ABSTRACT
The amount of data gathered, processed, and available as a result of students interacting with adaptive learning systems (ALSs), differentiates them from other forms of educational technology. However, leveraging ALS data requires technical, analytical, and other skills that educators don’t often possess or have time to develop. Therefore, while the prospect of having and using data analytics to improve teaching and learning outcomes is compelling, in practice, these potential insights remain underutilized or overwhelming to process and apply in an efficient manner. This study addresses the extent to which instructor self-efficacy impacts the use of data analytics intended to improve instructional practices. Recommendations built on our findings are also shared.
