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Improving Online Course Performance Through Customization: An Empirical Study Using Business Analytics
Abstract
The number of educational courses offered online is growing, with students often having no choice for alternative formats. However, personal characteristics may affect online academic performance. In this study, the authors apply two business analytics methods - multiple linear/polynomial regression and generalized additive modeling (GAM) - to predict online student performance based on six personal characteristics. These characteristics are: communication aptitude, desire to learn, escapism, hours studied, gender, and English as a Second Language. Survey data from 168 students were partitioned into training/validation sets and the best fit models from the training data were tested on the validation data. While the regression method outdid the GAM at predicting student performance overall, the GAM explained the performance behavior better over various predictor intervals using natural splines. The study confirms the usefulness of business analytics methods and presents implications for college administrators and faculty to optimize individual student online learning.
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