Forecasting Student Employment Trends in Colleges and Universities Education Using Time Series Analysis Algorithms

Abstract

Student employment trends in colleges have been evolving, with a notable shift towards internships, co-op programs, and other experiential learning opportunities. Colleges are increasingly emphasizing the importance of gaining practical work experience alongside academic studies to enhance students' employability. This trend reflects the growing demand from employers for graduates who possess not only theoretical knowledge but also relevant skills and hands-on experience. This study investigates student employment trends within Colleges and Universities education through the application of time series analysis algorithms. Utilizing historical data spanning five years from 2019 to 2023, the study examines the dynamics of student employment rates and explores the effectiveness of forecasting methodologies, including ARIMA and Ranking with ARIMA (R-ARIMA). The findings reveal a consistent upward trend in student employment rates over the study period, indicating a favorable environment for student career opportunities within higher education institutions. Moreover, the accuracy of forecasting models is demonstrated through the close alignment between predicted and observed employment rates. Optimal ARIMA configurations are identified, providing insights for strategic planning and resource allocation to support student career development and success. The findings reveal a consistent upward trend in student employment rates over the study period, with the percentage of employed students increasing annually. For instance, the student employment rate rises from 65% in 2019 to 77% in 2023. This indicates a favourable environment for student career opportunities within higher education institutions. Moreover, the accuracy of forecasting models is demonstrated through the close alignment between predicted and observed employment rates. Optimal ARIMA configurations are identified, with the best-performing model achieving an AIC score of 95.3 and a BIC score of 99.8. These configurations provide insights for strategic planning and resource allocation to support student career development and success.