This study analyzes Secondary School Certificate (SSC) examination results in Bangladesh over the past eleven years (2015–2025), focusing on key performance indicators such as the percentage of students achieving the highest grade (A+), the overall pass rate, and the failure rate. Statistical methods are applied to identify trends, patterns, and fluctuations in educational outcomes across different boards and years. In addition to descriptive analysis, this study employs predictive modeling using the Auto-Regressive Integrated Moving Average (ARIMA) method. ARIMA is a widely used time series forecasting technique that is effective for analyzing data exhibiting trends and temporal variations. To generate evidence-based predictions of future performance, the model is trained using eleven years of SSC result data to forecast outcomes for the next year. This predictive approach aims to assist policymakers, educators, and stakeholders in anticipating challenges, developing strategies, and implementing targeted interventions to improve student outcomes. The study contributes to the existing literature by integrating statistical analysis with time series forecasting within the context of Bangladesh’s secondary education system. The results indicate that ARIMA models predict a general improvement in nationwide pass rates, while the forecasts for A+ achievement rates present a more mixed trend. Overall, this study highlights the importance of data-driven decision-making in education and demonstrates the value of statistical and forecasting techniques in predicting the future performance of Bangladeshi students.
Shikder et al. (Sun,) studied this question.