Relationship Between Students’ Learning Interest and Mathematical Critical Thinking Skills Students in Statistics

Abstract

This study aims to analyze the relationship between students’ learning interest and mathematical critical thinking skills students in Statistics. The urgency of this research is based on the importance of developing critical thinking skills in 21st-century mathematics education, as well as the assumption that learning interest is one of the internal factors contributing to these skills. The study employed a quantitative approach with a correlational method. The sample consisted of 35 Grade X students from a public senior high school in Garut Regency, selected through purposive sampling. The research instruments included a learning interest questionnaire developed based on four main indicators and an essay test measuring mathematical critical thinking skills constructed in accordance with critical thinking indicators. The instruments were validated through expert judgment for content validity, and reliability testing was conducted using Cronbach’s Alpha coefficient for the questionnaire and internal consistency testing for the essay test. Data were analyzed using the Shapiro–Wilk normality test, followed by Spearman’s rho correlation test since one of the variables was not normally distributed. The results showed a correlation coefficient of 0.247 with a significance value of 0.152 (p > 0.05), indicating a weak and statistically non-significant positive relationship between learning interest and mathematical critical thinking skills. The novelty of this study lies in examining the relationship between the two variables specifically in the Statistics topic at the senior high school level. The findings imply that improving mathematical critical thinking skills cannot rely solely on enhancing students’ learning interest, but must also be supported by instructional strategies that promote higher-order thinking activities. Future research is recommended to involve a larger sample size and to consider additional variables such as self-efficacy, instructional models, or students’ prior knowledge.