Predictive Analysis of Educational Trends: A Time Series Approach to State Matriculation Exam Performance

This study presents a comprehensive analysis and forecasting of trends in state matriculation exam scores utilizing time series analytical techniques. Amidst growing concerns about educational outcomes and their implications for policy and practice, understanding the dynamics of exam scores over time becomes crucial. By employing a robust dataset encompassing several years of exam results, this paper applies methods such as Autoregressive Integrated Moving Average (ARIMA) and Seasonal Decomposition of Time Series to uncover underlying patterns, trends, and seasonal fluctuations in the data. The findings reveal significant insights into the performance trajectories of students, highlighting both consistent trends and notable deviations over the examined period. Based on these analyses, the study forecasts future trends in matriculation exam scores, providing valuable predictions that can inform educational strategies and policy-making. The paper discusses the implications of these forecasts for stakeholders, including educators, policymakers, and students, emphasizing the importance of data-driven decision-making in enhancing educational outcomes. Through a meticulous examination of past and projected exam score trends, this research contributes to the broader discourse on educational assessment and planning, offering a novel perspective on the predictive capabilities of time series analysis in the educational domain. Received: 2 May 2024 / Accepted: 16 August 2024 / Published: 05 September 2024