Artificial Neural Network Model for Forecasting Student Failure in Math Course

Poturić, Vanja Čotić; Bašić-Šiško, Angela; Črnjarić-Žic, Nelida

doi:10.21125/iceri.2022.1448

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• artificial neural networks or multilayer perceptron [1][2][3][4][5][6] ensemble methods with different classification methods [1,4];…”

Section: Introductionmentioning

confidence: 99%

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-Castro¹

2023

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…• artificial neural networks or multilayer perceptron [1][2][3][4][5][6] ensemble methods with different classification methods [1,4];…”

Section: Introductionmentioning

confidence: 99%

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-Castro¹

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…• artificial neural networks or multilayer perceptron [1][2][3][4][5][6] ensemble methods with different classification methods [1,4];…”

Section: Introductionmentioning

confidence: 99%

“…Much of the prior research in this domain has concentrated on online courses [1,2,4,5,8], namely computer networking and web design [1], mathematics [8], and STEM (science, technology, engineering, and mathematics) in general [5]. In these studies, the notion of "failing" encompasses both dropping out of the course and not successfully passing it.…”

Section: Introductionmentioning

confidence: 99%

Course Prophet: A System for Predicting Course Failures with Machine Learning: A Numerical Methods Case Study

Caicedo-Castro

2023

Sustainability

View full text Add to dashboard Cite

In this study, our purpose was to conceptualize a machine-learning-driven system capable of predicting whether a given student is at risk of failing a course, relying exclusively on their performance in prerequisite courses. Our research centers around students pursuing a bachelor’s degree in systems engineering at the University of Córdoba, Colombia. Specifically, we concentrate on the predictive task of identifying students who are at risk of failing the numerical methods course. To achieve this goal, we collected a dataset sourced from the academic histories of 103 students, encompassing both those who failed and those who successfully passed the aforementioned course. We used this dataset to conduct an empirical study to evaluate various machine learning methods. The results of this study revealed that the Gaussian process with Matern kernel outperformed the other methods we studied. This particular method attained the highest accuracy (80.45%), demonstrating a favorable trade-off between precision and recall. The harmonic mean of precision and recall stood at 72.52%. As far as we know, prior research utilizing a similar vector representation of students’ academic histories, as employed in our study, had not achieved this level of prediction accuracy. In conclusion, the main contribution of this research is the inception of the prototype named Course Prophet. Leveraging the Gaussian process, this tool adeptly identifies students who face a higher probability of encountering challenges in the numerical methods course, based on their performance in prerequisite courses.

show abstract

Quantum Course Prophet: Quantum Machine Learning for Predicting Course Failures: A Case Study on Numerical Methods

Caicedo-Castro

2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Artificial Neural Network Model for Forecasting Student Failure in Math Course

Cited by 3 publications

References 0 publications

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

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

Course Prophet: A System for Predicting Course Failures with Machine Learning: A Numerical Methods Case Study

Quantum Course Prophet: Quantum Machine Learning for Predicting Course Failures: A Case Study on Numerical Methods

Contact Info

Product

Resources

About