Toward a New Paradigm: Learning Analytics 2.0

Abstract

Innovation and advancements have led to the ability of higher education administrators and innovators to use machine learning (ML) to identify student academic risk behavior patterns at increasingly early points within a semester. These models bring with them the promise to help prioritize allocation of finite resources and inform scalable interventions to promote learner success. However, it may be more difficult to prioritize student needs when the measures for which a university is held accountable and use ML to predict are not specific to learning. How do we best navigate the ethical waters to emphasize and support student growth while simultaneously addressing business reporting needs? To begin this transformation, it’s critical that we gather better, more meaningful direct measures to build the models we use to predict outcomes, even if it means sacrificing some level of predictive validity, and then use our intervention strategies to improve these specific behavioral inputs feeding the models.