ANALYSIS OF STUDENT ERRORS IN COMPUTATIONAL THINKING BASED ON NEWMAN’S ERROR ANALYSIS IN STATISTICS - Raden Arsyilia Putri Makin Nabila; Rizky Rosjanuardi; Siti Fatimah (Universitas Labuhanbatu, 2026)
Purpose – Computational thinking is a fundamental skill every individual must possess, alongside reading and writing. Limitations in implementing this skill lead to student errors. This study aims to analyze errors in computational thinking using Newman’s error analysis in statistics.Methodology – The research method was qualitative, using a case study approach, and was conducted with 17 ninth-grade students from a junior high school in Garut Regency who had studied statistics. Three students were selected as subjects, each representing a different category of computational thinking. Data were collected through a computational thinking essay test containing decomposition, pattern recognition, abstraction, and algorithmic thinking in each question, as well as interview results.Findings – S1, S2, and S3 made errors in the computational thinking components. In decomposition, S1 and S2 made no errors, while S3 made a comprehension error. In pattern recognition, S1 made no errors, while S2 and S3 made understanding errors. In abstraction, S1 also made no errors, whereas S2 and S3 made errors in understanding and transformation. In algorithmic thinking, all three subjects made errors, specifically process skills errors and errors in writing the final answer.Contribution – This study contributes to educational assessment by showing how Newman Error Analysis evaluates students’ cognitive processes in computational thinking within mathematics learning. The findings provide diagnostic information that helps teachers make instructional decisions, develop targeted interventions, and improve evaluation practices in statistics education.