An Integrated Framework for Automated Measurement and Prediction of Program Outcome Attainment in Engineering Education

ABSTRACT Program Outcomes (POs) are critical for engineering program accreditation, yet traditional evaluation methods often lack objectivity, consistency, and timely feedback. While machine learning (ML) has been applied to predict general student success, its use for predicting PO attainment levels from early academic data remains underexplored. This study introduces an integrated framework for computer engineering programs, combining a systematic PO assessment model with ML‐driven prediction. The assessment model quantifies PO attainment rates (POAR) from weighted course assessments, mappings between Course Learning Outcomes (CLOs) and POs, CLO‐assessment relationships, and student grades. Using these POARs, various ML techniques were trained on historical data from 327 graduates, utilizing their grades from 25 early‐semester courses and graduation POARs. Our findings demonstrate that POARs can be successfully predicted from this early data, achieving a mean absolute percentage error around 5%. Consequently, this study presents a scalable and objective tool that (1) provides a systematic framework for POAR measurement; (2) offers an effective ML model for predicting graduation POARs of students; and (3) delivers data‐driven insights for proactive student support, timely interventions, and evidence‐based curriculum optimization, thereby supporting continuous program improvement and accreditation efforts.