Evaluation of an Adaptive Learning Technology as a Predictor of Student Performance in Undergraduate Biology

With increased use of educational technologies comes the need to not only evaluate whether or not these technologies are effective, but also how instructors can utilize these technologies to adapt teaching practices for maximized student performance on formal assessments. This study examines four specific aspects of LearnSmart™, an adaptive learning technology developed by McGraw-Hill Higher Education, and the potential effects these aspects might have on student assessment performance. With a focus on data from a module on cellular respiration, this study examines relationships between LearnSmart use and student quiz and exam scores. The results indicate statistically significant relationships when the module student score, module completion, total time spent on all LearnSmart™ exercises, and total average percent completion are used as predictors for exam score. Though other trends existed, most LearnSmart™ data is not a statistically significant predictor of assessment performance on a group level. Overall, however, all LearnSmart™ data can provide a useful tool for student self-reflection and for one-on-one interactions between instructor and student, including advising. Finally, in conjunction with data gathered from an optional LearnSmart™ student usage survey, and experience teaching and learning with LearnSmart™, the study concludes with best practices for instructors.