Students' performance tracking in distributed open education using big data analytics

Abstract

The field of Big Data Analytics (BDA) is advancing rapidly, and it is finding adoption in diverse areas such as Health, Commerce, Logistics, Retail and Manufacturing to name a few. Adoption of BDA techniques in the field of Higher Education is new, and it is steadily increasing. In this work, BDA techniques have been applied to track the Key Academic Performance Indicators (KAPIs) related to students at the Arab Open University (AOU) and to support the corresponding decisions in this regard. Since the AOU is a Pan Arab multi-campus distributed institution operating in 8 countries and makes extensive use of a wide range of cloud based applications to manage the students' life cycle, hence it is an ideal candidate for adoption of BDA techniques to track students' KAPIs across the AOU multiple country campuses. In order to achieve this objective, we have used IBM Watson Analytics (WA) platform to track the students' KAPIs. As a pilot project, we have focused in this work on the Information Technology and Computing (ITC) academic programme across the AOU. The Exploration and Business Intelligence BDA capabilities of WA have enabled us to analyze and track the academic KAPIs of the ITC students across AOU country campuses while the Predictive Analytics (PA) has led to identifying the dominant factors behind some of our problems such as students drop out rates. One of the most promising outcomes is the decision support dashboards such as the one related to the Student Risk Factor (SRF). By identifying At Risk Students, such dashboard can act as an Early Alert System to enable the AOU management to take corrective action to provide needed support to such students.