Analysis of the effect of reading and comprehension skills on mathematics achievement using data mining methods

This study examines the impact of students' reading and comprehension skills on their mathematics achievement using data mining techniques. Reading and comprehension are fundamental cognitive abilities that influence academic success in various subjects. Understanding the contribution of these skills to success in mathematics, a subject based on analytical thinking, can provide new findings and valuable insights into both theoretical frameworks and practical approaches to improving educational practices. In this study, correlation, clustering, and multiple linear regression analyses were conducted on a dataset of 1000 students to statistically examine the relationship between students' reading and comprehension skill scores and their mathematics performance. Furthermore, machine learning algorithms such as decision trees (DT), k-nearest neighbors (k-NN), support vector machines (SVM), and artificial neural networks (ANN) were used to predict students' mathematics scores. The research findings showed that reading and comprehension skills have a significant relationship with mathematics achievement. Specifically, students with higher reading and comprehension scores were found to have higher mathematics achievement. This result demonstrates that linguistic skills contribute to mathematical thinking and problem-solving skills. In this respect, the study suggests new ways to guide future educational research.