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Educational robots have unrivaled advantages and value in developing students' computational thinking. Currently, there are fewer studies on the overall effects of educational robots on K12 students' computational thinking, especially at the instructional style and cross-grade level. In order to investigate the overall effect of educational robots on students' computational thinking, the study used a meta-analytic approach to analyze 27 experimental and quasi-experimental research papers from the last decade. It was found that: educational robots have a positive and significant effect on K12 students' computational thinking development (SMD = 0.558), and their application effects are influenced by moderating variables such as school level, teaching style, participation style, experimental period, and sample size. Specifically: educational robots have a positive and significant effect on K12 students, with the most significant effect at the early childhood level; there is a significant difference in the development of students' computational thinking when using educational robots in different teaching modes, with the best effect being achieved in the design-based teaching mode; (3) participating in educational robots in small groups is more likely to promote the development of computational thinking in K12 students; (4) Different experimental cycles have a positive effect on students' computational thinking, but a long experimental cycle does not mean a good effect, etc.; (5) There is a significant difference between different sample sizes of educational robots on K12 students' computational thinking, and arranging a medium-sized (50–100 students) educational robotics activity can achieve a better learning effect. [ABSTRACT FROM AUTHOR]

In the context of our rapidly digitizing society, computational thinking stands out as an essential attribute for cultivating aptitude and expertise. Through the prism of computational thinking, learners are more adeptly positioned to dissect and navigate real-world challenges, poising them effectively to meet the exigencies of future societal landscapes. The paradigm of project-based learning, heralded as a potent educational methodology, significantly amplifies students' prowess in computational thinking. To delineate the nuanced interplay between project-based learning and its augmentation of computational thinking, we engaged in meta-analytical investigations, synthesizing outcomes from 31 distinct external experimental and quasi-experimental studies. Our analytical journey underscored that project-based learning markedly elevates students' competencies across five critical spectra: innovation, collaboration, critical analysis, algorithmic cognition, and problem resolution. Dissecting its impact across the K12 continuum reveals that primary students predominantly hone their collaborative acumen; middle-grade learners witness a pronounced ascent in creativity, critical, and algorithmic faculties, while the apex of high school education primarily sharpens critical analytical prowess. Thus, educators sculpting the contours of project-based learning should be calibrate their strategies, ensuring they resonate with the developmental and age-specific nuances of their audience, to fully unleash its latent potential in nurturing computational thinking. [ABSTRACT FROM AUTHOR]

This study investigated the effect of project-based Arduino robot application on students' computational thinking skills development in robotics programming course using the quasi-experimental research design. The participants consist of 73 students in tertiary institutions in South-East Nigeria that offer Computer and Robotics Education programme. There was no sampling as the entire second-year trainee teachers of computer and robotics education in the selected institutions participated in the study. The instrument used for data collection was the Robotics Programming Computational Thinking Scale (RPCTS). Mean and standard deviation were used to answer the research questions, while Analysis of Covariance (ANCOVA) was used to test the hypotheses at an alpha level of 0.05. The result of the study showed that students who were taught using Project-based Arduino Robot Application (PARA) had higher mean computational thinking scores than those taught using the conventional lecture method, and there was a significant difference in the mean computational thinking scores of students based on method of instruction. Also, the male students taught robotics programming using PARA had higher mean computational thinking scores compared to their female counterparts, and there was a significant difference in the mean computational thinking scores of male and female students taught robotics programming using PARA. Schools and other stakeholders in education should support the training of teachers on how to develop and used project-based approaches in teaching/learning robotics programming-related courses. [ABSTRACT FROM AUTHOR]

The integration of computational thinking (CT) into primary education is often facilitated using one or more CT tools, such as block-based programming environments and educational robotics. A major concern is that these CT tools often are used to design mathematics classroom activities that focus on CT at the expense of mathematics. Hence, there is a need to investigate more closely how CT tools can be used in primary mathematics classroom activities in ways that enable a stronger focus on the learning of mathematics. Using information ecology as a theoretical lens, this study aims to understand how and why CT tools are integrated into primary mathematics classrooms, and how teachers value the possible contributions of such tools. We draw on multiple interviews with two primary teachers, recordings of planning sessions where classroom activities that include CT were designed, the classroom implementations themselves, and reflective conversations with the teachers after the CT tools were integrated in their mathematics classrooms. A deductive analytical approach to our data revealed that (1) CT tools, to varying degrees, facilitate the learning of mathematics; (2) some CT tools were valued by teachers as a better 'fit' than others; and (3) CT tools are primarily used to support the learning of geometry, excluding other mathematical domains. Based on these findings, we suggest that there is a need for more research on the use of different CT tools and their role in the learning of primary mathematics. Moreover, more research is needed to understand how CT tools can be used in topics other than geometry. [ABSTRACT FROM AUTHOR]

Computational thinking (CT) has received growing interest as a research subject in the last decade, with research contributions attempting to capitalize on the benefits that CT may provide. This study included a systematic analysis aimed at revealing current trends in the CT subject, identifying educational interventions, and emerging assessment instruments. It also gave an overview of how teachers learned CT skills and how they integrated the CT curriculum into classroom practices. We searched the data in the Web of Science database and identified 360 articles. Most importantly, it emphasized the following points: (a) the most popular subject areas in CT literature; (b) CT intervention tools; (c) CT assessment practices used so far within educational courses; and (d) effective CT approaches to influence pre-service teachers. Results from this review revealed that CT's promotion in education had achieved significant progress in recent years. Along with the growth in the number of CT studies, the number of subjects, research questions, and teaching approaches also increased in recent years. It was also found that CT was mostly used in science, mathematics, programming, and computer science tasks, with little work in artificial intelligence (AI) and non-STEM areas. The essence of this paper implicated the researchers in designing the curriculum based on different subject domains. Furthermore, we recommended integrating augmented reality-based games using CT methodologies into the curriculum. [ABSTRACT FROM AUTHOR]

Oil refineries play a crucial role in meeting global energy demands, and optimizing the efficiency of critical processes is vital for economic feasibility and environmental sustainability. Simulation is an essential tool for the optimization of valuable products. This work presents the rigorous simulation of a vacuum distillation unit (VDU) based on actual data from the vacuum distillation processes using Aspen HYSYS V10. The Peng–Robinson fluid package is used in this simulation, and an input assay with a standard density of 29 API_60 (879.8 kg/m3) is employed. True boiling point (TBP) assay data are the type that is being used. Methane, ethane, propane, i-Butane, n-Butane, i-Pentane, and n-Pentane are the components listed in the simulation. The research determines that achieving a yield capacity of 685 tons/h requires thirty stages in the atmospheric distillation unit and twelve stages in the vacuum distillation unit while operating at 420 °C temperature and 9 kPa pressure. Adjustments in the flash section temperature (FST) and steam flow rate (SFR) are proposed to enhance operational efficiency. Increasing the FST from 370 °C to 400 °C and adjusting SFR from 10 tons/h to 26 tons/h increases the Light Vacuum Gas Oil (LVGO) yield by 7.2% while elevating the FST from 400 °C to 430 °C and adjusting SFR from 10 tons/h to 26 tons/h enhances the High Vacuum Gas Oil (HVGO) yield by 7.4%. These optimization strategies offer a practical and effective approach for refineries to improve the economic benefits of vacuum distillation units. The implications of this research can act as a computational thinking exercise for higher education students considering the case study where only through changing the operational strategies can the yield be enhanced by 10.81% in the vacuum distillation unit of the oil refinery. [ABSTRACT FROM AUTHOR]

The Precipitating Change Project was a 5-year development, implementation, and research study of an innovative 4-week middle school curricular unit in computational weather forecasting that integrates students' learning and use of meteorology and computational thinking (CT) concepts and practices. The project produced a list of CT skills and definitions that students use to predict the weather, CT assessment instruments, and a CT classroom observation protocol. Data was collected from 306 eighth grade (ages 13–14) students in rural indigenous communities in the Artic and urban and suburban Northeast communities in the USA. The project met its goal of producing an intentional instructional sequence that integrates disciplinary science and CT practices to increase students' science knowledge and their ability to use CT skills and processes. The results indicate that teachers were able to use the curriculum to embed CT practices into the classroom. Students, in turn, had the opportunity to practice using these skills in class discussion as evidenced by the classroom observation data, and students' science knowledge of CT content and practices significantly increased as evidenced by their performance on the weather content and CT skills pre- and post-assessments. While statistically significant gains in science knowledge and CT skills and practices were evident in all settings (urban, suburban, and rural indigenous communities), there were noticeable differences in gains in students' CT skills and practices between the three settings and additional research is needed in a diversity of settings to understand this difference. [ABSTRACT FROM AUTHOR]

Academics, scholars, and learners are increasingly turning to AI-based language models like ChatGPT for a variety of academic and non-academic applications including essay writing, speech creation, literature summarizing, and idea production. However, the use of ChatGPT in academic research has sparked debate and raised concerns about its influence on research and publishing. By offering an actual scenario and suggestions, this study intends to shed light on the practical implementation of ChatGPT in academic research. The dataset for this study includes 3,860 students from Thailand's basic and secondary school levels. The survey data gathered focuses on investigating innovative thinking skills. The findings suggest that ChatGPT might be a useful technique for generating first ideas in academic studies. However, it is crucial for researchers to exercise caution as they may encounter certain challenges during the process. As a result, while using ChatGPT in academic research, researchers must be aware of these issues and make educated judgments appropriately. Given the possible uses and ramifications of ChatGPT, the academic community must develop detailed protocols governing its usage in research and subsequent publishing. These rules will ensure the proper and ethical use of ChatGPT in the academic setting. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence–generated content (AIGC) has stepped into the spotlight with the emergence of ChatGPT, making effective use of AIGC for education a hot topic. Objectives: This study seeks to explore the effectiveness of integrating AIGC into programming learning through debugging. First, the study presents three levels of AIGC integration based on varying levels of abstraction. Then, drawing on extended effective use theory, the study proposes the underlying mechanism of how AIGC integration impacts programming learning performance and computational thinking. Methods: Three debugging interfaces integrated with AIGC by ChatGPT were developed for this study according to three levels of AIGC integration design. The study conducts a between‐subject experiment with one control group and three experimental groups. Analysis of covariance and a structural equation model are employed to examine the effects. Results and Conclusions: The results show that the second and third levels of abstraction in AIGC integration yield better learning performance and computational thinking, but the first level shows no difference compared to traditional debugging. The underlying mechanism suggests that the second and third levels of abstraction promote transparent interaction, which enhances representational fidelity and consequently impacts learning performance and computational thinking, as evidenced in test of the mechanism. Moreover, the study finds that learning fidelity weakens the effect of transparent interaction on representational fidelity. Our research offers valuable theoretical and practical insights. Lay Description: What is currently known about the subject matter?: In the present information era, programming and computational thinking are important.AIGC has attracted remarkable attention from both academics and managers.If it is appropriately utilised, AIGC can facilitate education. What this paper adds: Three forms of AIGC integration based on the level of abstraction, which enhance programming learning and computational thinking.Application of extended effective use theory to propose an underlying mechanism for how AIGC integration affects learning performance and computational thinking.Concrete information on the utilisation of AIGC in the education domain.Evidence that shows the importance of interaction transparency and representational fidelity for leveraging information technologies in education. Implications of the study findings for practitioners: AIGC can be an effective tool for teachers, learners, and institutions.Platform designers and teachers should carefully design AICG integration.Platform designers and teachers could make use of various methods and other forms of AIGC integration to promote interaction transparency. [ABSTRACT FROM AUTHOR]

Background: A resurgence in teaching coding in primary school classrooms has led to a pedagogical swing towards using physical computing and coding to develop students' use of algorithms, computational thinking, and problem‐solving skills. Two obstacles impede the optimal development of these objectives: the availability of a suitable pedagogy and an instructional sequencing model for primary school teachers to effectively present coding and computational thinking concepts and skills to students in alignment with their developmental stage. Objective: This study aims to address both obstacles by introducing the 3C Model, a newly developed instructional sequence grounded in established pedagogies and designed to effectively teach coding and computational thinking skills to primary school students based on their developmental stage. Methods: The qualitative study employed two data sources to triangulate findings, using: (1) semi‐structured interviews and thematic analysis to investigate 11 primary school students' perceptions of their learning experiences with the 3C Model, and (2) researcher observations along with reflections of the students' developed and demonstrated learning through the method of knowing‐in‐action, reflection‐in‐action, and reflection‐on‐action. Results and Conclusions: The findings of this study fill a gap in the existing literature by demonstrating that the pedagogical and sequential approach embedded in the 3C Model not only enhanced students' engagement levels but also resulted in improved curriculum learning outcomes. The 3C Model provides teachers with a coherent and age‐appropriate instructional structure. It uses physical computing devices and digital coding platforms to introduce coding concepts, furthering the development of computational thinking skills in primary school students beyond mere procedural and rote learning. Implications: The study holds important implications for practical applications, as it addresses an absence in the literature of an established pedagogy and instructional sequencing model for effectively teaching coding and computational thinking concepts and skills to primary school students. Drawing on established pedagogical and developmental learning theories, the 3C Model provides primary school teachers with an engaging, age‐appropriate instructional method that avoids decontextualised teaching and surface‐based learning. Instead, it encourages collaborative student work and contextualised learning, steering away from isolated and generic approaches. Lay Description: What is currently known about this topic: Teaching coding and computational thinking concepts and skills is complex due to their abstract nature.Teaching resources such as work samples, curriculum connections and tools are available to primary school teachers.There is no consensus on the most effective methods to teach these abstract concepts and skills.Primary school teachers default to applying general teaching strategies due to the lack of an age‐appropriate, specific activity sequencing model to use during coding instruction. What does this paper add: The study fills the existing gap by presenting a newly conceptualised instructional model, which considers the students' developmental stage of learning and pedagogical best practices.The study trialled the 3C Model with primary school students in a classroom setting.The study found that employing the pedagogical approach of the 3C Model increased students' engagement levels and resulted in enhanced curriculum learning outcomes. Implications for practitioners: Without an age‐appropriate instructional sequencing model students may not be grasping the underlying concepts.Simply focusing on the code without emphasising concepts such as decomposition and algorithmic thinking will result in shallow learning.Key considerations in using the 3C Model are ensuring that conceptual development occurs using physical computing devices and language students are familiar with prior to introducing coding language and its symbolic representations.Primary school teachers should consider incorporating physical interactive digital devices, physical movement, staged language development and subsequent coding procedures within the context of an engaging and authentic problem. This approach enables students to effectively grasp and represent coding and computational thinking concepts and skills, surpassing mere procedural and rote learning. [ABSTRACT FROM AUTHOR]

Background: Science teachers' understanding of the roles of computing practices in science frame how they enact scientific computational practices in their teaching and how their students perceive the relationship between computational practices and scientific endeavours. Objectives: This critical, integrative review synthesizes teacher learning literature about the role of computational literacy and computing practices in K‐12 science teaching. Methods: We examined 54 peer‐reviewed articles and analysed the ways the researchers and teacher participants describe the affordances of integrating computational thinking (CT) and other computational practices in science. We characterize how CT and computational practices are framed in relation to scientific learning goals. We identify six primary affordances for integrating computational practices with science that are conveyed to teachers and by teachers, as represented in these studies of teacher learning. Results and Conclusions: These six perspectives include (1) learning computer science principles, (2) developing CT dispositions, (3) engagement and inclusion in science, (4) taking ownership of science, (5) supporting learning science content, and (6) participating in computational practice as a form of scientific epistemic practice. Our analysis indicates that computational thinking and computational practices are often integrated in science in order to teach something about computing (e.g., Perspective 1), rather than to support learners' scientific work. Only the 29 articles coded for the sixth perspective—that is, in service of epistemic aims in science—demonstrate commitment to students' uses of computational ideas and practices as epistemic tools to participate in the sensemaking work of science. Takeaways: Comparison of Perspectives 5 and 6 illustrates the nuance between computational practices in science that reify something students have already "figured out," rather than those that serve epistemic goals. Perspective 6 encapsulates the deep synergy among (1) the reflexive nature of computing with scientific ideas and (2) computing as a central practice in science and engineering. We contend that a more focused message of computational practices in service of scientific sensemaking goals is necessary if we expect teachers to enact CT and related computational practices in their classrooms. Lay Description: What is already known about this topic?: Science teachers' understanding of the roles of computing practices in science frame how they enact scientific computational practices in their teaching. What this paper adds?: We analysed 54 peer‐reviewed articles regarding teacher learning about the role of computational literacy and computing practices in K‐12 science teaching.We characterized the ways the researchers and teacher participants describe the affordances of integrating computational thinking (CT) and other computational practices in science.We present six primary affordances for integrating computational practices with science that are conveyed to teachers and by teachers, as represented in these studies of teacher learning. These six perspectives include (1) learning computer science principles, (2) developing CT dispositions, (3) engagement and inclusion in science, (4) taking ownership of science, (5) supporting learning science content, and (6) computational practices in service of epistemic aims in science. Implications for practice and/or policy: We find that computational thinking and computational practices are often integrated in science in order to teach introductory computer science principles, but not to support the original science learning aims.Only the 29 of 54 articles demonstrate commitment to students' uses of computational ideas and practices as epistemic tools to participate in the sensemaking work of science.We contend that a more focused message of computational practices in service of scientific sensemaking goals is necessary if we expect teachers to enact CT and related computational practices in their classrooms. [ABSTRACT FROM AUTHOR]

This research aimed to determine the level of influence of computational and visual thinking on the learning of mathematics in first-year students in the professional business management career. The methodology is quantitative and has a cross-sectional non-experimental design. The sample consists of 76 students from the accounting program, chosen probabilistically by clusters. A 20-item questionnaire is applied for the independent variables and a test to evaluate mathematics learning. The results show that 66.41% and 67.57% of the students have developed their computational and visual thinking, respectively, to a regular and acceptable level. Likewise, there is a high positive correlation of mathematics learning with computational and visual thinking. It is concluded that the use of computational and visual thinking during the performance of mathematical activities significantly influences the conceptual, procedural and attitudinal learning of mathematics. [ABSTRACT FROM AUTHOR]

Background: Computational thinking (CT) is crucial to fostering critical thinking and problem-solving skills. Many elementary schools have been cultivating students' CT through block-based programming languages such as Scratch using traditional teacher-centered teaching methods. However, the approach excessively relies on teacher lectures, so the teacher's teaching load is hefty, and most students cannot receive timely assistance from teachers, thus reducing students' programming learning performance, interest, and confidence. Therefore, this study designs a Jigsaw collaborative learning method implemented in a metaverse virtual space (JCLM-MVS) for peer-to-peer Scratch programming learning to promote learners' programming learning performance, CT, and learning attitudes. This study used a quasi-experimental research method, with 48 fifth-grade students from two classes in Taiwan's elementary school as the research participants. One class of 24 students was randomly assigned to the experimental group using JCLM-MVS to conduct Scratch programming learning, whereas the other class of 24 students was assigned to the control group using the traditional teacher-centered teaching method. Main findings: The study found that the experimental group of learners showed significantly better Scratch programming learning performance and attitude than the control group, and there was no statistically significant difference in CT between both groups, but both learning approaches effectively promoted CT. Additionally, the interview results showed that most interviewees stated that using JCLM-MVS for Scratch programming learning could receive timely assistance from peers, make collaborative learning more efficient and learning more enjoyable, and more intend to use JCLM-MVS for Scratch programming learning than using traditional teacher-centered teaching method due to the effective collaborative interaction mechanisms and realistic learning space provided in the metaverse virtual space. Conclusions/potential implications: This study presents a novel and engaging learning approach by integrating a metaverse virtual space with the Jigsaw collaborative learning method, referred to as JCLM-MVS, which enhances the effectiveness of the Jigsaw collaborative learning method in promoting Scratch programming learning performance, CT, and attitudes. [ABSTRACT FROM AUTHOR]

The computing and creative skills of students in Indonesia are still low since the government has not focused on student creativity and computational empowerment programs. This research aims to develop a science, technology, engineering, art, mathematics, and reflection (STEAMER) hybrid learning project model for teachers' creative and computational thinking abilities, as well as analyze elementary school teacher candidates' perceptions of the use of STEAMER hybrid learning model to improve teachers' creative and computational thinking abilities. This research is development research with an analysis, design, development, implementation, and evaluation (ADDIE) model. The instruments used in this study were questionnaires and interviews with experts, lecturers, and elementary school teacher candidates. The research was conducted at eight universities in Indonesia with a total sample of 100 elementary school teacher candidates. Through quantitative and qualitative data analysis, the research results have developed the STEAMER hybrid learning project model based on learning theory, syntax, social systems, support systems, and the instructional impact of learning models. The results of the validation show that the developed learning model is feasible in terms of model, material, media, and language experts. The model is suitable for elementary school teacher education. Furthermore, based on the perceptions of the teacher candidates, it is stated that the STEAMER hybrid learning project can develop the ability of the teacher candidates to think creatively and computationally. [ABSTRACT FROM AUTHOR]

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Copyright of Modern Educational Technology is the property of Editorial Board of Modern Educational Technology, Tsinghua University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Artificial intelligence (AI) has demonstrated significant potential in addressing educational challenges in digital learning. Despite this potential, there are still concerns about the interpretability and trustworthiness of AI methods. Dynamic Bayesian networks (DBNs) not only provide interpretability and the ability to integrate data-driven insights with expert judgment for enhanced trustworthiness but also effectively process temporal dynamics and relationships in data, crucial for early predictive modeling tasks. This research introduces an approach for the temporal modeling of learners' computational thinking abilities that incorporates higher-order influences of latent variables (hereafter referred to as memory of the model) and accordingly predicts learners' performance early. Our findings on educational data from the AutoThinking game indicate that when using only first-order influences, our proposed model can predict learners' performance early, with an 86% overall accuracy (i.e., time stamps 0, 5, and 9) and a 94% AUC (at the last time stamp) during cross-validation and 91% accuracy and 98% AUC (at the last time stamp) in a holdout test. The introduction of higher-order influences improves model accuracy in both cross-validation and holdout tests by roughly 4% and improves the AUC at timestamp 0 by roughly 2%. This suggests that integrating higher-order influences into a DBN not only potentially improves the model's predictive accuracy during the cross-validation phase but also enhances its overall and time stamp-specific generalizability. DBNs with higher-order influences offer a trustworthy and interpretable tool for educators to foresee and support learning progression. [ABSTRACT FROM AUTHOR]

This study investigates the potential of a voice-controlled collaborative robot to promote computational thinking (CT) skills in preschoolers aged 4 to 6 in Southern Chile. During a week-long workshop, 34 children engaged in activities designed to develop several CT components while interacting with a robotic arm. Pre- and post-workshop assessments utilizing the TechCheck-K tool revealed significant improvements in CT skills. The findings of this preliminary study demonstrate the effectiveness of voice-controlled robots as innovative tools in early education, significantly enhancing young learners' understanding of CT concepts, practices, and perspectives. This research advocates for further exploration of robotic integration in educational settings, highlighting their potential to improve or extend preschool learning environments and foster CT skill development. [ABSTRACT FROM AUTHOR]

As professional science becomes increasingly computational, researchers and educators are advocating for the integration of computational thinking (CT) into science education. Researchers and policymakers have argued that CT learning opportunities should begin in elementary school and span across the K‐12 grades. While researchers and policymakers have specified how students should engage in CT for science learning, the success of CT integration ultimately depends on how elementary teachers implement CT in their science lessons. This new demand for teachers who can integrate CT has created a need for effective conceptual tools that teacher educators and professional development designers can use to develop elementary teachers' understanding and operationalization of CT for their classrooms. However, existing frameworks for CT integration have limitations. Existing frameworks either overlook the elementary grades, conceptualize CT in isolation and not integrated into science, and/or have not been tested in teacher education contexts. After reviewing existing CT integration frameworks and detailing an important gap in the science teacher education literature, we present our framework for the integration of CT into elementary science education, with a special focus on how to use this framework with teachers. Situated within our design‐based research study, we (a) explain the decision‐making process of designing the framework; (b) describe the pedagogical affordances and challenges it provided as we implemented it with a cohort of pre‐ and in‐service teachers; (c) provide suggestions for its use in teacher education contexts; and (d) theorize possible pathways to continue its refinement. [ABSTRACT FROM AUTHOR]

As online learning has become an inevitable trend in the post-peak era of the COVID-19 pandemic, distributed pair programming (DPP) is gaining momentum in both education and industry. DDP serves as a collaborative programming approach and also benefits the development of computational thinking, a fundamental skill in today's world. This study conducted a systematic review of studies on DPP published after 2010 to understand the themes and factors that impact the team effectiveness of DPP and thus inform future research and practices on how to better leverage this approach for teaching and learning. The results showed that individual characteristics attracted major investigations in the selected 23 studies, including prior programming experience, actual skill, perceived skill, gender, personality, time management, confidence, and self-esteem, with pair compatibility identified as a critical team design factor that significantly affects programmers' satisfaction. Although the feel-good factor in the team process was investigated, no significant impact was found. Under the team environment theme, we compared different opinions on the orientation (e.g., scripted roles) and the use of technology (e.g., integrated development environment tools). Future research should investigate how task structure influences team effectiveness of DPP and relates to computational thinking education. Additionally, because most studies were conducted in higher education contexts, more research in primary and secondary educational contexts is also needed. [ABSTRACT FROM AUTHOR]

As computational methods are widely used in science disciplines, integrating computational thinking (CT) into classroom materials can create authentic science learning experiences for students. In this study, we classroom-tested a CT-integrated geoscience curriculum module designed for secondary students. The module consisted of three inquiry investigations that were age-appropriately translated from the computational practices of volcanologists who study tephra hazards and risks. We developed a domain-specific, block-code-based computational modeling environment where students carried out 1 of the 3 science practices in each inquiry investigation: experimentation, data visualization and interpretation, and modeling. We examined (1) student learning outcomes using pre- and post-tests and (2) student reflections on the computationally supported science practices surveyed at the end of each inquiry investigation during the module. Results indicate that students made statistically significant gains in science content as well as in computationally supported experimentation, data visualization and interpretation, and modeling practices. Over 80% of students identified different ways in which block coding supported their practices during inquiry investigations. Based on these findings, we discuss implications for the future development of computationally supported inquiry investigations in science. [ABSTRACT FROM AUTHOR]

Children's preschool years are crucial for the development of computational thinking (CT) skills. However, debate continues regarding whether CT should be developed through plugged-in or unplugged activities. This study investigated the similarities and differences between plugged-in and unplugged activities with similar learning content and assessment methods for cultivating computational thinking (CT) in young children. Twenty-four young children (aged 5–6 years) from a kindergarten in Foshan, China, were randomly assigned to either the plugged-in or unplugged group to participate in a five-week study. In the plugged-in group, Dodobot was used in the classroom, while in the unplugged group, unplugged materials such as paper, pencil and tangram puzzles were used. Research results indicate that 1) both plugged-in and unplugged activities significantly improved the young children's CT skills after a short-term educational intervention, but there were no significant differences between the two groups; 2) the extent to which the plugged-in and unplugged activities promoted subdimensions of CT was different, with the plugged-in group demonstrating significant improvements in hardware, algorithm, and modularity and the unplugged group demonstrating significant improvements in terms of representation; 3) the children from both the plugged-in and unplugged groups showed high motivation; And 4) the children in both the plugged-in and unplugged groups showed cooperative behaviors. The frequency of cooperative behavior was more related to materials, and cooperation quality was more related to teacher guidance. [ABSTRACT FROM AUTHOR]

AbstractCreativity and problem-solving are 21st-century skills that we need to develop in our students. Research on computational thinking (CT) integration in school subjects and STEAM activities has shown a positive effect on students’ problem-solving skills and creativity. Our study extends our previous work from the integration of CT in a mathematics lesson into a STEAM lesson to develop students’ creativity. Our Math + CT activities have been developed through educational design research (EDR), and now we have added the context of arts (Batik), science (beeswax and coloring), and engineering (3D modeling and printing). Online learning was conducted with pre-service teachers and high school students. We facilitated digital fabrication by asking students and pre-service teachers to create their Batik stamp pattern from 2D to 3D to be printed. Data were collected from participants’ GeoGebra and 3D files they had created. We analyzed the data descriptively and presented participants’ creations. The result showed that students could extend their limited coding and programming knowledge to create the Batik patterns. Moreover, within a limited time, the artwork they designed significantly differs from the initial figure introduced to them. We also presented the realistic 3D-printed Batik stamp and how it worked and our limitations. [ABSTRACT FROM AUTHOR]

The International Challenge on Informatics and Computational Thinking Bebras is a tool used in Colombia to measure students' skills in Computational Thinking, and is an opportunity to reflect on how to improve educational practices using new tools and technology. Science and engineering education focuses on learning and developing skills through the assimilation of content; however, to prepare students for today's world, it is important to stimulate critical and computational thinking. Bebras Colombia had the participation of 2751 and 5191 students between 8 and 21 years old in the years 2021 and 2022 respectively. The results showed that there are easy and difficult pillars by category, and the strongest correlations between pairs of pillars were identified. Significant differences were also found by gender and type of educational institution. La introducción del desafío Bebras en Colombia representa una medida que promueve el fortalecimiento de competencias en Pensamiento Computacional, una habilidad esencial para afrontar los desafíos del presente y del porvenir. Asimismo, resulta de gran relevancia que el sistema educativo se ajuste a las innovaciones tecnológicas y recursos disponibles con el propósito de formar a los estudiantes en consonancia con las demandas del entorno laboral y la sociedad en su conjunto. [ABSTRACT FROM AUTHOR]

Currently, equity of access to computer science education represents a challenge: computer science education is indispensable for active participation in a computational world, but current initiatives tend to increase existing gaps due to cultural, socioeconomic and contextual factors. This paper proposes a model for equitable computing education that contributes to the reduction of these gaps through three main strategies: (I) Investigating how students learn in particular contexts; (II) Building teacher capacity to teach computing through professional development programs, while investigating how teachers learn, and how they succeed (or fail) in bringing it to the classroom; and (III) Designing accessible devices for teaching computing. This accessibility includes devices that are low-cost, but that are also versatile, in terms of their appearance, the ways they interact, and the concepts they teach. This article describes the model for equitable computer education, and shows a case study for each of the strategies. [ABSTRACT FROM AUTHOR]

The digital transformation of the last decades has brought many changes and challenges for education, teachers must be alert to all these developments and be in a continuous updating, therefore, it is important to create spaces where they can be trained and thus acquire technical and pedagogical skills to apply in their classes and thus strengthen the computational thinking of their students. Among these technical skills we find decomposition, pattern generalization, abstraction, and algorithmic thinking. Many research and efforts were found that are being made so that from the classroom the teacher contributes to student learning through computational thinking, these researches involve children, adolescents and young people, it is a creative challenge for everyone, as for those who disseminate these new techniques as for those who receive and apply them in all instances not only in the computational. In this article we sought to identify the experience of higher education teachers in their classrooms, to identify the strategies they use and how they perceive their students when receiving instruction; it is identified that most students still expect to receive masterly training, they are not very self-taught and as for the perception of the topics they tend to memorize and forget, since they do not use very often the explicit long-term memory where they make learning conscious and the implicit memory where they learn through experience, both can be strengthened by applying computational thinking. Students should be taught to analyze from computational thinking all the problems of the different subjects and thus their memorization will be explicit conscious, they will not forget, and they will be able to face many challenges of their area of study every day. [ABSTRACT FROM AUTHOR]

Teaching computational thinking in K-12 education has gained notable prominence in recent years. The main objective of these initiatives is not to teach programming from childhood but to strengthen the ability to reason, solve problems, and develop cognitive processes. For this reason, studying the cognitive effects and impacts that teaching computational thinking has on students has become a field of action with multiple challenges. Seeking to contribute to this new field of study, this pilot test evaluated the effect of a computational thinking training program on the executive functions of school-age children. The results showed that the intervention in computational thinking improved some tasks associated with the executive functions of inhibition, visuospatial working memory, and planning. [ABSTRACT FROM AUTHOR]

With the advent of natural language processing models, the way we usually refer to human language, of Large Language Models (LLM) type, such as Bidirectional Encoder Representations from Transformers (BERT), Language Model for Dialogue Applications (LaMDA), Large Language Model Meta Artificial Intelligence (LLaMA), or the so called Generative Pre-trained Transformers (GPT), users and the general public are beginning to generate any number of speculations and expectations about how far thy can go, and are also starting to explore new ways of use, some realistic, others creative, and others verging on fantasy. Computational Thinking is no exception to this trend. This is why it is of utmost importance to try to elaborate a clear vision about this innovative technology, seeking to avoid the creation and propagation of myths, which only make the perception and understanding that we have, and to try to find the right balance in terms of the reaches that can have this type of technological trends, and the ways of use that can be given to them, with special emphasis on Computational Thinking. In this paper we present a brief analysis of what Generative Pre-trained Transformer is, as well as some reflections and ideas about the ways in which Large Language Models can influence Computational Thinking, and their possible consequences. In particular, in this paper, we analyze the well-known ChatGPT, presenting an evaluation of the generated text outputs, and its credibility for its use in tasks of common use for Computational Thinking, such as performing an algorithm, its code and solving logical problems. [ABSTRACT FROM AUTHOR]

Some subjects are considered to have a high degree of difficulty in students of primary and upper secondary levels. Usually, they are mathematics and physics. Both have become a challenge throughout the planet as the pedagogical demands are becoming increasingly complex in today's society. Prioritizing the theoretical part has always been typical because it improves the ability to generalize and reason new knowledge. However, it hardly contributes to deductive and critical thinking. In addition to this, currently, young people have yet to interest in theoretical subjects. Their attitude needs to be more active, which does not help them want to participate. In order to promote a change of attitude in the youth of section 31 of the 4th semester of the Ing. Pascual Ortiz Rubio High School of the Michoacana University of San Nicolás de Hidalgo, Mexico, a computational thinking workshop (PC). The objective was to generate motivation and stimulation of learning. It is known that the PC stimulates the ability to solve problems with its methodology, including creativity, abstraction, reasoning, and critical thinking. For this first workshop, 17 young people participated, whose performance improved throughout the activity, and later, they reduced the time and obtained deductions and automation according to what was planned. At the end of the workshop, the young people were overjoyed, motivated and knew the power of deductive and critical thinking. At this point, the workshop is considered to have met its objective. [ABSTRACT FROM AUTHOR]

Computational thinking emerges as a field of knowledge due to the increasing importance of technology and computing in today's society. It has been demonstrated that computational thinking can enhance problem-solving abilities by employing techniques from computer science to develop solutions for complex problems. It often involves the application of mathematical principles, such as algebra, geometry, and statistics. Mathematics is fundamental to computational thinking as it provides the theoretical and conceptual foundation for many computer science techniques. Mathematical concepts like functions, sets, and graph theory are essential for understanding algorithms and data structures. The aim of this study was to analyze the integration of mathematics and computational thinking and how this relationship has been measured using both connected and disconnected activities in primary school students. A total of 302 articles were reviewed, applying inclusion and exclusion criteria related to the study's variables of interest. The review found that most articles integrate mathematics and computational thinking based on their interconnectedness, using classroom strategies that support both fields of knowledge. Eleven articles propose activities encompassing four mathematical components (geometric, metric, spatial, and numeric) to address computational thinking, with the geometric component being the most commonly utilized. Only seven articles included students at the 0th grade level, and only two articles worked with populations from Central (Panama) and South America (Uruguay), suggesting that computational thinking is in its early stages in our continent, excluding North American countries where a higher number of articles are reported. [ABSTRACT FROM AUTHOR]

The purpose of this literature review was to perform an analysis of Computational Thinking (CT) definitions, aiming to find points of convergence among the 62 analyzed definitions. The publication year, authors, and country of origin of the first author were considered as variables to understand the behavior of the CT construct, assuming different perspectives proposed from 2006 to 2022. To achieve this purpose, five sources of information were reviewed, and 62 articles that presented a definition of CT were selected. According to the results, the years with the highest number of proposals were 2015 and 2017. The countries with the most publications featuring definitions were the United States, followed by Spain. The most representative authors were Jeannette Marie Wing, Cynthia Selby, and Francisco José García-Peñalvo. Regarding the analysis of the words, two lines stand out: one corresponds to approaches related to informatics, computing, technology, or programming, and the other assumes the absence of these terms. It is important to highlight the prominence of the word "problem(s)," which appeared with a frequency of 89% in the analyzed articles' definitions, and the word "solve(s)" with 54%. In line with the results, it is concluded that there is diversity in the proposals to define CT, making it challenging to find a point of convergence. Therefore, it is concluded that those interested in investigating the construct can adopt the definition that suits their research needs. [ABSTRACT FROM AUTHOR]

Modeling is a key scientific practice and is usually computer-implemented, but it is challenging to teach because it seems high on presuppositions, for example, coding. This design-based research study presents a geography course offered during the 2019/20 winter semester in computer modeling, with no prior coding knowledge. It was implemented in two elective classes: one in the Bachelor of Science program and one in the teacher program. Interviews were conducted to monitor the students' development, and the final reports showed their competencies at the end of the course. Since there were no noticeable differences between the Bachelor of Science students and the teacher trainees in terms of competence acquisition, four learning types could be described based on analysis of the interviews and final reports, which are important for the planning and adaptation of the course concepts. In summary, our results show four key findings: (1) mere reproduction of learning content is of no value for the development of geographic modeling skills; (2) the ability to access conceptual thinking patterns is crucial; (3) process-level understanding is a fundamental prerequisite for advanced geographic modeling understanding; and (4) poor programming skills hinder but do not prevent at least basic modeling understanding. [ABSTRACT FROM AUTHOR]

Computational thinking (CT), an essential 21st century skill, incorporates key computer science concepts such as abstraction, algorithms, and debugging. Debugging is particularly underrepresented in the CT training literature. This multi-level meta-analysis focused on debugging as a core CT skill, and investigated the effects of various debugging interventions. Moderator analyses revealed which interventions were effective, in which situations, and for what kind of learner. A significant overall mean effect of debugging interventions ( g ¯ = 0.64, CI = (0.32, 0.96), p <.001), was found based on 62 effect sizes from 18 source articles. Significant between-studies variation indicated that true effects could range from −0.54 to 1.82. In addition, sensitivity analyses and checks on confounding provided further understandings of intervention features and their impacts. Interventions using enhanced debuggers and systematic instruction were particularly effective in fostering debugging skills. Debugging intervention effects varied by participant population and potentially by publication type. Moreover, debugging interventions had impact regardless of how debugging skills were measured, programming medium used, control-group type, and whether the study was randomized. Future studies should investigate the best practices for improving debugging abilities for whom and under what circumstances. [ABSTRACT FROM AUTHOR]

Computational Thinking (CT) is essential for developing creativity, problem-solving, and digital competence in the 21st century. Coding tools like robotic toys and tablet apps have become popular in early childhood education to support CT development, but there is a debate on which tool is more effective. Little evidence exists on the effect of coding on children's Social-Emotional Competence (SEC), which is crucial for lifelong development and extends beyond cognitive development. This experimental study aimed to compare the effectiveness of two 9-week programs on promoting 73 preschool children's CT and SEC, one using coding robots and the other using coding apps. The results showed that children who participated in the Coding Robot Program had higher CT scores than those in the Coding App Program, after controlling for age, gender, family socioeconomic status, and baseline CT scores. While the SEC scores showed no substantial disparities between the groups, it was revealed that the initial performance levels moderated the intervention effects on emotional regulation and overall SEC. This suggests that the Coding Robot Program could be especially advantageous for a subset of children who initially have difficulties with emotional regulation and social skills. Implications of this study are presented for research and practice. [ABSTRACT FROM AUTHOR]

Copyright of Media4u Magazine is the property of Ing. Jan Chromy, Ph.D. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Circulatory system is a challenging subject for middle school students to learn and understand conceptual relationships. To address these challenges, this study developed plugged (computational thinking activities using computer) and unplugged (computational thinking activities without using computer) teaching modules that integrated computational thinking components into the circulatory system topic. The aim of the study was to investigate the impact of these teaching modules on the conceptual learning and computational thinking skills of 6th grade students. A quasi-experimental design was employed with experimental group 1 using plugged instructional modules, experimental group 2 using unplugged instructional modules and the control group following an inquiry-based constructivist approach aligned with the Turkish science curriculum. These interventions were implemented over a period of 16 lesson hours (4 weeks) for all three groups. The researchers used the Circulatory System Conceptual Learning and Computational Thinking Skills Scale (CSCL-CTSS) as a data collection tool. The results of the study indicated that both experimental group 1 (n = 30) and experimental group 2 (n = 30) showed significantly higher success in circulatory system conceptual learning and computational thinking skills compared to the control group (n = 30). Therefore, this experimental study provides a concrete example of integrating computational thinking into science teaching and presents innovative educational approaches that can enhance the learning of challenging science concepts. Furthermore, it compares the effectiveness of these innovative educational approaches (plugged and unplugged implementations) in developing conceptual learning and computational thinking skills. [ABSTRACT FROM AUTHOR]

Copyright of Modern Educational Technology is the property of Editorial Board of Modern Educational Technology, Tsinghua University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Education Technology Theory & Practice / Egitim Teknolojisi Kuram ve Uygulama is the property of Education Technology Theory & Practice (Journal) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Computational thinking is recognized as a critical competency in contemporary education, preparing individuals to tackle complex challenges in a digitally pervasive world. In this quasi-experimental design study with pretest and post-test measures, the possibility of developing computational thinking from the field of didactics of mathematics in higher education students was investigated. This was performed via a problem-based learning (PBL) methodology using problem solving in the experimental group or, alternatively, focused on the analysis of solved problems in the control group. After the intervention, the control group experienced a statistically significant improvement in the scores obtained in the post-test measure. Thus, PBL and problem solving did not lead to an improvement in the students' computational thinking, whereas the analysis of solved problems approach did. Therefore, the results suggested the potential benefits of this latter methodology for teaching computational thinking. [ABSTRACT FROM AUTHOR]

Copyright of Campus Virtuales is the property of Campus Virtuales and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research examines the application of students' computational thinking (CT) in solving numerical method problems through computer software. Data collection involved observing their learning process and conducting tests to evaluate their CT skills within the context of the root approach material using Newton-Raphson method. The results indicate that the use of Microsoft Excel facilitates problem-solving for students and educators when employing Newton-Raphson method. Furthermore, it helps identify aspects or indicators of CT in students' problem-solving processes. The research findings demonstrate that students with strong mathematical abilities should document their conclusions in the algorithmic aspect. Students with moderate mathematical abilities exhibit all indicators in every aspect of CT when solving problems using Newton-Raphson method. On the other hand, students with weak mathematical skills fail to articulate questions, formulas, or conclusions in the algorithm design aspect, but they do show all indicators in the pattern recognition and abstraction aspects. [ABSTRACT FROM AUTHOR]

Computational Thinking (CT) can be defined as the thought processes involved in formulating problems so that their solutions can be represented as sequential steps and algorithms. It is a key skill for children in the 21st century. However, it is unclear how CT can be developed most effectively in children. Several pedagogical methodologies have been proposed and are being investigated. The main aim of this paper is to test the hypothesis which states that using three‐dimensional (3D) simulated robots helps in the learning of programming and CT concepts, such as directions, loops, conditionals, and functions. The research questions are: Does this hypothesis hold true? Are some concepts easier or better learned than others and to what extent? The goal is to measure and evaluate the effect of using as a learning tool a platform with 3D simulated robots and realistic physics, and compare it with the standard Scratch learning tool which does not use robotics but a two‐dimensional (2D) cartoon avatar they are already familiar with. For practical reasons, a quasiexperimental design with nonequivalent groups and 85 second‐year Secondary Education students (ages 12–13) was performed. They were separated into control and experimental groups and followed a seven‐session intervention with the baseline 2D Scratch and the 3D simulated robots platform, respectively. Both used a visual block programming language and the same activities. To have quantitative and reliable results, a widely accepted CT test has been used, pre‐ and postintervention. Also qualitative feedback is presented. The obtained results show that using the platform with simulated 3D robots significantly helps when developing students' CT. With it, the students do learn basic programming concepts and reach higher scores in the CT test. This improvement applies to all CT‐analyzed concepts except in functions where the grades are maintained. Furthermore, students manage to master the activities on the 3D simulated robots platform, which reflects on the empowerment the platform has got in them. [ABSTRACT FROM AUTHOR]

Educational robots foster engaging, democratic, and collaborative learning environments, enhancing students' mathematical and engineering abilities by enabling them to interact, solve problems, assimilate information, and communicate effectively within the classroom. This study aims to examine the impact of educational robots on the development of engineering mathematics skills. The study uses a quantitative approach with a quasi-experimental design for a single pre- and post-test group. The sample consisted of 40 fifth grade students who initially studied geometry using traditional methods, followed by studying the same content using the educational robot. The study found that robot-based education significantly improved engineering mathematics skills. It was concluded that using educational robotics as a tool for teaching mathematics improved computational thinking, engineering mathematics, motivation, creativity, cooperation, and teamwork. It is possible to argue that the study's results corroborate relevant research showing how educational robots raise students' academic performance and improve their critical thinking. These results lend credence to the idea that educational robots improve learning beyond traditional approaches by actively involving students, giving subjects greater concreteness, and capturing their interest and attention. The study recommends the creation of new mathematical activities using educational robots across various mathematical contents and educational stages, further enhancing engineering mathematics among students. [ABSTRACT FROM AUTHOR]

Computational thinking represents an innovative pedagogical paradigm embraced by numerous academic institutions to cultivate the problem-solving acumen of students. Despite its widespread acceptance across diverse disciplines, the integration of computational thinking within medical education remains incomplete. This study seeks to ascertain the attitudes of medical university students in Saudi Arabia regarding computational thinking. Employing an exploratory research design, the investigation encompassed 394 health sciences students, transcending educational backgrounds and gender, with the objective of gaging their attitudes and awareness concerning the value, learning, and perceived difficulty associated with computational thinking within the context of medical curricula. The findings of the study indicate a notably positive disposition among students enrolled in medicine and health informatics programs toward computational thinking, surpassing the attitudes observed in other health disciplines. This discernible preference underscores the potential for integrating computational learning into medical curricula. The results further reveal a pronounced enthusiasm among students for computational thinking, suggesting a genuine interest in leveraging this approach for clinical problem-based learning. Consequently, it can be posited that the incorporation of computational thinking within medical education and clinical problem-solving scenarios warrants active encouragement in forthcoming educational initiatives.

This paper introduces the DPGraphJ package, a collection of reusable Java functions to solve optimisation problems using a dynamic programming algorithm. The latter is based on a recursive schema that follows a top-down approach and uses the memoisation technique. This algorithm is a reusable software component that is generic and efficient. Moreover, it has been developed by paying special attention to good practices in the design of software. For using DPGraphJ, the problem to be solved needs to be modelled as an AND/OR graph. In the DPGraphJ package, we provide 5 academic case studies with detailed comments. We strongly believe that our proposal can be helpful for several kinds of users, such as students, researchers, and practitioners.

The integration of computational thinking (CT) to enhance creativity in design students has often been underexplored in design education. While design thinking has traditionally been the cornerstone of university design pedagogy and remains essential, the increasing role of digital tools and artificial intelligence in modern design practices presents new opportunities for innovation. By introducing CT alongside design thinking, students can expand their creative toolkit and engage with emerging technologies more effectively. Although many design students may have limited experience with programming, incorporating accessible, no-code tools can help them confidently embrace computational methods, unlocking new pathways for creative exploration and innovation. This study proposes an alternative approach to improve the motivation of design students by introducing machine learning tools into product design processes. We developed an experimental pedagogy in which 56 industrial design university students learned how to use Waikato Environment for Knowledge Analysis (Weka), a machine learning tool, for three hours of design work a week, for a total of eight weeks. Our covariate analysis of data collected in the pretest and posttest shows that the general learning motivations in the group using Weka are significantly higher than those in the group without Weka. However, no significant differences were found between the two groups in terms of learning strategies, collaboration, or critical thinking. Students using Weka spent more time focusing on model training and tended to improve their algorithmic thinking, and the introduction of Weka appeared to enhance their motivation to learn. On the other hand, these students might have been focusing on working individually at their computers, potentially neglecting communication and collaboration. The findings suggest that teaching machine learning applications without requiring coding has the potential to boost design students' motivation to engage with CT skills, though care must be taken to maintain collaborative practices.

This study aims to provide an in-depth overview of trends in Computational Thinking (CT) learning among elementary school students through bibliometric analysis. The development of CT skills, which include logical thinking, systematic problem-solving, decomposition, pattern recognition, abstraction, and algorithmic design, is essential to prepare students for navigating a technology-driven future. By encouraging CT in primary education, students better understand technology and versatile skills that can be applied in various fields. This study uses a bibliometric approach by systematically collecting and analyzing scientific articles on CT in elementary schools. The analysis used specific metrics, including publication trends, citation analysis, and identifying leading research areas. Data is drawn from leading academic databases and analyzed to track the progress and impact of CT research over time. The results show a significant increase in publications and citations on CT, which peaked in 2021. The main trend highlights the importance of visual programming tools like Scratch in improving students' understanding of programming concepts, logic, and motivation. The study also uncovered research gaps and suggested areas that need further investigation, particularly in developing CT curricula and more effective teaching methodologies. These findings are expected to significantly contribute to advancing CT education and help equip the younger generation with the skills needed.

This paper discusses the significance of school curricula in reflecting societal priorities and needs, focusing on the incorporation of computational thinking (CT) in Nordic national curricula. Our point of departure is that the preparedness of future generations for a digitally driven society can be determined by analysing how CT is either explicitly or implicitly framed in school curricula. Accordingly, this study examined the school curricula of Denmark, Finland, and Norway in terms of their similarities and differences in how they framed CT, as these countries have different approaches to the inclusion of CT. A framework for analysis that was grounded in influential works on CT in education was developed, focusing on problem-solving, algorithmic and transversal practices. National-level curricula were examined using a content analysis. Despite the differences in the approaches used in these countries, our findings indicate similarities across all three curricula, with an emphasis on how CT was framed.

As computational thinking (CT) enters school curricula, and research on teaching of CT emerges, the time has come to spotlight CT in teacher education (TE). To this end, we conducted a literature review on CT in TE for STEM subjects with particular focus on research into pre-service teachers’ (PSTs) learning of pedagogical practices. We found 31 articles addressing CT in TE for STEM subjects between 2012 and 2023, applying qualitative, quantitative, and mixed methods, mainly with smaller sample sizes. Almost all describe teaching interventions with research on PSTs’ CT skills or attitudes. Only five articles include research questions explicitly addressing pedagogical practices for learning to teach CT. However, 13 articles explicitly describe such pedagogical practices and another seven implicitly do so. The review shows that the practices for teaching CT is an under-researched area, and the field lacks a common language and systematic research approaches. However, we do find that TE has clear ambitions for teaching PSTs about pedagogical practices as well as CT skills and attitudes.