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IJIET 2023 Vol.13(3): 439-447
doi: 10.18178/ijiet.2023.13.3.1824

Student Gross Enrolment Ratio Forecasting: A Comparative Study Using Statistical Method and Machine Learning

Jamal Hussain, David Rosangliana*, and Vanlalruata

Abstract—Educational data mining has advanced substantially within the past decade. These mining strategies lay out a plan for increasing overall academic enrollment. An increase in student enrolment, in general, would enhance academic performance. Therefore, the student enrollment pattern demands great attention, as it is a vital performance indicator of academic sustainability. In this paper, student enrolment data is pre-processed to obtain the gross enrolment ratio (GER). GER analysis and forecasting were performed using the state of art models Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The purpose of this study is to analyze and compare student GER (time series data) using ARIMA (statistical methods) and LSTM (machine learning approach), forecast GER using a better method, and propose corrective measures for increasing student enrolment. The comparison results confirmed that LSTM out-performs ARIMA by an average of 0.1322% and 5.6% in both Root Mean Square Error (RMSE) and Accuracy. The predicted GER using LSTM for the academic year 2035 is 34.23% which is far lower than 50% which is targeted by Govt. of India. An in-depth analysis of student enrolment and GER in higher education in Mizoram was done, and corrective measures were proposed for enhancing GER.

Index Terms—Machine learning, statistical method, GER prediction, forecasting model

Jamal Hussain, David Rosangliana, and Vanlalruata are with Department of Mathematics and Computer Science, Mizoram University, Aizawl, India.
*Correspondence: drosangliana@gmail.com (D.R.)


Cite: Jamal Hussain, David Rosangliana*, and Vanlalruata, "Student Gross Enrolment Ratio Forecasting: A Comparative Study Using Statistical Method and Machine Learning," International Journal of Information and Education Technology vol. 13, no. 3, pp. 439-447, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2022: 2.0), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net


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