What is Log(file) Analysis

Undergraduates who engage in high structure, high enrollment, active learning courses tend to perform best when they self-regulate their learning. Student self-regulated learning is encouraged by active learning designs that provide resources used to enact strategies and engage in planning, monitoring, and evaluation of one's learning processes. Some learners may need to “learn to learn” before these resources can be leveraged effectively. This chapter documents a scalable co-design and learning analytics project led by learning scientists and science, technology, engineering, and math (STEM) instructors who enhanced digital resources of large-lecture courses' learning management systems (LMSs). Students' course engagement produced data about self-regulated learning processes, affording feature engineering, prediction modeling and provision of timely support to students predicted to struggle. The authors report initial results through the lens of self-regulated learning theory and elaborate cases demonstrating institutionalization and replicability to research and regional institutions.