An overview of using analytics approach to predict internet usage and student performance in education: a proposed prescriptive analytic approach

• Main Authors: Khamis, Shakiroh, Ahmad, Azizah, Muraina, Ishola Dada
• Format: Article
• Language: English
• Published: 2018
• Subjects: LB2300 Higher Education
• Online Access: http://repo.uum.edu.my/26791/1/IJEPC%203%2012%202018%201%207.pdf; http://repo.uum.edu.my/26791/; http://www.ijepc.com/archived.asm

For many years, Institution of higher education have been concerned about the quality of education and use different means to analyze and improve the understanding of student success, retention and achievement. Data mining plays an important role in the business world and it helps to the educational institution to predict and make decisions related to the students’ academic status. While Big Data analysis has become a keyword in recent years, now prescriptive analytics has taken place in the evolution of data analysis in higher education after the descriptive and predictive. This research focuses on these smarter analytics allow educational decision-makers to detect patterns that exist within the masses of data, project potential outcomes and make intelligent decisions based on those projections. The objective of this paper is to examine the analytics approach by describing the different academic analytics and providing examples of various applications. The paper discusses different definitions of academic analytics to analyze Internet usage and student performance. We propose a Prescriptive Visualization model using the prescriptive analytic approach. The paper will provide a broad overview of big data analytics for researchers and practitioners.