Analysis of Student Computational Thinking in Solving Social Statistics Problems

This study aims to see students' computational thinking in solving social statistics questions and to find out why students experience their mistakes. This type of research uses a descriptive qualitative approach. The subjects used in this study were Governmental Science students taking the Social Statistics course. Data collection techniques were carried out by observation and tests. The instruments used in this study were the observation sheet and the test question sheet. The analysis is carried out by reducing the data first, then presenting the data, and ending by concluding the results of the computational thinking indicator. The results showed that all aspects of computational thinking have been carried out by students, starting from Decomposition, Pattern Recognition, Abstraction, and Algorithm design. Students get the highest percentage, namely algorithm design with 84% and the lowest on decomposition with 65.5%. The cause of errors, in general, is because students are not used to completing in a structured manner. Students are accustomed to solving problems by directly substituting values into the formula without first writing down what is known and looking for what is needed in the questions first.