A Learning Analytics Approach to Model and Predict Learners’ Success in Digital Learning

Abstract

Learning analytics methods are widely applied in the educational field to gain insights on hidden patterns from educational data. Methods like predictive learning analytics are used to identify and measure patterns in learning data and extrapolate future behaviours. It can be used to enable the learners to be more self-aware of their learning behaviours and to enable the instructor to take appropriate actions informed by the trace of data. Thus such methods can empower learners as they progress through online training, and allows them to be self-regulated in order to solidify their learning and develop positive habits that will enhance their learning experiences. This paper reports on the use of a popular decision tree classification algorithm using behavioural features from a public domain dataset to develop a predictive model for predicting learning performance. Among the five behavioural features, we find that the measure of visited resources provides the most discriminating rules in the classifier.