A big data exploratory for pattern and forecasting postgraduate student analysis in a Malaysian University

• Published in: AIP Conference Proceedings
• Main Author: Bin Mamat A.M.I.; Bin Anuar F.I.; Abdul Khalil K.B.
• Format: Conference paper
• Language: English
• Published: American Institute of Physics 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203156667&doi=10.1063%2f5.0223855&partnerID=40&md5=935387ff7a890b0be0dee8924c44d4fa

A sustainable strategic plan in higher education institutions (HEIs) requires inputs and facts that can be obtained from analyzing the big data. HEIs collect numerous data from students every enrollment, creating big data. However, big data should be given more attention. This study aimed to explore the big data of the postgraduate students in a Malaysian University, namely University Teknologi MARA (UiTM), analyze data characteristics, patterns, and anomalies, and forecast future enrollment models using the Exploratory Data Analysis (EDA) technique. The data were extracted from the university academic database, Student Information Management Systems (SIMS). Several parameters were set in the Structured Query Language (SQL) to mine the data and exported to the Excel software for data post-processing and analysis. The single parameter fitted polynomial regression method was used to develop a forecasting model for postgraduate enrolment from 2022 to 2030. In this paper, we presented the data characteristics and pattern of the postgraduate student demographic and discussed the summary of the postgraduate students' achievements in depth. The developed enrolment forecast modeling shows that the university will have approximately 50,000 postgraduate students enrolment in 2030. Thus, the university must have a strategic plan to identify the future research area, acquire suitable expertise, and upgrade the supporting facilities to accommodate future needs. © 2024 Author(s).