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Course Prophet: A Machine Learning-Based System for Early Prediction of Student Failure in Numerical Methods Course in the Bachelor’s Degree in Engineering at the University of Córdoba, Colombia
Caicedo-Castro, I. Course Prophet: A System for Predicting Course Failures with Machine Learning: A Numerical Methods Case Study. Sustainability2023, 15, 13950.
Caicedo-Castro, I. Course Prophet: A System for Predicting Course Failures with Machine Learning: A Numerical Methods Case Study. Sustainability 2023, 15, 13950.
Caicedo-Castro, I. Course Prophet: A System for Predicting Course Failures with Machine Learning: A Numerical Methods Case Study. Sustainability2023, 15, 13950.
Caicedo-Castro, I. Course Prophet: A System for Predicting Course Failures with Machine Learning: A Numerical Methods Case Study. Sustainability 2023, 15, 13950.
Abstract
In this paper, we address the following research question: Is feasibility to use an artificial intelligence system to predict the risk of student failure in a course based solely on their performance in prerequisite courses? Adopting a machine learning-based quantitative approach, we implement Course Prophet, the prototype of a predictive system that maps the input variables representing student performance to the target variable, i.e., the risk of course failure. We evaluate multiple machine learning methods and find that the Gaussian process with Matern kernel outperforms other methods, achieving the highest accuracy and a favorable trade-off between precision and recall. We conduct this research in the context of the students pursuing a Bachelor’s degree in Systems Engineering at the University of Córdoba, Colombia. In this context, we focus on predicting the risk of failing the Numerical Methods course. In conclusion, the main contribution of this research is the development of Course Prophet, providing an efficient and accurate tool for predicting student failure in the Numerical Methods course based on their academic history in prerequisite courses.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
