Computational Thinking of High School Students in Solving Mathematical Problems Reviewed from the Level of Mathematical Anxiety

Rilo Pambudi; I Ketut  Budayasa; Elly Matul  Imah

doi:10.63922/ijevss.v5i03.5046

Rilo Pambudi Mathematic Education, Postgraduate, State University of Surabaya, Indonesia
I Ketut Budayasa Mathematic Education, Postgraduate, State University of Surabaya, Indonesia
Elly Matul Imah Mathematic Education, Postgraduate, State University of Surabaya, Indonesia

DOI: https://doi.org/10.63922/ijevss.v5i03.5046

Keywords: Computational Thinking, math anxiety, math problem

Abstract

Technological developments in the 21st century are increasingly influencing learning in the world of education. These technological advancements require students to develop adaptive skills in line with technological developments, one of which is computational thinking. Computational thinking is a way of thinking aimed at solving problems by utilizing basic computer science concepts, with indicators including decomposition, abstraction, pattern recognition, and algorithms. However, several studies have found that students' computational thinking skills are not yet optimal. Furthermore, high school students' mathematical problem-solving abilities are still relatively low. One factor contributing to this suboptimal ability to solve mathematical problems is math anxiety. The level of math anxiety varies from student to student. There are three levels of math anxiety: high, moderate, and low, and these differences can be influenced by many factors. Therefore, this study has a goal to describe high school students' computational thinking based on their level of math anxiety in solving mathematical problems.

Based on the research objectives, this study used a descriptive exploratory research approach with a qualitative approach. The study was conducted at Taman 1 State Senior High School, Sidoarjo Regency, with three 11th-grade male students with low, medium, and high levels of math anxiety as subjects. Three instruments were used: a math anxiety questionnaire, a math problem-solving task, and an interview guide. Data collection was conducted through semi-structured interviews with each subject using math problem-solving task method. Interview transcripts were then classified according to computational thinking indicators for interpretation. Based on the qualitative analysis, differences in a person's level of anxiety significantly impact their computational thinking skills.

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