learning tools. However, due to the predominantly fragmented nature of online learning content, the challenge lies in how to systematically and efficiently acquire in-depth knowledge in a specific field within E-learning. Among the hundreds of courses available on E-Learning websites, self-learners need a specific learning path and the most suitable courses tailored to individual needs and abilities.
This research simulates a basic E-Learning system, named EduPath, and introduces enhancements to the system. The study focuses on exploring Graph Neural Network (GNN) methods and social network mining algorithms, combining these models to construct a recommendation system integrated into the E-Learning system. The system gathers user information through a Chatbot survey to provide personalized learning paths and suggest highly relevant courses for each user. Additionally, the Chatbot supports users by providing suggestions on job positions, employment opportunities, and corresponding salary levels.
The 10th International Conference on Future Data and Security Engineering (FDSE) is a leading forum designed for researchers and students interested in advanced and practical activities related to data, information, knowledge, and security engineering. The conference aims to explore advanced ideas, present and exchange research results, as well as discuss new issues related to data, information, knowledge, and security engineering. At FDSE, researchers and students can share research solutions for current social issues in technical security and data, while also identifying new issues and directions for future research and development.
FDSE Proceedings are indexed in Scopus, EI Compendex, DBLP, and listed in the Conference Proceeding Citation Index (CPCI) by Thomson Reuters.
Further information: https://www.facebook.com/UIT.Fanpage/posts/pfbid0HFXSNg94gQNKQcKbR7rLBh6HnfD1o7X94Sy6RTWY4PCZKiDeScmojKBrpKGJy6Wel
Written by: Hai Bang
Translated by: Ngoc Diem
