Analysis of Content Validity on Mathematical Computational Thinking Skill Test for Junior High School Student Using Aiken Method

Abstract

Computational thinking skills are a relevant approach to future problem-solving. Therefore, these skills need to be integrated into mathematics learning in schools. This research is part of developing mathematical computational thinking skill tests for junior high school students. In this segment, the study aims to analyze the content validity of the mathematical computational thinking skill test. The main stages in this research are define, design, and develop. Expert validation data were collected using Google Form sheets. The analysis technique used is the content validity technique with the V Aiken method. The results of the study revealed that the results of content validation through the assessment of 7 experts, developed a test specification containing 20 items that measure mathematical computational thinking skills with a coefficient (V) in the interval (0.770 – 0.920) with an average of 0.866 or very good category. The test instrument is valid for measuring decomposition indicators, pattern recognition, abstraction, algorithmic thinking, and evaluation indicators. Each indicator is measured by 4 items with a coefficient of V decomposition indicator of 0.868, pattern recognition 0.883, abstraction 0.865, algorithmic thinking 0.833, and evaluation 0.883. The study concludes that the indicators of decomposition, pattern recognition, abstraction, algorithmic thinking, and evaluation indicators are valid in measuring mathematical computational thinking skills.

Computational Thinking Skills in 21st Century Mathematics Learning

“…Although existing research has highlighted the importance of computational thinking in mathematics learning, there is a lack of comprehensive research that specifically evaluates the impact of various learning approaches, such as problem-based learning, metaphorical thinking, and flipped classroom models, on the development of mathematics learning. computational thinking skills in mathematics (Putri et al, 2022;Yerizon et al, 2022;Widyatiningtyas et al, 2015). Apart from that, the influence of students' intrapersonal intelligence, learning motivation, and selfregulated learning on mathematical computational thinking abilities is still an area that requires further investigation (Aswin et al, 2022;Maratusyo seen et al, 2021;Mulyono et al, 2020).…”