ABSTRACT Human advancement hinges on the capacity to acquire knowledge and engage with complex ideas. Education, therefore, plays a pivotal role in shaping cognitive and societal growth. However, the increasing commercialization of education has raised significant concerns regarding declining academic standards, reduced student performance, and escalating unemployment. To address these systemic challenges, this study proposes a machine learning‐based framework for predicting and evaluating Course Learning Outcomes (CLOs) and Program Learning Outcomes (PLOs) in an undergraduate engineering context. The proposed model analyzes historical academic records to investigate the influence of midterm and final assessments on overall grade performance and CLO/PLO attainment. Results indicate that CLO 1 has consistently achieved approximately 90% success over the past 2 academic years, a trend expected to persist based on predictive insights. These findings offer actionable guidance for academic departments to implement targeted interventions, such as scenario‐based evaluations, to enhance student learning outcomes. By leveraging Python‐based machine learning techniques, institutions can scale their data‐driven assessment strategies and reinforce evidence‐based educational practices. This study contributes to the growing field of AI‐enhanced education, offering practical implications for improving academic quality and institutional decision‐making.
Zaman et al. (Tue,) studied this question.
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