Published in Data Science and Engineering (DSE) Record 2024 Vol. 5 No. 1 pp. 123-140
Abstract
This study emphasizes the critical role of course learning outcomes, particularly in assessing student capability, mainly in the cognitive learning framework provided by Bloom's Taxonomy. In computer science education, aligning these outcomes with curriculum guidelines is important for program quality and relevance. The study introduces machine learning models, including Multinomial Naive Bayes, Logistic Regression, Random Forest, and Extreme Gradient Boosting (XG Boost), to predict and visualize course learning outcomes classification using radar charts. The primary aim is to establish a classification model aligning with ACM/IEEE undergraduate computer science program curriculum guidelines. Additionally, the study addresses the ambiguity inherent in Bloom's Taxonomy, where the same action verb may span multiple cognitive levels, potentially confusing in defining learning objectives across Familiarity, Usage, and Assessment domains. Through a semi-automated prototype, the study showcases a scalable and adaptable framework for visualizing learning outcomes classification results by radar charts. This framework is intended to benefit educators, curriculum developers, and accreditation bodies, enhancing the coherence and effectiveness of computer science undergraduate programs.