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September 10, 2025Open Access

Predicting Probabilities of Getting a High Grade in Mathematics: Probability Analysis Based on Previous Scores using Machine Learning Techniques

Puntos clave

Machine learning models have high accuracy in predicting high mathematics grades, potentially enhancing teaching methods.
The predictive model utilizes algorithms, including logistic regression and neural networks, based on previous student performance.
Data were divided into training and test groups to evaluate precision, recall, and f1-score for model effectiveness.
This research identifies key factors influencing math outcomes and supports personalized teaching approaches for students.

Resumen

This paper examines the possibilities of predicting high mathematics scores by analyzing likelihood and probability, based on students' previous scores, and using advanced machine learning techniques to optimize predictions. The main purpose of the study is to create a model that can predict the likelihood that a student will achieve a high grade on future math tests, based on their performance history and other influencing factors, such as frequency of preparation, grade point average, class participation, as well as socio-economic data. The methodology used in this study is the quantitative approach with a predictive and analytical study design. In this research, data from previous test results are analyzed and statistical models and machine learning algorithms are used, such as logistic regression, random forests, and neural networks, to build a predictive model. To train and test the model, the data is divided into a training group and a test group, while the efficiency of the model is evaluated using the measures of accuracy, positive precision (precision), care (recall), and f1-score. This process helps identify the factors that most influence math outcomes and provides important insights for educators on ways to predict student success. The results show that machine learning models, based on previous performance data, have a high accuracy in predicting high grades and can be used as support tools to personalize teaching. By providing a more accurate estimate of the probability of success, this study contributes to the field of mathematics education and creates a basis for further development of methods for adapting teaching content and support to students to improve mathematics results.

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