Dr. STANLEY MUNGA NGIGI
Lecturer, Computer Science, Artificial Intelligence & Data Science
stanley.munga@zetech.ac.ke
Dr. Stanley Munga is a Computer Science and Information Technology academic with over seven years of university teaching and academic experience. His academic and professional interests span Artificial Intelligence, Machine Learning, Data Science, Predictive Analytics, Software Engineering, Cybersecurity, Information Systems, and Programming.
His academic experience includes teaching, research supervision, curriculum development, assessment and moderation, quality assurance, student mentorship, and the integration of technology into teaching and learning. He has taught and supported programmes in Computer Science, Information Technology, Data Science, Software Engineering, and Information Systems.
- PhD in Information Technology from Kirinyaga University, with research focused on An Ensemble Model for Predicting Learner Attrition in Online Learning.
- MSc in Information Systems Management from KCA University
- Bachelor of Business Information Technology from Mount Kenya University.
PROFESSIONAL CERTIFICATIONS & MEMBERSHIP
-
Huawei Certified Academy Instructor (HCAI) – Huawei ICT Academy
-
Huawei Certified ICT Associate (HCIA) – Internet of Things
-
Huawei Certified ICT Associate (HCIA) – Big Data
-
Foundations of Project Management – Coursera
-
Google Data Analytics Professional Training – Coursera
-
Member, Computer Society of Kenya
-
Oracle Academy Member
- Tutorial Fellow – Zetech University January 2019 – Present
- Adjunct Faculty – African Leadership College, Mauritius April 2024 – September 2024
- External Moderator – BSc (Hons) Software Engineering, African Leadership College, Rwanda August 2024 – December 2025
- Software Development Trainer – Power Learn Project June 2022 – December 2024
- Lecturer – Computer Science, Nairobi Institute of Business Studies (NIBS) January 2017 – December 2018
Journals
His research interests and scholarly contributions focus primarily on:
-
Artificial Intelligence and Machine Learning
-
Educational Data Mining
-
Predictive Analytics
-
Learner Attrition and Student Success Prediction
-
Ensemble Learning
-
Cybersecurity
-
Data Science and Intelligent Systems
He has published peer-reviewed research in areas related to Artificial Intelligence, Machine Learning, Educational Data Mining, and Cybersecurity.
NON-PEER-REVIEWED RESEARCH PAPERS
His non-peer-reviewed research and academic working papers focus on the application of Artificial Intelligence, Machine Learning, predictive analytics, educational data mining, and intelligent systems to address challenges in education and technology.
His current research interests include AI-driven educational analytics, early prediction of learner attrition, responsible use of Artificial Intelligence in education, machine learning applications, and the development of intelligent systems for decision support.
conference papers
- Gradient Boosting–Neural Network Hybrids for Early Detection of Dropout Risk in Online Learning Environments Kirinyaga University Research Conference — March 2026.
- Leveraging Ensemble Models for Predicting Learner Attrition in Online Learning Annual Conference on Emerging Technologies, St. Paul's University — October 2025.
- Leveraging Ensemble Models for Predicting Learner Attrition in Online Learning Bomet University College Research Conference — April 2026.
- Hybrid Ensemble Learning Framework for Early Prediction of Learner Attrition in Online Education Zetech University Research Conference — June 2026.