Manuscript received October 27, 2022; revised November 18, 2022; accepted December 7, 2022.
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).