Developing and Assessing E-Learning Techniques for Teaching Forecasting

Abstract

In the modern business environment, managers are increasingly required to perform decision making and evaluate related risks based on quantitative information in the face of uncertainty, which in turn increases demand for business professionals with sound skills and hands-on experience with statistical data analysis. Computer-based training technologies allow the new cadre of business professionals to obtain such a hands-on experience in an environment where mistakes can be made and outcomes can be measured. The authors discuss their experiences in developing a new e-learning tool designed to apply methodological forecasting concepts to real-life business and finance problems through an interactive self-learning and self-assessing module of online case studies.

Keywords:

• business statistics
• decision making and analysis
• e-learning
• forecasting
• interactive learning
• technology in education

ACKNOWLEDGMENTS

We would like to thank Paul Kates from the Centre for Teaching Excellence (CTE) and Nancy Orvis-Korn from the Math Undergraduate Office (MUO) of the University of Waterloo for technical assistance with the learning management systems and student databases.

FUNDING

The research of Gel was supported in part by grants from the Social Sciences and Humanities Research Council of Canada (SSHRC) and the Learning Initiatives Fund (LIF) of the University of Waterloo, and O’Hara Hines was supported in part by a grant from the National Science and Engineering Research Council of Canada.

Notes

1. While the developed e-learning tool, including the case studies and interactive self-assessment options, is a property of the University of Waterloo, and, hence, is not publicly available, we are happy to discuss implementation of the proposed e-learning activities upon request.

2. The complete case studies grade is based on answers to the online questions.

3. We also have information on the grade point average over the single previous term (TGPA). However, the overall GPA and TGPA are highly correlated, and so we omit TGPA from further analysis. Defaced data with all available independent and dependent variables (i.e., the final exam performance, case study grades, GPA, TGPA, major, year of study, and gender, are available from authors upon request.