Pre-Service Teachers and Computational Thinking: Designing Meaningful Learning in Higher Education

Abstract

This study aims to understand the students’ computational thinking skills in statistics. The type of research is descriptive with a qualitative approach. The data collection techniques in this study include 1) Tests; 2) Interviews; 3) Documentation; and 4) Validation Sheets. The data analysis in this study involves: 1) Data condensation; 2) Data presentation; 3) Verification; and 4) Conclusion drawing. The validity of the data in this study is ensured using the technique of triangulation. Subjects were selected using purposive sampling.  The instruments used were two statistical problem-solving questions. The results showed that in solving the first and second questions, the respondents could address the problems using the components of Computational Thinking, starting with decomposition, abstraction, and algorithm tasks. However, the pattern recognition component was not evident in the problem-solving process, even though some respondents gave incorrect answers. This was because the respondents did not fully understand the questions. They only read the questions once or twice, so the information was not fully comprehended. Additionally, the respondents only considered the simplest path and overlooked more complex paths in solving the second question. Students can carry out abstraction and algorithmic tasks, but they still struggle with decomposition and pattern recognition.

Keywords: student, mathematics, statistics, computational thinking, ability