Research

Research Philosophy

My research bridges programming, economics, and applied AI to address real-world business and educational challenges. I take an interdisciplinary, practice-informed approach—combining academic rigor with hands-on problem solving. I am particularly drawn to research that connects technical tools (such as Python, web development, or machine learning) with strategic business and economic decision-making.

Research Areas & Interests

• Econometrics and Volatility Forecasting

• Applied AI in Business & Education

• Programming Education and Pedagogical Innovation

• Operations Management and Decision Support Systems

Publications

1. Wong, A. and Jeganathan, S. (2020). ‘Factors that influence e-learning adoption by international students in Canada’, International Journal of Management in Education, Vol. 14, No. 5, pp. 453–470.

2. Jeganathan, S., Dhameeth, S., Perera, N.K., Perera, N. (2005). *Distribution Management*. M&N Solutions, Sri Lanka (ISBN 955-1244-6-0).

3. In Progress: Impact of US News-based Uncertainty on Canadian Stock Volatility

Ongoing Research Projects

• Forecasting Financial Volatility using Python and GARCH-MIDAS

• Gamified Microeconomics using Web Applications

• AI Tutor Prototype for Teaching JavaScript Fundamentals

• Developing Data-Driven Dashboards for Classroom Performance Monitoring

Open to Collaborations

I am currently seeking opportunities to collaborate on research projects that merge programming, data analytics, and business systems. If you're interested in interdisciplinary work or co-developing applied academic tools or case studies, feel free to connect!