The Society for Learning Analytics Research (SoLAR, 2011) defines learning analytics as the “measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.”
The School of Mathematics & Science regularly carries out learning analytics projects with the aim of improving student learning.
Identifying Students At-Risk for Failure
One ongoing project uses machine learning to identify students at-risk for failure. Patterns discovered in academic results, attendance records, and other data are used to flag at-risk students so that an intervention can take place early in the semester. Examples of intervention include peer tutoring, and additional coaching by lecturers.