Assessing Data Literacy Competencies in Mathematics Among Junior High School Students in Yogyakarta City

Abstract

This research aims to assess students' data literacy skills, focusing on their ability to translate data from one format to another, comprehend data-related problems, analyze strategies for data-driven problem-solving, predict outcomes based on data, draw conclusions, and construct arguments grounded in data. The study employs a survey methodology with both quantitative and qualitative approaches. A total of 362 students from various educational levels in Yogyakarta's state junior high schools were surveyed and selected through stratified and proportionate random sampling techniques. Data were collected using a test instrument comprising six questions designed to measure different aspects of data literacy, alongside interview results. The students' data literacy was categorized into high, medium, and low levels. The findings indicate that a significant majority (95%) of students exhibited low data literacy, particularly struggling with drawing conclusions and constructing arguments based on data. Students with high data literacy skills successfully met all the indicators. Those with medium-level skills performed well on most indicators but faced difficulties in choosing strategies for data problem-solving and drawing conclusions, often due to insufficient time for deeper reflection. Conversely, students with low data literacy struggled to meet any of the indicators effectively.