物理计算思维能力(CT)在实验中的分析比较

S. Fayanto, Sul Daeng Naba, Aris Kurniawan, Utami Putri, Veronika Dua Padang
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引用次数: 0

摘要

研究目的本研究旨在从计算工具的角度分析学生使用计算思维的反应,并分析性别对学生计算思维能力的影响。研究方法:研究设计采用比较法,数据收集技术采用李克特量表问卷调查,包括 25 个项目,涵盖计算思维能力的五个维度:抽象、分解、算法思维、评价和概括。研究对象涉及物理、物理教育、地理、采矿工程和职业技术教育五个班级,重点考察学生使用 JASP 和 IBM SPSS 分析数据的能力。数据分析方法包括(1).比较分析;(2).相关分析(斯皮尔曼);(3).卡方检验。研究结果结果显示,各班学生的计算思维能力参差不齐,各技能维度之间存在显著相关。物理和物理教育成绩突出,地理和采矿工程也取得了不错的进步。职业技术教育课程在计算思维能力的各个方面都显示出近乎完美的相关性。同时,从性别上看,性别对计算思维能力有显著影响(Sig<0.00)。分析凸显了不同班级之间计算思维能力的差异以及性别的显著影响。意义:本研究强调了在高等教育中培养计算思维能力的重要性,以及采用包容性方法提高学生计算能力的必要性。本研究的意义为改进物理教育中的计算思维教学提供了宝贵的启示。可采取的步骤包括识别和加强薄弱环节,如抽象和概括,并使用特定策略增加学生的知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Analyze Comparative of Physics Computational Thinking Skill (CTs) in Experiment Laboratory
Objective: This study aimed to analyze students' response to the use of computational thinking from the perspective of computational tools and to analyze the influence of gender on students' computational thinking skills. Method: Research design using a comparative approach with data collection techniques involved a survey using a Likert scale questionnaire comprising 25 items, covering five dimensions of computational thinking skills: abstraction, decomposition, algorithm thinking, evaluation, and generalization. The study subjects involved five classes: physics, physics education, geography, mining engineering, and vocational-technical education, focusing on students' ability to analyze data using JASP and IBM SPSS. The data analyze methods included: (1). Comparative Analyze; (2). Correlation analyzes (Spearman); (3). Chi-square test. Finding: The results showed that the computational thinking skills of students from various classes varied, with significant correlations between the skill dimensions. Physics and Physics Education stood out with exemplary achievements, while Geography and Mining Engineering also showed good progress. The vocational-technical education program displayed nearly perfect correlations in all aspects of computational thinking skills. Meanwhile, from the gender aspect, gender significantly influenced computational thinking skills (Sig<0.00). The analyze highlighted the differences in computational thinking skills between classes and the significant influence of gender. Implication: This emphasized the importance of developing computational thinking skills in higher education and the need for inclusive approaches to enhance computational excellence among students. The implications of this study give valuable insights for improving the teaching of computational thinking in physics education. Steps that might be addressed include identifying and enhancing weak components, such as abstraction and generalization, and using particular tactics to increase students' knowledge.
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