Leveraging Prolog's Declarative Power for Clustering Student Performance in a Timed Quiz
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Abstract
Student performance analysis in programming education presents challenges in identifying learning patterns and addressing diverse needs. This study introduces an automated Prolog-based quiz program designed to evaluate student learning progression through a structured quiz system. Unlike conventional assessment tools, the Prolog program leverages its declarative nature to dynamically generate questions, offer real-time feedback, and facilitate level-based progression, ensuring an interactive and adaptive learning experience. Data collected from 140 students during a 15-minute quiz session was analyzed using the K-Means clustering algorithm to group students based on their highest achieved levels. Clusters revealed distinct performance profiles: foundational challenges, transitional difficulties, and variability among advanced learners. Visualizations, including scatter plots, bar charts, and box plots, highlighted these clusters, showing that most students struggled with intermediate levels, while fewer mastered advanced topics. The findings underscore the need for tailored educational interventions and demonstrate the novelty and efficacy of combining Prolog-based assessments with clustering techniques for scalable educational data mining.
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