International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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A KNN - Powered Evaluation System for Students’ Performance in Computer Programming 1 and Early Intervention

Author(s) Mr. Kyle M. Valdez, Mr. Prince Geoffrey C. Castillo, Mr. Ralph Vincent V. Serrano, Mr. John Nikko M. Simangan, Prof. Dr. Jeusuel Nonnatus N. Deluna, Prof. Zenaida A. Gueta
Country Philippines
Abstract The evaluation of student performance in Computer Programming 1 at Perpetual Help College of Manila is commonly based on traditional methods such as quizzes, exams, laboratory activities, and coding projects. While these approaches measure academic output, they often fail to identify struggling students early, leading to poor performance and low retention in the subject. Instructors also lack a system that can analyze student data and provide timely insights for intervention.To address this concern, this study proposes the development of a KNN-Powered Evaluation System that utilizes the K-Nearest Neighbors (KNN) algorithm to analyze student performance data and predict academic outcomes. By using indicators such as quiz scores, attendance, and laboratory performance, the system can identify at-risk students and provide early intervention through data-driven insights and personalized recommendations. This approach aims to enhance teaching strategies and improve student success in Computer Programming 1.
Keywords K-Nearest Neighbors, Algorithm, Artificial Intelligence, Early Intervention, Early Detection, Recommendation, Learning Management System, Computer Programming 1
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-04-10
DOI https://doi.org/10.36948/ijfmr.2026.v08i02.72464
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E-ISSN 2582-2160
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