Predictors and early warning systems in higher education: a systematic literature review

Abstract

The topic of predictive algorithms is often regarded among the most relevant fields of study within the data analytics discipline. Nowadays, these algorithms are widely used by entrepreneurs and researchers alike, having practical applications in a broad variety of contexts, such as in finance, marketing or healthcare. One of such contexts is the educational field, where the development and implementation of learning technologies led to the birth and popularization of computerbased and blended learning. Consequently, student-related data has become easier to collect. This Research Full Paper presents a literature review on predictive algorithms applied to higher education contexts, with special attention to early warning systems (EWS): tools that are typically used to analyze future risks such as a student failing or dropping a course, and that are able to send alerts to instructors or students themselves before these events can happen. Results of using predictors and EWS in real academic scenarios are also highlighted.