Rahmatika Diah Silvia, A. S. Pramasdyahsari, Nizaruddin Nizaruddin
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Using the problem- solving ability test, 2 students with high-level problem-solving abilities, 2 students with medium-level problem-solving abilities, and 2 students with low-level problem-solving abilities were selected using the problem-solving ability test. The data collection techniques used were problem solving ability tests, computational thinking ability tests, and interviews. Data analysis techniques were carried out in 3 stages, namely data reduction, data presentation, and drawing conclusion. Data validity checking technique uses source triangulation. 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引用次数: 0
摘要
许多学生在解决数学相关问题时仍然有困难,其中之一就是代数材料。计算思维是一种通过抽象、模式识别、分解和算法技能来帮助学生解决学生所面临的问题的能力。本研究旨在描述初中生在解决数学问题时的计算思维能力。这种类型的研究是定性描述性研究。进行研究的时间是在2022/2023学年的奇数学期。被试对象是SMP Negeri 2 Karangrayung的IX A年级学生。采用问题解决能力测试,选择具有高水平问题解决能力的学生2名,具有中等水平问题解决能力的学生2名,具有低水平问题解决能力的学生2名。使用的数据收集技术是问题解决能力测试、计算思维能力测试和访谈。数据分析技术分为数据简化、数据呈现、得出结论3个阶段。数据有效性检验技术采用源三角测量法。本研究结果表明,解决问题能力高的学生能够满足计算思维的所有指标,解决问题能力中等的学生能够满足4项计算思维指标中的3项,而解决问题能力低的学生能够满足4项计算思维指标中的2项。
ANALISIS KEMAMPUAN COMPUTATIONAL THINKING SISWA PADA MATERI ALJABAR DITINJAU DARI PEMECAHAN MASALAH MATEMATIS
Many students still have difficulty solving problems related to mathematics, one of which is algebraic material. Computational thinking is an ability needed to assist students in solving problems faced by students through abstraction, pattern recognition, decomposition, and algorithm skills. This study aims to describe the computational thinking abilities of IX grade junior high school students in solving mathematical problem solving. This type of research is a qualitative descriptive research. The time for conducting research is in the odd semester of the 2022/2023 academic year. The subjects taken were IX A grade students at SMP Negeri 2 Karangrayung. Using the problem- solving ability test, 2 students with high-level problem-solving abilities, 2 students with medium-level problem-solving abilities, and 2 students with low-level problem-solving abilities were selected using the problem-solving ability test. The data collection techniques used were problem solving ability tests, computational thinking ability tests, and interviews. Data analysis techniques were carried out in 3 stages, namely data reduction, data presentation, and drawing conclusion. Data validity checking technique uses source triangulation. The results of this study show that students with high-level problem-solving skills are able to meet all indicators of computational thinking, students with medium- level problem-solving abilities are able to meet fulfill 3 of 4 computational thinking indicators, while students with low-level problem-solving skills are able to fulfill 2 of 4 computational thinking indicators.