An application of participatory action research in advising-focused learning analytics

Abstract

Advisors assist students in developing successful course pathways through the curriculum. The purpose of this project is to augment advisor institutional and tacit knowledge with knowledge from predictive algorithms (i.e., Matrix Factorization and Classifiers) specifically developed to identify risk. We use a participatory action research approach that directly involves key members from both advising and research communities in the assessment and provisioning of information from the predictive analytics. The knowledge gained from predictive algorithms is evaluated using a mixed method approach. We first compare the predictive evaluations with advisors evaluations of student performance in courses and actual outcomes in those courses We next expose and classify advisor knowledge of student risk and identify ways to enhance the value of the prediction model. The results highlight the contribution that this collaborative approach can give to the constructive integration of Learning Analytics in higher education settings.