Biography
Chong Ke Ting is a Computer Science academic and researcher specializing in Artificial Intelligence, Machine Learning, Data Analytics, and Educational Data Mining, with experience in both higher education and industry. She holds a PhD in Computer Science and a Bachelor of Computer Science (Bioinformatics). Her research focuses on developing reliable, interpretable, and practical AI solutions for real-world applications, with her doctoral research focusing on student engagement prediction in online learning. Her current research interests are expanding toward Artificial Intelligence in biomedical and healthcare applications, particularly the use of machine learning, deep learning, explainable AI, and data-driven approaches for biomedical prediction and early detection. She also has practical experience in AI engineering, machine learning pipeline development, and dashboard analytics, complemented by teaching experience in Artificial Intelligence, Statistical Learning, Python Programming, Business Analytics, and Information Technology. She is passionate about integrating research, teaching, and emerging AI technologies to develop trustworthy and impactful intelligent systems.
Research Interest
Artificial Intelligence, Machine Learning, Deep Learning, Explainable Artificial Intelligence (XAI), Biomedical and Healthcare AI, Educational Data Mining, Feature Selection, Class Imbalance handling.
Educational Background
PhD (Computer Science), Faculty of Computing, Universiti Teknologi Malaysia (2026)
BSc (Computer Science in Bioinformatics), Universiti Teknologi Malaysia (2021)
Working Experience
- Teaching Assistant – Universiti Teknologi Malaysia (UTM)
- Part-time Lecturer – Tunku Abdul Rahman University of Management and Technology (TARUMT)
Publications
| Journal Name | Indexed |
Genetic Algorithm Ensemble Filter Methods on Kidney Disease Classification | International Journal of Innovative Computing | MyCITE |
Leveraging Expert Insight to Define Multifaceted Student Engagement Levels in Higher Education Online Learning Using a Belief Rule-Based Framework | Innovative Teaching and Learning Journal | MyCITE |
Multidimensional Context Clustering to Analyse Student Engagement in Online Learning Environment | International Journal of Innovative Computing | MyCITE |
A Systematic Review Of Machine Learning Techniques For Predicting Student Engagement In Higher Education Online Learning | Journal of Information Technology Education: Research | WoS (Q2) |
Multiclass Student Engagement Level Prediction using Belief-Rule Based Labelling | 2023 Sixth International Conference of Women in Data Science at Prince Sultan University | Scopus |
A Class Density-Weighted Gain Ratio Feature Selection for Multiclass Student Engagement Classification | International Journal of Academic Research in Progressive Education and Development | ERA |
Honors / Awards
| Conference/Competition Name | Award |
Student Engagement Level During Emergency Remote Learning: K-Means Clustering | Sustainable & Integrated Engineering International Conference 2022 | Best Paper Award |
Multifaceted Student Engagement Level Prediction with An Equitable Feature Selection Approach | Eduinnovation 2023 | Gold Award |
A Class Density-Weighted Gain Ratio Feature Selection for Multiclass Student Engagement Prediction | 4th International Conference On Creative and Innovative Technology Education 2025 | Best Paper Award |