Mapping Anxiety, Developing Solutions: A Statistical Study of Student Anxiety Using The K-Modes Clustering Method

Abstract

Statistics anxiety is a common issue among university students that can negatively affect their learning process and academic performance. This study aims to identify patterns of statistics anxiety among undergraduate students at Universitas Negeri Padang using the Statistics Anxiety Rating Scale (STARS), which consists of six dimensions. A total of 479 valid responses were analyzed using the k-modes clustering method, which is appropriate for categorical data. The optimal number of clusters was determined using the elbow and silhouette methods, resulting in three clusters. The clustering results reveal three distinct groups of students characterized by high, moderate, and low levels of statistics anxiety. The average silhouette value of 0.52 indicates a moderately well-defined cluster structure. Further analysis shows that each cluster exhibits different patterns across the six anxiety dimensions, highlighting the heterogeneity of students’ responses to statistics. These findings suggest that clustering provides a more informative approach than conventional descriptive analysis in understanding statistics anxiety. The results of this study can serve as a basis for developing targeted strategies to reduce student anxiety in statistics learning