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Similarities and differences between CT and mathematical thinking. Adapted from Sneider et al. (2014).
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This paper examines the growing field of computational thinking (CT) in education. A review of the relevant literature shows a diversity in definitions, interventions, assessments, and models. After synthesizing various approaches used to develop the construct in K-16 settings, we have created the following working definition of CT: The conceptual...
Context in source publication
Context 1
... (2005) defined mathematical thinking as global across many problems and "… governs one's ways of understanding" (p. 31). Mathematical thinking consists of three parts: beliefs about math, problem solving processes, and justification for solutions. The main commonality between CT and mathematical thinking is problem solving processes (Wing, 2008). Fig. 1 shows the full set of shared concepts of computational and mathematical thinking: problem solving, modeling, data analysis and interpretation, and statistics and ...
Citations
... Örneğin, öğrencilere gerçek dünya problemleri sunulduğunda, BİD tekniklerini kullanarak bu problemlere yenilikçi ve etkili çözümler geliştirebilirler. Bu süreçte, öğrenciler hem bireysel hem de grup çalışmaları aracılığıyla iş birliği yapma ve iletişim kurma becerilerini de geliştirme fırsatı bulurlar (Shute, Sun, & Asbell-Clarke, 2017). Ayrıca, bilgi işlemsel düşünme, öğrencilere matematiksel kavramların daha derinlemesine anlaşılmasını sağlar. ...
ÖZET
Bu çalışma, bilgi işlemsel düşünme (BİD) kavramının matematik eğitimi ile entegrasyonunu ve bu entegrasyonun öğretim süreçleri üzerindeki etkilerini incelemektedir. Çalışmanın amacı, BİD'nin matematik eğitiminde nasıl bir rol oynadığını ve bu sürecin öğretmenler ve öğrenciler üzerindeki etkilerini değerlendirmektir. Çalışma kapsamında, BİD'nin matematiksel kavramların anlaşılmasına, problem çözme becerilerinin gelişimine ve teknoloji destekli öğrenme ortamlarının kullanımına olan etkileri incelenmiştir. Araştırmanın bulguları, BİD'nin matematik eğitimine entegrasyonunun öğrencilere problem çözme, soyutlama ve algoritma geliştirme gibi önemli becerileri kazandırdığını göstermektedir. Ayrıca, BİD tabanlı etkinlikler, öğrencilerin matematiksel kavramları daha iyi anlamalarını ve bu kavramları günlük hayatla ilişkilendirmelerini sağlamaktadır. Öğretmenler ise BİD tabanlı pedagojik uygulamaları kullanarak teknoloji ve bilgisayar destekli eğitim araçlarını daha etkili bir şekilde kullanabilmişlerdir. Bu süreçte öğretmenlerin karşılaştığı zorluklar ve bu zorlukların nasıl aşılabileceği konusunda önemli bilgiler elde edilmiştir. Sonuç olarak, BİD'nin matematik eğitimine entegrasyonu, öğrencilerin analitik ve yaratıcı düşünme becerilerini geliştirmekte ve öğretmenlerin pedagojik uygulamalarını zenginleştirmektedir. Bu çalışma, BİD'nin eğitimdeki önemini vurgulamakta ve öğretim stratejilerinin geliştirilmesine yönelik değerli öneriler sunmaktadır.
ABSTRACT
This study examines the integration of computational thinking (CT) into mathematics education and its impact on teaching processes. The aim of the study is to evaluate the role of CT in mathematics education and its effects on both teachers and students. The study investigates the impact of CT on the understanding of mathematical concepts, the development of problem-solving skills, and the use of technology-supported learning environments. The findings of the research show that the integration of CT into mathematics education equips students with essential skills such as problem-solving, abstraction, and algorithm development. Additionally, CT-based activities help students better understand mathematical concepts and relate these concepts to daily life. Teachers have been able to use technology and computer-supported educational tools more effectively by applying CT-based pedagogical practices. This process has provided significant insights into the challenges faced by teachers and how these challenges can be overcome. In conclusion, the integration of CT into mathematics education enhances students' analytical and creative thinking skills and enriches teachers' pedagogical practices. This study emphasizes the importance of CT in education and offers valuable suggestions for the development of teaching strategies.
... Lee et al. (2023) describe abstraction as the filtering out of unnecessary detail, ignoring potential distractions, and prioritising the most relevant information while algorithmic thinking involves step by step planning and the generation of rules to follow in the correct order with clear start, end and in-between steps. This is similarly described by Shute et al. (2017) as systematic testing. ...
Theories linked to embodied cognition emphasise the importance of kinaesthetic learning in shaping higher-order cognitive processing. By spreading the cognitive load to other senses while still contributing to central schema, embodied learning can build long-term memory and engagement, especially in young children, leading to improved performance. This approach is particularly suited to computational thinking (CT) which is arguably the critical digital literacy skill of the twenty-first century. This article reports on the co-design of a digital exhibit in collaboration with a Science Discovery Centre (SDC) intended to encourage CT in young children , underpinned by an embodied cognition approach incorporating whole-body actions. Each stage of the exhibit co-design process was informed by research into embodied cognition as well as competencies associated with CT, namely deconstruc-tion, pattern identification, abstraction, and algorithmic thinking, over a 12-month period from conception to fruition. The article describes how theories surrounding embodied cognition and CT were enacted in the realisation process. It concludes by considering the value of co-designing with industry partners such as the SDC, as well as the value of learning designs incorporating kinaesthetic learning especially in relation to abstract concepts such as CT, as embodied knowledge can lead to the construction of enriched mental representations whereby new information is not just seen and heard but is connected to information from the physical environment.
... A previous study of the general-domain category stated that computational thinking was a basic concept foundation through algorithmic methods, to solve realworld problems and achieve specific solutions (Israel-Fishelson et al., 2021). This thinking skill is subsequently required across various contextual disciplines (Günbatar, 2019;Shute et al., 2017), with its relevance needed for a wider range of courses, including science, arts, and humanities (Kalelioʇlu, 2015). Regarding the perspectives of cognitive psychology, the specific domain prioritizes computational thinking as a skill related to a particular field, such as computer (Lai, 2019). ...
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.
... However, it was Wing (2006) who gave impetus to research into CT by emphasizing the importance of developing this ability in children (Knie et al., 2022;Menolli & Neto, 2022). Although this topic is not new to the scientific community, several gaps remain regarding CT, in particular the existence of varied definitions for CT skills (Ausiku & Matthee, 2021;Román-González et al., 2018) and, as a result, the diversity of instruments used to analyze CT skill levels based on these differing definitions (Shute et al., 2017). CT is often associated with computer science as a necessary skill for using technology, which is increasingly present in the 21 st century (Kılıç et al., 2021;Li et al., 2024b). ...
... They highlighted that computing skills will be essential for all students' futures, regardless of whether they pursue computer science. Shute et al. (2017) point out the importance of having a valid and reliable tool that can be used to analyze CT skills, regardless of the subject in which it is integrated. The same authors also highlight the difficulty of analyzing CT competence levels to determine not only the success of interventions but also the progress of students throughout the intervention, identifying this as a gap in CT research. ...
Computational thinking (CT) is an essential mathematical skill for problem-solving and students’ future lives. It is integrated into the educational curricula of several countries, including Portugal. Therefore, pre-service teachers (PST) must possess didactic knowledge to effectively develop CT in students. The aim of this study encompassed three main objectives: translating and adapting the computational thinking scale into Portuguese (study 1), validating the scale (study 2), and assessing the perceived levels of CT competencies among PST in Portuguese university students while examining differences between undergraduate and master’s level PST (study 3). The sample consisted of study 1 with 43 participants and study 2 and study 3 with 382 participants. In study 1, temporal stability was assessed indicating strong stability. The internal consistency showed good homogeneity of the items. The exploratory factor analysis revealed consistency with the structure of the original scale. In conclusion, the Portuguese version of the CT scale demonstrates adequate psychometric properties, proving valid and reliable for assessing CT in university students. Additionally, significant differences were observed between undergraduate and master’s degree students, underscoring the importance of tailored training programs to meet the specific needs of undergraduate students.
... Much research has been carried out on CT in K-12 settings (Lu et al., 2022). This includes conceptualizations and delineations of CT (Shute et al., 2017), CT instruction (Hsu et al., 2018), and CT assessment (Tang et al., 2020). Although there is substantial evidence regarding CT in K-12 settings, research on CT in higher education is only gradually evolving (Zhang et al., 2024). ...
... A core set of CT facets can be identified that specifies this concept (Lyon & Magana, 2020;Shute et al., 2017). Table 1 summarizes this core set. ...
... Although there is an (increasing) interest in CT assessment in higher education, issues remain about the methodological rigor and systematic evaluation of interventions. Shute et al. (2017) point to the Computational Thinking test (CTt) as an internationally established standardized CT assessment instrument. Román-González et al. (2017 have developed and validated a CTt for secondary students. ...
Technological advancements, particularly in artificial intelligence, significantly transform our society and work practices. Computational thinking (CT) has emerged as a crucial 21st-century skill, enabling individuals to solve problems more effectively through an automation-oriented perspective and fundamental concepts of computer science. To ensure the effective integration of CT into educational curricula, it is crucial to develop efficient assessment frameworks that allow teachers to measure and promote student CT proficiency. Therefore, our aim is to develop a short test to measure CT among undergraduate students. To this end, we consider two performance tests: the Computational Thinking test (CTt) and the Algorithmic Thinking Test for Adults (ATTA). We use items from both instruments to compile a short test. Based on a sample of 290 second-year non-computer science undergraduate students, we provide evidence on the quality of our test. Besides classical test theory, we apply item response theory, namely Rasch modeling, and confirmatory factor analysis. Our test shows favorable properties, e.g., Cronbach’s alpha > .75, and may be suitable for the efficient assessment of CT across higher education programs.
... Identifying recurring structures or trends to gain insights. Pattern recognition is crucial in data analysis and prediction [30]. ...
Computer vision education is increasingly important in modern technology curricula; yet, it often lacks a systematic approach integrating both theoretical concepts and practical applications. This study proposes a staged framework for computer vision education designed to progressively build learners’ competencies across four levels. This study proposes a four-staged framework for computer vision education, progressively introducing concepts from basic image recognition to advanced video analysis. Validity assessments were conducted twice with 25 experts in the field of AI education and curricula. The results indicated high validity of the staged framework. Additionally, a pilot program, applying computer vision to acid–base titration activities, was implemented with 40 upper secondary school students to evaluate the effectiveness of the staged framework. The pilot program showed significant improvements in students’ understanding and interest in both computer vision and scientific inquiry. This research contributes to the AI educational field by offering a structured, adaptable approach to computer vision education, integrating AI, data science, and computational thinking. It provides educators with a structured guide for implementing progressive, hands-on learning experiences in computer vision, while also highlighting areas for future research and improvement in educational methodologies.
... Central educational frameworks, such as the National Generation Science Standards (NGSS), have emphasized the critical role of computational thinking, positioning it as a pivotal tool for interpreting natural phenomena within science education that goes beyond the mere acquisition of coding skills (Shute et al., 2017;Weintrop et al., 2016). ...
This qualitative case study investigated how computational models can help students engage in scientific practice and influence their emotional, epistemic, and conceptual aspects. Twenty-four sixth-graders were guided to conduct scientific practices as they predicted and modified the computational models on food web using StarLogo Nova. Three types of qualitative data were collected—video recordings, the researcher’s field notes (teacher’s diary), and post-interviews with students—and three themes about ‘Act like scientists’ were created inductively using the constant comparative method. The use of computational models was found to influence three primary aspects of students’ scientific practices: emotional, epistemic, and conceptual. Regarding the emotional aspect, the students enthusiastically engaged in scientific practices while experiencing awe, an emotion that enhances learning and encourages focus on something larger than oneself. Regarding the epistemic aspect, the students actively evaluated and revised their food web model by redesigning codes. Lastly, they formulated theories for how to improve the sustainability of the food web, which improved their conceptual understanding. Based on the findings, we discussed how computational models can be meaningfully utilized in science classes.
... Computational thinking (CT) has emerged as a fundamental skill in the digital era, enabling individuals to solve complex problems by applying logical reasoning, abstraction, and algorithmic strategies (Shute et al., 2017). However, despite its growing importance, there remains a gap in how to effectively cultivate CT skills, ...
This study aimed to compare the effectiveness of the experiential learning cycle (ELC) and self-regulated learning (SRL), both implemented through a game-based approach (AI 2 Robot City board game), in fostering computational thinking (CT) and understanding of artificial intelligence (AI) applications in university level. The sample consisted of 63 first-year students, divided into two groups: 31 students using ELC and 32 students using SRL. The study was conducted over a 12-hour session. The Jansen-Neyman method was utilized to analyze the interaction between pretest scores and instructional design. Results revealed a significant interaction between these instructional approaches and pretest performance, impacting learning outcomes related to logical thinking and AI anxiety. Specifically, SRL demonstrated greater efficacy in improving delayed learning achievement compared to the ELC, highlighting its importance in promoting long-term retention. However, ELC is recommended for students exhibiting higher initial AI anxiety or lower perception of CT.
... In STEM education, CT is a thinking process where "solutions are represented in a form that can be effectively carried out by an information-processing agent" (Wing, 2011, p. 1). CT revolves around illstructured problems, which are complex real-world challenges with solutions that are neither clearly defined nor easily measurable (Shute et al., 2017). An example of a CT problem is the design of a medical device that ensures accurate and prompt drug delivery, allows nurses to set doses efficiently and accurately, and provides a user-friendly interface for both patients and nurses while systematically and rigorously balancing the trade-off between input speed and error prevention (Curzon et al., 2014). ...
To address the limitations of general-purpose artificial intelligence (AI) tools, we developed a task-oriented AI chatbot based on the 5E (i.e. “engage”, “explore”, “explain”, “elaborate” and “evaluate”) model to scaffold students’ instructional design process. We examined the impact of integrating the 5E instructional model-informed AI chatbot on students’ learning performance and perceptions. The results indicated that the AI chatbot, when combined with human teacher scaffolding, significantly improved the students’ instructional design performance relative to receiving human teacher scaffolding only. The chatbot provided valuable suggestions on instructional design frameworks, class activities and teaching topics during the “explore” phase. In the “evaluate” phase, the chatbot offered immediate feedback on the students’ design plans and proposed alternative instructional frameworks regarding areas for improvement. However, the students expressed concerns about the chatbot’s evaluation quality, noting that it needed to be better aligned with the course assessment rubric. We recommend using AI chatbots for instructional design conceptualisation, although we emphasise the critical role of human teachers in evaluating final design work and providing timely support.
... However, it was Wing (2006) who gave impetus to research into CT by emphasizing the importance of developing this ability in children (Knie et al., 2022;Menolli & Neto, 2022). Although this topic is not new to the scientific community, several gaps remain regarding CT, in particular the existence of varied definitions for CT skills (Ausiku & Matthee, 2021;Román-González et al., 2018) and, as a result, the diversity of instruments used to analyze CT skill levels based on these differing definitions (Shute et al., 2017). CT is often associated with computer science as a necessary skill for using technology, which is increasingly present in the 21 st century (Kılıç et al., 2021;Li et al., 2024b). ...
... They highlighted that computing skills will be essential for all students' futures, regardless of whether they pursue computer science. Shute et al. (2017) point out the importance of having a valid and reliable tool that can be used to analyze CT skills, regardless of the subject in which it is integrated. The same authors also highlight the difficulty of analyzing CT competence levels to determine not only the success of interventions but also the progress of students throughout the intervention, identifying this as a gap in CT research. ...
Computational thinking (CT) is an essential mathematical skill for problem-solving and students' future lives. It is integrated into the educational curricula of several countries, including Portugal. Therefore, pre-service teachers (PST) must possess didactic knowledge to effectively develop CT in students. The aim of this study encompassed three main objectives: translating and adapting the computational thinking scale into Portuguese (study 1), validating the scale (study 2), and assessing the perceived levels of CT competencies among PST in Portuguese university students while examining differences between undergraduate and master's level PST (study 3). The sample consisted of study 1 with 43 participants and study 2 and study 3 with 382 participants. In study 1, temporal stability was assessed indicating strong stability. The internal consistency showed good homogeneity of the items. The exploratory factor analysis revealed consistency with the structure of the original scale. In conclusion, the Portuguese version of the CT scale demonstrates adequate psychometric properties, proving valid and reliable for assessing CT in university students. Additionally, significant differences were observed between undergraduate and master's degree students, underscoring the importance of tailored training programs to meet the specific needs of undergraduate students.