Pengaruh Model Problem Based Learning terhadap Kemampuan Computational Thinking Berbantuan Media Geogebra

The research aims to determine: (1) the effect of the problem-based learning models on computational thinking skills assisted by geogebra media; (2) student responses to the use of geogebra-assisted problem-based learning models for flat sided geometric material. The type of research is a quasi-experimental with a posttest-only control design, the research population was all students of class VIII SMP Negeri 23 Medan for the 2022/2023 academic year. The sample of this research were 25 students in the experimental class and 25 students in the control class who were taken by cluster random sampling. Data collection on computational thinking skills used a test instrument in the form of a description consisting of 2 questions and student responses using a response questionnaire. The indicators of computational thinking skills that were measured in this research were decomposition, pattern recognition, generalization and abstraction, and algorithmic thinking. Data analysis used the t test (Independent Sample t-Test) for computational thinking skills and percentages for student response questionnaire. Based on the research results obtained (1) there is a significant influence of the problem-based learning model on the ability computational thinking skills assisted by geogebra; (2) there is a positive response from students towards the use of a problem-based learning model assisted by geogebra media on flat sided geometric material

Students’ Computational Thinking Skills In Physics Learning: A Case study of Kinematic Concepts

Physics learning provides a context for future careers in fostering ability in high-end logic with the 21st learning goals. Applying computational thinking in schools is challenging and requires systemic transformation and teacher attention. This study aims to investigate the computational thinking of students in physics learning. This study used exploratory qualitative research. Data were gathered through observation, interviews, and portfolio documents. The data are analyzed through six stages: preparing and organizing, exploring, building descriptions, representing the findings, interpreting the results, and validating the accuracy. The result indicated four primary computational thinking skills: decomposition, abstraction, simulation, and evaluation. The computational thinking skills in physics learning can develop students’ understanding and implementation of physics concepts based on data, not just mathematical formulas. Computational thinking in physics learning gives students the opportunity and space to explore and develop their ideas and logical reasoning more deeply in problem-defining, solutions, and evaluation. Students use their logical reasoning to solve the problem precisely. This study is expected to be used as a basis and support for physics teachers to integrate computational thinking into their learning classroom

Computational Thinking Skills Indicators in Number Patterns

This research aims to examine a) what computational thinking indicators have been developed by researchers, b) what computational thinking indicators can be used in learning mathematics appropriately, and c) how to describe the development of student computational thinking indicators from the answers of computational thinking tests. This research is a qualitative descriptive study through a process of collecting data from literature reviews, integrated computational thinking math tests, and interviews. Data collection instruments used research notes, interview sheets, and CT question sheets. The results showed that a) 20 computational thinking indicators had been studied by researchers, b) computational thinking indicators that could be used in learning mathematics include problem decomposition, abstraction, pattern recognition, procedural algorithms, and generalizations, and c) From the student answers, five proposed computational thinking indicators can be developed even though they were not perfect. The general implication of this research is that there are five indicators of computational thinking skills that can be used in mathematics learning, specifically in number patterns, which include problem decomposition, abstraction, pattern recognition, pattern recognition, procedural algorithm, and generalization. The researchers developed all five computational thinking skills indicators in the instructional designs of not only the number pattern concept but also combination, geometry, combinatorics, etc

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

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

THE EFFECTIVENESS OF USING INSTRUMENT AND RUBRIC OF CREATIVE THINKING SKILL-BASED ASSESSMENT PROJECT IN THE LEARNING OF CONSUMER EDUCATION

The purpose of this research was to determine (1) the effectiveness of the use of the instruments and rubrics of creative thinking skills–based assessment project in the learning of consumer education, (2) the aspect of the ability of creative thinking skills that is already possessed by students after learning consumer education using instruments and rubrics of assessment project, and (3) the opinion of students about the learning of consumer education using the instruments and rubrics of creative thinking skills–based assessment project. The research was held using the type of survey with the evaluation approach of students‘ study results. It was conducted in the first semester of the academic year 2014/2015 at the Study Program of Three Years Diploma of Clothing Technique of Yogyakarta State University as many as 34 students. A set of questions and rubrics for the pre-test, post-test and task of creative thinking skills-based assessment project. The data was analysed using quantitative descriptive statistical techniques. The results showed that the instruments and rubrics of assessment project were effectively able to develop creative thinking skills of students by 88.24%, the score is above 80% of students achieved a score above 75 competent equivalent to a score of B+ (75-79), which includes: the ability to fluently generate new ideas (fluency), the ability to suggests a variety of approaches to the problem-solving (flexibility), the ability to spark ideas in an original way (originality), the ability to describe something in detail (decomposition), and the ability to review things from a different perspective of those already known by many people (reformulation). The aspects of fluency, originality, and the decomposition are included in the high category, whereas the flexibility and reformulation aspects are in the middle category. The result shows that the students‘ opinions on the use of instruments and rubrics of creative thinking skills–based assesment project in learning consumer education are in the excellent category (26 students (76.47%)) and 8 (23.52%) are in good category

Computational Thinking Self-Efficacy in High School Latin Language Learning

Research suggests that computational thinking is a necessary skill exercised in STEM courses, non-STEM fields, and in everyday life. However, very little research has investigated the potential transfer of computational thinking self-efficacy available through classical Latin courses. This causal comparative study contrasted the computational thinking self-efficacy of computer science students with no exposure to Latin to computer science students with exposure to Latin at a Memphis all-boy high school. The independent variables were Latin language learning experience, i.e., up to 6 years total of Latin language learning (n = 33), versus 0 years of Latin language learning experience (n = 20). Additional data on the number of years enrolled in other foreign languages was collected. The dependent variable was mean scores of items found on a computational thinking and problem solving self-efficacy scale. This instrument uses a Likert scale to measure students self-efficacy in nine computational thinking components including algorithmic thinking; abstraction; problem decomposition; data collection, representation, and analysis; parallelization; control flow; incremental and iterative; testing and debugging; and questioning. Conducting this research addressed the question of whether the computational thinking skills present in Latin can transfer to a students computational thinking self-efficacy which may affect STEM/computer science course achievement. To test the null hypothesis that having a Latin language learning yields no significant influence on computer science students self-efficacy in computational thinking and problem solving, a multivariate analysis of variance (MANOVA) test was utilized for this causal-comparative study. To test the null hypotheses that having a Latin language learning yields no significant influence on computer science students abstraction, problem decomposition, data, parallelization, control flow, incremental and iterative, testing and debugging, and questioning skills self-efficacy, a separate ANOVA test were run for each computational thinking skill component.The data did not meet of the necessary assumptions for a MANOVA test. The sample size for the non-Latin group was a concern at n = 20. The means from the descriptive statistics show that the non-Latin group outscored the Latin group in most of the computational thinking skills. Pillais trace statistic from the MANOVA test showed no statistical significance in the computational thinking and problem solving scale. The individual results from the ANOVA tests showed no statistical significance for any of the nine subscales

Student’s Computational Thinking Ability in Solving Trigonometry Problems in the Review of Self-Regulated Learning

This research is motivated by the habits we often encounter in learning, especially in mathematics. Each student has different computational thinking abilities. Computational thinking ability is a thinking ability that supports problem-solving solutions. Computational thinking components include decomposition, pattern recognition, abstraction, and algorithm design. This research aims to: 1) Describe the computational thinking abilities of students with high self-regulated learning in solving trigonometry problems, 2) Describe the computational thinking abilities of students with moderate self-regulated learning in solving trigonometry problems, 3) Describe the computational thinking abilities of students with low self-regulated learning in solving trigonometry problems. This research used a qualitative approach with a case study type of research. This research was conducted at SMKN 2 Tulungagung which was attended by all students of class XI TKRO 3, totaling 32 students. Of the 32 students, 6 students will be selected as subjects who are classified based on the level of self-regulated learning. Data collection techniques used are observation, tests, interviews, and documentation. Data analysis techniques were carried out through the stages of data collection, data presentation, and conclusion. The results of this research indicate that: 1) students with high self-regulated learning can fulfill 3-4 indicators of computational thinking skills in solving trigonometry problems, 2) students with moderate self-regulated learning can fulfill 2-3 indicators of computational thinking skills in solving trigonometry problems, 3) and students with low self-regulated learning can fulfill 0-1 indicators of computational thinking skills in solving trigonometry problem

Problem Decomposition Skills, Mathematical Maturity, and Their Relation to Mathematics Problem-Solving in A Computer Science Learning Class

This study investigates how students represent ideas when decomposing mathematical problems and how their mathematical maturity influences the problem-solving process. The method used in this research is explorative research. The subject of this research was six Computers Science Education Department students at the Indonesian Education University. The instrument used task-based interviews. Data analysis used the concept of Miles and Huberman, including data reduction, presentation, and drawing conclusions. The research found that problem decomposition skills, mathematical maturity, and their relation to solving mathematical problems in computer science learning classes influenced one another. Decomposition skills were influenced by how basic math skills are taught, so they can affect students' maturity in solving math problems