Bridging geometry and cultures for junior high school level: Rumoh Aceh design from a computational thinking perspective

Understanding and constructing mathematical proofs is fundamental for students in abstract algebra courses. The computational thinking approach can aid the process of compiling mathematical proofs. This study examined the impact of integrating computational thinking components in constructing mathematical proofs. The researcher employed a sequential explanatory approach to ascertain the enhancement of algebraic proof capability based on computational thinking through the t- test. A total of 32 prospective teachers in mathematics education programs were provided with worksheets for seven meetings, which were combined with computational thinking components. Quantitative data were collected from initial and subsequent test instruments. Moreover, three prospective teachers were examined through case studies to investigate their mathematical proof capability using computational thinking components, including decomposition, abstraction, pattern recognition, and algorithmic thinking. The study's findings indicated that CT intervention enhanced students' logical reasoning, proof-writing abilities, and overall engagement with abstract algebra concepts. The findings illustrate that integrating computational thinking into learning strategies can provide a framework for developing higher-order thinking skills, especially in proving, which are essential for studies in mathematics education programs.

The purpose of this study is to investigate the computational thinking skills of junior high school students in terms of FI and FD cognitive styles on materials with numerical patterns. The type of research used is qualitative descriptive. The subjects of the study were 57 students from class VIII at one of the public junior high schools in Jepara. The data collection instruments used are computational thinking test questions, GEFT tests, and interviews. Researchers adopted three questions from the Pusmendik Kemdikbud class VIII to test students' computational thinking skills. All questions were validated by three mathematics education experts and tested on 5 grade VIII students before being used. In this study, four components of computational thinking: abstraction, pattern recognition, algorithmic thinking, and generalization were used to analyze students' computational thinking skills. This study focused on exploratory examination of pupils’ computational thinking with high categories, researchers selected 3 students on FI cognitive style and 2 students on FD cognitive style. The results showed that students with a FI cognitive style were able to meet all indicators of computational thinking namely abstraction, pattern recognition, algorithmic thinking, and decomposition. In contrast, students with FD cognitive styles are able to meet three indicators of computational thinking: abstraction, pattern recognition and generalization. Thus, it may be said that the ability to think computationally is related to the cognitive style of students.

The purpose of this study was to assess rural students’ computational thinking abilities. The following proofs were observed: (1) Students’ abstraction affected algorithmic thinking skills; (2) Students’ decomposition influenced algorithmic thinking skills; (3) Students’ abstraction impacted evaluation skills; (4) Students’ algorithmic thinking affected evaluation skills; (5) Students’ abstraction impacted generalization skills; (6) Students’ decomposition impacted generalization skills; (7) Students’ evaluation affected generalization skills. Gender differences were observed in the relationship among the computational thinking factors of junior high school students. This included the abstraction-generalization skills; evaluation-generalization skills; and decomposition-generalization skills relationships, which were moderated by the gender of the students. 258 valid surveys were collected, and they were utilized in the study. Conducting the descriptive, reliability, and validity analyses used SPSS software, and the structural equation modeling (SEM) was also conducted through Smart PLS software to assess the hypothetical relationships. There were gender disparities in the correlation among computational thinking components of the junior high school students’ studying in rural areas. Research has shown that male and female students may have different abstractions, evaluations, and generalizations related to computational thinking, with females being more strongly associated than males in non-programming learning contexts. These results are expected to provide relevant information in subsequent analyses and implement a computational thinking curriculum to overcome the still-existing gender gaps and promote computational thinking skills.

The purpose of this study is to assess the computational thinking skills of fourth-grade elementary students within the framework of mathematics instruction. Computational thinking is a crucial 21st-century competency that should be cultivated from an early age, particularly in solving mathematical problems. This study adopts a descriptive quantitative method using a written test instrument developed based on four key computational thinking components: decomposition, pattern recognition, abstraction, and algorithmic processes. The participants in this research were fourth-grade students from a selected elementary school. The results indicate that most students demonstrate a moderate level of computational thinking. Among the assessed indicators, decomposition was the most successfully achieved, while algorithmic thinking was the least mastered. These results highlight the importance of integrating contextual and problem-based learning strategies to further enhance students’ computational thinking abilities in mathematics education.

The purpose of this study is to analyze the effectiveness of investigation group learning model based on Marzano’s instructional framework in improving students’ higher order thinking skills. The method used in this study was a quasi-experimental method with the matching only control group pretest-posttest design. Here, the data were collected through observations, questionnaires, and tests. The tests consisting of limited and extensive tests aim to analyze the effectiveness of the investigation group learning model based on Marzano’s instructional framework in improving students’ higher order thinking skills. The results of the study showed that the majority of students (>75%) have an increased level of higher order thinking skills (critical and creative thinking skills). It was proved by the results of statistical analysis where the sig. value < 0.05 and t(table) > t(count) .Thus, H0 is rejected meaning that there is a significant difference on students’ higher order thinking skills before and after applying the investigation group learning model based on Marzano’s instructional framework in the teaching and learning process. Then, if it is classified, the improvement of students’ critical and creative thinking skills is included into the medium category. Hence, it can be concluded that the investigation group learning model based on Marzano’s instructional framework is effective in improving students’ higher order thinking skills in social studies learning.
 Keywords: investigation group; Marzano’s learning dimensions; higher order thinking skills; critical thinking skills; creative thinking skills

This study aims to provide an overview of the computational mathematical thinking abilities of male and female students in solving mathematical problems and the aspects (abstraction, decomposition, algorithmic thinking, and generalization) that influence these differences. This study uses a qualitative method with a case study approach in a junior high school in Jakarta for the academic year 2023/2024. The participants in this study are 36 students. Data were collected through written examinations and interviews and analyzed using the triangulation technique. The results of the study showed different patterns of computational thinking skills between male and female students. Male students excel in decomposition and algorithmic thinking but are weak in abstraction and generalization. Female students excel in abstraction and decomposition but are weak in algorithmic thinking and generalization. Both male and female students master the context of decomposition for computational thinking skills but lack mastery in the context of generalization. In conclusion, gender differences influence specific aspects of computational thinking skills, indicating the need for tailored educational strategies to address these differences.

The important skill to be applied in the 21st century is computational thinking (CT). Students need to develop computational thinking skills by being trained to solve open-ended problems. The descriptive qualitative method is the method used in this research. This research aims to describe the computational thinking skills of junior high school students in solving open-ended mathematical problems by explaining each component of computational thinking, namely abstraction, decomposition, algorithmic thinking, generalization, and debugging. The subjects of the research were several 8th-grade students from a private junior high school in Surabaya, consisting of 55 students who had been given TKM and TPM. Then, 3 students with different categories who provided unique answers were selected. Student in the low computational thinking category (BK1) do not fulfill all computational components. Student in the moderate category (BK2) have incomplete results in the final solution, and the problem-solving methods provided are also inadequate, resulting in imperfect final outcomes. Student in the high computational thinking category fulfill all computational components. Student (BK3) provides correct and varied problem-solving methods and solutions.
 

This study investigates the effects of different programming tools on the self-efficacy beliefs and computational thinking skills of pre-service elementary mathematics teachers. The growing integration of computational thinking in education necessitates research on how programming education influences teachers’ confidence and problem-solving abilities, particularly in mathematics teaching. The study was conducted over 12 weeks with 47 university students enrolled in an Elementary Mathematics Teacher Education Program. A quasi-experimental research design was employed, with pre-test and post-test measurements to assess the impact of programming education. Participants were divided into two experimental groups: one using a block-based programming environment (Scratch) and the other using a text-based programming language (C#). The structured implementation of programming instruction allowed for a systematic comparison of how different approaches influence computational thinking and self-efficacy in teaching mathematics. The results indicate a strong positive correlation between computational thinking skills and self-efficacy beliefs in mathematics teaching. Both programming tools contributed significantly to the improvement of these skills, demonstrating that exposure to algorithmic thinking and coding enhances teachers’ confidence in their ability to teach mathematics. However, no statistically significant difference was found between the groups, suggesting that both block-based and text-based programming approaches are effective in fostering computational thinking and self-efficacy. These results imply that regardless of the programming tool used, engaging in coding activities strengthens essential teaching competencies. These findings highlight the importance of integrating programming education into teacher training programs to prepare future educators for technology-enhanced teaching environments. Additionally, the study emphasizes the role of programming in developing higher-order thinking skills, such as problem-solving, logical reasoning, and adaptability.

This research aimed to develop teaching materials to improve students' computational thinking skills in solving smart coffee agroforestry problems through machine learning, using the RBL-STEM makerspace. Computational thinking skill goes beyond coding and programming and is related to the students' higher-order thinking skills. This research uses the ADDIE development model in developing the learning materials. The learning material products consist of assessment instruments, students' worksheets, and lesson plans. The research employed questionnaires, validation sheets (including content, construct, programming, and language), and observation sheets to collect data regarding the instruments' effectiveness, practicality, and validity. We evaluated the effectiveness of the teaching materials in a single classroom using a paired-test, examining the significant difference between the pre-test and post-test scores. The research subjects are 42 students of the Science Education Department at the University of Jember for the academic year 2023-2024. The average overall score, including content, construct, programming, and language, is 92.97%. The results show that the learning materials satisfy in all aspects. We did in-depth interviews with some selected students at low, medium, and high levels of computational thinking skills and compared the interview results using NVIVO software to making project maps. Furthermore, the score of paired t-test shows α-value = 0.003< 0.05. We concluded that RBL-STEM makerspace learning materials significantly contribute to the development of students’ computational thinking skills. It implies that the learning materials developed in this research are ready to be used in the learning activities to foster students' computational thinking skills.

Argumentation is a scientific literacy practice focused on developing scientific thinking skills associated with problem-solving. As computing has become an integral part of our world, computational thinking skills are requisite for successful problem-solving. The significant effect of computational thinking applications on the efficacy of scientific literacy practices is increasingly acknowledged. In this article, we propose a framework that conceptualizes the constructivist argumentation as a context for problem-solving by applying five computational thinking dimensions, viz. algorithmic design, decomposition, abstraction, evaluation, and generalization. The framework emphasizes two aspects, students’ problem-solving capability and quality of argumentation. Drawing from the literature on scientific argumentation and problem-solving, we argue that the application of computational thinking dimensions in science learning is currently overlooked in the instructional environment. To nurture higher order thinking skills and to engage effective problem-solvers, our framework incorporates four Computational Thinking-Argumentation design principles to support instructional innovation in the teaching and learning of science at the secondary school level, viz. 1) developing problem-solving competencies and building capability in solving uncertainties throughout scientific inquiry; 2) developing creative thinking and cooperativity through negotiation and evaluation; 3) developing algorithmic thinking in talking and writing; 4) developing critical thinking in the processes of abstraction and generalization.

The continued call for twenty-first century skills renders computational thinking a topical subject of study, as it is increasingly recognized as a fundamental competency for the contemporary world. Yet its relationship to academic performance is poorly understood. In this paper, we explore the association between computational thinking and academic performance. We test a structural model—employing a partial least squares approach—to assess the relationship between computational thinking skills and academic performance. Surprisingly, we find no association between computational thinking skills and academic performance (except for a link between cooperativity and academic performance). These results are discussed respecting curricular mandated instruction in higher-order thinking skills and the importance of curricular alignment between instructional objectives and evaluation approaches for successfully teaching and learning twenty-first-century skills.

This study aims to develop an Interdisciplinary Hypothetical Inquiry (IHI) learning model that is (1) feasible and practical, and (2) determine the effectiveness of this model in improving undergraduate students' higher-order thinking skills (HOTS) and computational thinking skills (CTS) in solving social problems in social science (IPS) education study programs. This research uses a design and development research approach which includes six stages: (1) problem identification, (2) goal description, (3) product design and development, (4) product testing, (5) evaluation of test results, and (6) communication of results. The development of the IHI learning model was tested through (1) feasibility tests by expert lecturers in the field of education, evaluation experts, and social science experts; (2) practicality test through observation of learning implementation and responses from lecturers and student users; and (3) effectiveness test using a quasi-experimental method with a sample of undergraduate students in Social Sciences Education, Yogyakarta State University. The research instruments include expert lecturer review and assessment sheets, HOT and CT test questions, observation sheets on the implementation of the IHI model, and user response questionnaires. The research results show that the IHI learning model (1) is feasible based on the assessment of expert lecturers; (2) practical with an implementation score of 4.54 (very practical), a lecturer response score of 3.9 (very practical), and a student response score of 4.5 (very practical); and (3) potentially effective based on the higher N-Gain HOTS and CTS values in the experimental class (0.63 and 0.56) compared to the control class (0.59 and 0.15), as well as the t-test results with significance value 0.00 (p < 0.05). The student-centered IHI learning model encourages collaboration and democratic learning through the stages: 1) problem orientation, (2) hypothesis brainstorming, (3) hypothesis development, (4) investigation design, (5) investigation data collection, (6) interpretation of investigation data, (7) reporting and communication of results. This research concludes that the IHI learning model is feasible, practical, and effective for increasing HOTS and CTS for students in the social studies education study program.

Computational thinking is thinking process that is needed in formulating problems and solutions, so that these solutions can be effective information processing agents in solving problems. Indicators of computational thinking consist of problem decomposition, algorithmic thinking, pattern recognition, abstraction and generalization. To improve higher-order thinking skills, we apply RBL learning integrated with STEM approach. To improve students’ thinking skills, it is necessary to develop tools that support the success of learning activities. The learning tools that have been developed meet the criteria of valid, practical, and effective. The validity scores obtained by each device are 3.5 for the face-to-face plan, 3.41 for the student worksheet, and 3.56 for the learning outcomes test. The observation result of the learning implementation score was 3.8 with a percentage of 95%. There were 23 students who completed or around 88,46%, percentage of average score of student activities was 94.17%, and as many as 94.47% of students gave a positive response.

Computational thinking is thinking process that is needed in formulating problems and solutions, so that these solutions can be effective information processing agents in solving problems. Indicators of computational thinking consist of problem decomposition, algorithmic thinking, pattern recognition, abstraction and generalization. To improve higher-order thinking skills, we apply RBL learning integrated with STEM approach and their aplication to batik matif design. To improve students’ thinking skills, it is necessary to develop tools that support the success of learning activities. The learning tools that have been developed meet the criteria of valid, practical, and effective. The validity score obtained on each device is 3.58 for the student assignment plan (RTM), 3.47 for the student worksheet (LKM), and 3.64 for the learning outcomes test (THB). The observation result of the learning implementation score was 3.72 with a percentage of 93%. In addition to being valid and practical, the material also meets the criteria for effectiveness. On average, 95% of students in this trial class are classified as complete students and the response from students is positive. Based on the test results, researchers got 23 students who scored above 60. This means that 82% of students in this class have completed and met one of the effectiveness criteria. Student response questionnaires also give more positive responses than negative responses.

A study on the relationship between student' learning engagements and higher-order thinking skills in programming learning