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

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.