Big data analytics framework for effective higher education Institutions

Abstract

There has been an increased dependency on Information and Communication Technologies (ICTs) in undertaking various activities in Higher Education Institutions (HEIs) ecosystems. Because of that, huge volumes of data have increasingly been generated. There have been, for instance, considerable amounts of data generated through electronic platforms involved in students’ admission and registration process, students’ academic records management, teaching and learning data, curriculum related data, and several other administrative data. Analysis of data generated from these platforms stands to give students, lecturers, HEIs Management, policy makers and implementers, and other stakeholder’s useful insights that would help in improving HEIs’ effectiveness. Unfortunately, literature has identified several challenges associated with existing big data analytics frameworks in HEIs. It was on this line that the present study, which was based on desk research, was carried out to propose an effective big data framework for analytics of such data. The proposed framework is composed of five stages; data collection, data pre-processing, data storage, data analytics, and data visualization. The stages were arranged systematically to address the identified challenges in the existing frameworks. Effective implementation of this framework will help HEIs to make a productive use of various data they generate. This will ultimately be beneficial to not only HEIs but also to aspired students, labor market, the government and the public at large.