Big Data Exploration Towards Analysing and Predicting Student’s Academic Progress in Higher Education: A Review

The Industry 4.0 revolution encompassing technologies like Artificial intelligence (AI), Big data and the Internet of Things is integrating and influencing Education, Business, Health care, and other sectors. Big data benefits business by providing enhanced decision making, offering novel perceptions, and incorporating competent processing. Big data and Industry 4.0 are also used to enhance the learner’s involvement and experience and utilization of Tutor time. University leaders are keen in using technologies to check if students get detached and are at risk. Big data helps Universities to determine the institute’s retention, progress and completion aspects relating to students. This further assists in lowering attrition by the early discovery of at-risk learners and informing teachers and administrators. This paper attempts to provide an insight of the research and steps taken by various universities or education bodies towards pooling this varied data both structured and unstructured that is present in a massive amount and can be exploited for prediction related to student’s progress and status. The review paper explores Learning Analytics technology and classification methods: Decision Tree, Artificial Neural Networks, Naïve Bayes, Bayesian Network and Multi-Linear Regression and software like data analytics using WEKA tool, Statistical Package for Social Sciences (SPSS) analysis tool, Python among others for prediction.