Assistant Professor

Bio

Dr. Edmund Evangelista is an Assistant Professor at the College of Technological Innovation at Zayed University (Abu Dhabi Campus). He received his Ph.D. in Information Technology from Saint Paul University Philippines. His primary field of research is Machine Learning and Data Mining. Before joining Zayed University, Dr. Edmund taught at various universities in the  Philippines such as the University of St. Louis, St. Mary’s University, and Cagayan State University. 

In addition to the academic experience, he worked as Software Engineer at Ibri College of Technology – Oman for 7 years and as Web/Moodle Developer for 4.5 years at Gulf University for Science and Technology – Kuwait.

Office

Abu Dhabi Campus, MF2-0-049

Phone:

+971 2 599 3611

Email:

Teaching Areas

Applied Database Systems, Management Information Systems, Programming, Web Design and Development, Systems Analysis and Design, IT Project Management

Research and Professional Activities

Research Area(s)

His research interests are in Machine Learning, Data Mining, Predictive Analytics, Software Engineering, Distributed Database Systems

Refereed journal papers

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Mustapha, S.M.F.D.S., Evangelista, E., & Marir, F (2023). Towards Designing a Knowledge Sharing System for Higher Learning Institutions in the UAE Based on the Social Feature Framework. Sustainability 2023, 15, 15990. https://doi.org/10.3390/su152215990

Evangelista, E. (2023). An Optimized Bagging Ensemble Learning Approach Using BESTrees for Predicting Students’ Performance. International Journal of Emerging Technologies in Learning (iJET), 18(10), pp. 150–165. https://doi.org/10.3991/ijet.v18i10.38115

Evangelista, E. & Sy, B. (2022). An approach for improved students’performance prediction using homogeneous and heterogeneous ensemble methods. International Journal of Electrical and Computer Engineering (IJECE)Vol.12, No.5, October 2022, pp. 5226~5235ISSN: 2088-8708. https://doi.org/10.11591/ijece.v12i5.pp5226-5235

Evangelista, E. (2021). A Hybrid Machine Learning Framework for Predicting Students’ Performance in Virtual Learning Environment. International Journal of Emerging Technologies in Learning (iJET), 16(24), pp. 255–272. https://doi.org/10.3991/ijet.v16i24.26151

Evangelista, E. (2019). Development of Machine Learning Models using Study Behavior Predictors of Students’ Academic Performance Through Moodle Logs. International Journal of Innovative Technology and Exploring Engineering (IJITEE), Volume-8, Issue-6S3, 2019, pp. 22-27.

>> Papers in refereed conference proceedings

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Evangelista, E. (2019). Development of Machine Learning Models using Study Behavior Predictors of Students’ Academic Performance Through Moodle Logs. In Proceedings of the 2019 World Conference on Applied Science Engineering and Technology, pp. 41.