Predictive Modelling in Learning Analytics: A Machine Learning Approach in R

Abstract

AbstractPrediction of learners’ course performance has been a central theme in learning analytics (LA) since the inception of the field. The main motivation for such predictions has been to identify learners who are at risk of low achievement so that they could be offered timely support based on intervention strategies derived from analysis of learners’ data. To predict student success, numerous indicators, from varying data sources, have been examined and reported in the literature. Likewise, a variety of predictive algorithms have been used. The objective of this chapter is to introduce the reader to predictive modelling in LA, through a review of the main objectives, indicators, and algorithms that have been operationalized in previous works as well as a step-by-step tutorial of how to perform predictive modelling in LA using R. The tutorial demonstrates how to predict student success using learning traces originating from a learning management system, guiding the reader through all the required steps from the data preparation all to the evaluation of the built models.