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All Journal International Journal of Ethno-Sciences and Education Research International Journal of Technology and Education Research
-, Fadhilah Nur Sa'diyyah
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Religion Humanities Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Mathematics Transportation Other

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The Effect of Computational Thinking and Prior Mathematical Ability on Problem Solving Skills -, Fadhilah Nur Sa'diyyah; Mar Athul Wazithah T.
International Journal of Ethno-Sciences and Education Research Vol. 6 No. 2 (2026): International Journal of Ethno-Sciences and Education Research (IJEER)
Publisher : Research Collaboration Community (Rescollacom)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijeer.v6i2.1271

Abstract

This study investigates the influence of computational thinking and prior mathematical ability on students’ mathematical problem-solving skills. The research was conducted using a quantitative correlational approach involving second-semester students of the Mathematics Education Study Program at Universitas Negeri Makassar in the 2024/2025 academic year, specifically in the Basic Geometry course. Data were collected through written tests designed to measure computational thinking, prior mathematical ability, and problem-solving skills. The collected data were analyzed using multiple linear regression with the assistance of SPSS. The results indicate that both computational thinking and prior mathematical ability have a significant positive effect on students’ mathematical problem-solving skills, both partially and simultaneously. Computational thinking shows a more dominant contribution compared to prior mathematical ability, indicating that students who are capable of decomposing problems, recognizing patterns, abstracting concepts, and constructing systematic solutions tend to perform better in solving mathematical problems. Meanwhile, prior mathematical ability plays a crucial role as a cognitive foundation that supports students in understanding and applying relevant concepts during the problem-solving process. Furthermore, the regression model demonstrates that both variables contribute substantially to explaining variations in problem-solving ability, suggesting that these factors are essential components in developing higher-order thinking skills. The findings imply that mathematics instruction, particularly in Basic Geometry, should integrate computational thinking approaches while strengthening students’ foundational knowledge. Such integration is expected to enhance students’ ability to solve complex mathematical problems effectively.
Error Analysis of Students in Implementing the Newton-Raphson and Bisection Methods on Nonlinear Equation Root Problems Based on Newman's Error -, Fadhilah Nur Sa'diyyah
International Journal of Technology and Education Research Vol. 4 No. 02 (2026): International Journal of Technology and Education Research (IJETER)
Publisher : International journal of technology and education research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63922/ijeter.v4i02.3569

Abstract

This study aims to describe students' errors in solving nonlinear equation root problems using the Bisection and Newton-Raphson methods based on Newman's Error Analysis (NEA). This study uses a qualitative approach with descriptive methods. The research subjects consisted of 33 students of the Mathematics Education Study Program who had taken the Numerical Analysis course. The research instrument was a Mid-Semester Exam question on the material of nonlinear equation roots covering the Bisection and Newton-Raphson methods. Data collection techniques were carried out through written tests and semi-structured interviews, then analyzed based on Newman's error categories, namely reading, comprehension, transformation, process skills, and encoding. The results showed that the most dominant errors made by students were in the process skills category. In the Bisection method, student errors mostly occurred in the process of evaluating functions and determining new intervals during iteration, while in the Newton-Raphson method, dominant errors occurred in determining function derivatives and substituting iteration values ​​into the Newton-Raphson formula. In addition, it was found that some students experienced transformation errors due to the inability to connect mathematical concepts with algorithmic procedures of numerical methods. Interview results indicate that student errors are influenced by a lack of understanding of basic concepts, low accuracy in numerical operations, and a lack of habit of double checking calculation results. This research is expected to serve as a basis for designing Numerical Analysis learning strategies that emphasize students' conceptual understanding and procedural accuracy.
Co-Authors Mar Athul Wazithah T.