An Overview on the Use of Educational Data Mining for Constructing Recommendation Systems to Mitigate Retention in Higher Education

Abstract

In higher education, many students exceed the expected time to complete their undergraduate programs. This delay is called retention, which can lead to program abandonment. STEM undergraduate programs, in particular, have higher retention and dropout rates when compared to other non-STEM programs. The students in such programs end up exchanging or dropping out the programs before graduating, causing waste in economic, social and academic terms. In this context, Recommendation Systems can be used to support students and managers in choosing disciplines, contributing for them achieving better academic performance and thus aiming to improve student learning and engagement and mitigate retention. For constructing these Recommendation Systems, Educational Data Mining techniques, including machine learning algorithms, can be used to identify and predict retention situations and contribute to reducing their occurrence. The aim of this paper is to present a Systematic Literature Review (SLR) for identifying the use of Educational Data Mining methodologies, techniques and tools to implement Recommendation Systems with a focus on preventing student retention in higher education programs. We selected studies available in digital libraries that are international references in publications of scientific articles, in order to answer the following research question: What machine learning methods were used in Recommendation Systems in the context of Educational Data Mining? Among the various studies found to reduce student retention rates, most used methods to predict student grades. We observed that there are many papers proposing the use of machine learning methods for predicting failure in disciplines, either through regressors or classifiers. However, just a few studies have proposed Recommendation Systems to assist students in choosing subjects at the time of enrollment for the next term, which indicates a large area for the development of further future work in this field.