Measuring active learning to predict course quality

This study investigated whether active learning within computer-based training courses can be measured and whether it serves as a predictor of learner-perceived course quality. A major corporation participated in this research, providing access to internal employee training courses, training representatives, and historical course evaluation data. Twenty sample courses were subdivided into 1,884 time-based instructional events and categorized by eight design principles for learner engagement: attend, organize, recall, practice, interact, apply, explore, and absorb. This analysis produces a quantitative pattern for the cognitive activity a course encourages within the learner, summarized as the active learning index. A regression model, with the active learning index as a predictor variable for learner-perceived course quality, resulted in a correlation coefficient of .83 (r2=.69) and a p-value <.0001. These results suggest a framework for quantifying the active learning components in computer-based training courses and guiding the work of instructional designers toward higher-quality courses.