Forecasting Scheduled Tribe Student Enrollment in India Using the ARIMA Algorithm: A Data Mining Approach

Background and purpose: Forecasting is crucial in strategic decision-making processes that facilitate effective and efficient management practices. In the context of higher education institutions (HEIs), mining and forecasting prove advantageous in their ongoing endeavor to analyze historical, current, and longitudinal data associations about specific scenarios within an educational framework. The primary objective of this study is to forecast the enrollment figures of students belonging to Scheduled Tribes (STs).Methods: This study employed the widely recognized ARIMA (p, d, q) model to forecast the total STs’ students’ enrollments for the academic sessions from 2022-23 to 2030-31, utilizing the comprehensive enrollment time series data of Schedule Tribes (ST) students from the years 2012-13 to 2021-22 to the Indian Higher Education System (IHES). The SPSS, JMP, R studio, and MS Excel (V. 21) software are used for the analysis. The various combinations of p, d, and q parameters were evaluated, with the model yielding the lowest Akaike Information Criterion (AIC) value selected for the predictive analysis.Key Results: The simulation outcomes indicated that the ARIMA (0,1,2) model emerged as the statistically appropriate model for forecasting enrollment trends in the IHES for ST community students. The forecast revealed a noticeable upward trend in enrollment figures for the subsequent academic years up to 2030-31, with a total of 113% increasement in enrollment of the STs in Indian Higher Education.Implications: The findings of this study are anticipated to yield several advantages, including socio-educational policy perspectives, enhancement of the current admission and retention policies for STs’ student, and the facilitation of central decision-making regarding the enrollment management framework of IHES. The forecasted enrollment figures may also serve as a foundational basis for advocating the construction of additional classroom facilities and faculty to accommodate the rising demand.