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This study aims to investigate the relationship between the personal traits and computational thinking skills of second graders within the context of robotics activities.

Through literature review, a research model and hypotheses were tested with 122 second graders after robotic activities.

The hypothesized model showed that learning preference, intrinsic motivation and self-efficacy were the main predictors of coding achievement and computational thinking skills, while no direct relationship was found between learning preference, intrinsic or extrinsic motivation. The final path analysis revealed that intrinsic and extrinsic motivation predict self-efficacy, self-efficacy predicts coding achievement and coding achievement predicts computational thinking skills. Another important finding was the strong impact of self-efficacy on coding achievement, as well as computational thinking skills. Results are interpreted with reference to implications for potential methods of improving computational thinking skills when using robotics in the lower grades in elementary schools.

This study not only examined these relationships but also proposed, tested and built a research model containing a wide range of personal traits based on path analysis and multiple regression analysis, which, to the best of the researchers’ knowledge, has not been investigated in the current literature.

As reflected in the final research model, self-efficacy played an important role in impacting second grader’s coding achievement and computational thinking skills.

Few studies have investigated the various relationships in the context of robotics instruction in elementary schools as in this study. Given the increasing popularity of robotics education in elementary schools, the re-examination and identification of the pivotal role of self-efficacy in predicting second graders’ learning of coding and computational thinking skills have important implications for the implementation of robotics education.

This paper aims to present a novel pedagogical model that aims at bridging creativity with computational thinking (CT) and new media literacy skills at low-technology, information-rich learning environments. As creativity, problem solving and collaboration are among the targeted skills in twenty-first century, this model promotes the acquisition of these skills towards a holistic development of students in primary and secondary school settings. In this direction, teaching students to think like a computer scientist, an economist, a physicist or an artist can be achieved through CT practices, as well as media arts practices. The interface between these practices is imagination, a fundamental concept in the model. Imaginative teaching methods, computer science unplugged approach and low-technology prototyping method are used to develop creativity, CT, collaboration and new media literacy skills in students. Furthermore, cognitive, emotional, physical and social abilities are fostered. Principles and guidelines for the implementation of the model in classrooms are provided by following the design thinking process as a methodological tool, and a real example implemented in a primary school classroom is described. The added value of this paper is that it proposes a pedagogical model that can serve as a pool of pedagogical approaches implemented in various disciplines and grades, as CT curriculum frameworks for K-6 are still in their infancy. Further research is needed to define the point at which unplugged approach should be replaced or even combined with plugged-in approach and how this proposed model can be enriched.

This paper presents a pedagogical model that aims at bridging creativity with CT, collaboration and new media literacy skills.

The proposed model follows a pedagogy-driven approach rather a technology-driven one as the authors suggest its implementation in low-tech, information-rich learning environments without computers. The added value of this paper is that it proposes a novel pedagogical model that can serve as a pool of pedagogical approaches and as a framework implemented in various disciplines and grades. A CT curriculum framework for K-6 is an area of research that is still in its infancy (Angeli et al., 2016), so this model is intended to provide a holistic perspective over this area by focusing how to approach the convergence among CT, collaboration and creativity skills in practice rather than what to teach. Based on literature, the authors explained how multiple moments impact on CT, creativity and collaboration development and presented the linkages among them. Successful implementation of CT requires not only computer science and mathematics but also imaginative capacities involving innovation and curiosity (The College Board, 2012). It is necessary to understand the CT implications for teaching and learning beyond the traditional applications on computer science and mathematics (Kotsopoulos et al., 2017) and start paying more attention to CT implications on social sciences and non-cognitive skills. Though the presented example (case study) seems to exploit the proposed multiple moments model at optimal level, empirical evidence is needed to show its practical applicability in a variety of contexts and not only in primary school settings. Future studies can extend, enrich or even alter some of its elements through experimental applications on how all these macro/micromoments work in practice in terms of easiness in implementation, flexibility, social orientation and skills improvement.

The added value of this paper is that it joins learning theories, pedagogical methods and necessary skills acquisition in an integrated manner by proposing a pedagogical model that can orient activities and educational scenarios by giving principles and guidelines for teaching practice.

Despite the popularity of the Computational Thinking (CT) paradigm and the call for broad social diffusion of CS fundamentals, the authors argue that the concept is inherently limited and limiting and does not sufficiently convey an understanding of how to enable people to create with computational technologies. The authors suggest an alternate paradigm, procedural creativity, that calls for the development of conceptual creative spaces governed by procedurally generative principles. The authors also call for game development to be the focus of procedural creativity pedagogy.

The authors first discuss the limitations of the CT paradigm, focusing, in particular, on the issue of abstraction and representation as opposed to execution and action. The authors then define procedural creativity in more detail. Following that, they discuss the use of game development as pedagogy, with a focus on Margaret Boden’s notion of conceptual creative spaces.

CT is limited because it focuses overly on solutions to computational “problems”, because it is tied too closely with economic concerns and because it focuses on abstraction at the cost of action. Procedural creativity, on the other hand, focuses on the individual’s capacity for personal expression with the computer and on the generative capacity of code in action. Game development is in ideal platform for procedural creativity because it emphasizes the development of creative domains and conceptual spaces.

This paper offers a challenge to the CT status quo and presents a novel way forward for understanding computation as a creative practice.

This paper aims to identify computational thinking (CT) in 4th to 6th grade students in the context of project-based problem-solving while engaged in an after-school program.

This case study approach was selected due to its suitability for answering “how” or “why” questions about real-world phenomena within a set context (Creswell and Poth, 2018; Yin, 2018). This was an appropriate fit given the context of an after-school program and the research question asked how to identify learners’ demonstrated CT through project-based learning hands-on activities and problem-solving in a naturalistic environment.

Results show that heuristics, algorithms and conditional logic were observed more than other components of CT such as data collection, simulations and modeling. Descriptions of common activities in a naturalistic learning environment are presented to illustrate how the students practiced CT over time, which could help readers develop an understanding of CT in conjunction with hands-on problem-solving activities in elementary students. Identifying and classifying CT in this study focused on students’ learning process.

This study contributes to the challenging field of evaluating CT while focusing on observable behaviors and problem-solving activities with various degrees of teacher’s facilitation instead of final artifacts. Implications for researchers and educators interested in integrating CT in K-12 learning and its assessment are discussed.

This study aims to explore the effects of an alternative learning environment, such as the video game making (VGM) within science content, on computational thinking (CT) skills development and student performance.

A didactic intervention was performed for five weeks. Two student groups were taught the same computational concepts in two ways. One group was taught by constructing a video game within science content to practice science and computing curriculum while the other group constructed appropriately designed projects to practice only the computing curriculum. Additionally, the students constructed a pretest project before the beginning of the intervention and a post-test project after its end. Results were based on quantitative and qualitative code analysis and interviews from the students.

VGM within science content resulted in projects with more CT skills and also supported students to effectively apply their acquired coding skills, after the end of the intervention.

The results of this study suggest an interdisciplinary environment, such as the VGM within science content, which can effectively support CT skills development and computing curriculum.

Although VGM has been successfully applied to teach science content, this study explored the potential influence of this learning environment on CT skills development and coding fluency. Such interdisciplinary educational environments could be applied in the typical school settings to promote a plethora of skills and academic contents.

Computational thinking (CT) is widely considered to be an important component of teaching generalizable computer science skills to all students in a range of learning environments, including robotics. However, despite advances in the design of robotics curricula that can teach CT, actual enactment in classrooms may often fail to reach this target. This study aims to understand whether the various instructional goals teachers’ hold when using these curricula may offer one potential explanation for disparities in outcomes.

In this study, the authors examine results from N = 206 middle-school students’ pre- and post-tests of CT, attitudinal surveys and surveys of their teacher’s instructional goals to determine if student attitudes and learning gains in CT are related to the instructional goals their teachers endorsed while implementing a shared robotics programming curriculum.

The findings provide evidence that despite using the same curriculum, students showed differential learning gains on the CT assessment when in classrooms with teachers who rated CT as a more important instructional goal; these effects were particularly strong for women. Students in classroom with teachers who rated CT more highly also showed greater maintenance of positive attitudes toward programming.

While there is a growing body of literature regarding curricular interventions that provide CT learning opportunities, this study provides a critical insight into the role that teachers may play as a potential support or barrier to the success of these curricula. Implications for the design of professional development and teacher educative materials that attend to teachers’ instructional goals are discussed.

Advancements in technology require that everyone is skilled with computational thinking (CT), problem-solving and computer programming skills. This study aims to examine the development of CT in problem-solving skills (PSS) and programming learning attitude by integrating LEGO robotics kits in a project-based learning course.

This study examines the development of CT in PSS and programming learning attitude by integrating LEGO robotics kits in a project-based learning course. This study consists of a single group pre-post-test research design with 32 freshmen university students. Quantitative and qualitative data were collected by pre-post-tests and recording of classroom discussions, respectively.

Therefore, this finding implies that robotics can be used to develop CT in university students; however, there is a need for designing curricula with advanced robotic kits as artificial intelligence (AI) has become more prevalent. Hence, programming knowledge learned will help students to understand the application of robots in AI.

The study creates educators' awareness that CT skills might be developed in freshmen university students through robotics. However, many still consider them toys rather than learning aids.

The purpose of this paper is to examine the intersection of artificial intelligence (AI), computational thinking (CT), and mathematics education (ME) for young students (K-8). Specifically, it focuses on three key elements that are common to AI, CT and ME: agency, modeling of phenomena and abstracting concepts beyond specific instances.

The theoretical framework of this paper adopts a sociocultural perspective where knowledge is constructed in interactions with others (Vygotsky, 1978). Others also refers to the multiplicity of technologies that surround us, including both the digital artefacts of our new media world, and the human methods and specialized processes acting in the world. Technology is not simply a tool for human intention. It is an actor in the cognitive ecology of immersive humans-with-technology environments (Levy, 1993, 1998) that supports but also disrupts and reorganizes human thinking (Borba and Villarreal, 2005).

There is fruitful overlap between AI, CT and ME that is of value to consider in mathematics education.

Seeing ME through the lenses of other disciplines and recognizing that there is a significant overlap of key elements reinforces the importance of agency, modeling and abstraction in ME and provides new contexts and tools for incorporating them in classroom practice.

Many mathematical models have been shared to communicate about the COVID-19 outbreak; however, they require advanced mathematical skills. The main purpose of this study is to investigate in which way computational thinking (CT) tools and concepts are helpful to better understand the outbreak, and how the context of disease could be used as a real-world context to promote elementary and middle-grade students' mathematical and computational knowledge and skills.

In this study, the authors used a qualitative research design, specifically content analysis, and analyzed two simulations of basic SIR models designed in a Scratch. The authors examine the extent to which they help with the understanding of the parameters, rates and the effect of variations in control measures in the mathematical models.

This paper investigated the four dimensions of sample simulations: initialization, movements, transmission, recovery process and their connections to school mathematical and computational concepts.

A major limitation is that this study took place during the pandemic and the authors could not collect empirical data.

Teaching mathematical modeling and computer programming is enhanced by elaborating in a specific context. This may serve as a springboard for encouraging students to engage in real-world problems and to promote using their knowledge and skills in making well-informed decisions in future crises.

This research not only sheds light on the way of helping students respond to the challenges of the outbreak but also explores the opportunities it offers to motivate students by showing the value and relevance of CT and mathematics (Albrecht and Karabenick, 2018).

This study is part of a participatory design research project and aims to develop and study pedagogical frameworks and tools for integrating computational thinking (CT) concepts and data science practices into elementary school classrooms.

This paper describes a pedagogical approach that uses a data science framework the research team developed to assist teachers in providing data science instruction to elementary-aged students. Using phenomenological case study methodology, the authors use classroom observations, student focus groups, video recordings and artifacts to detail ways learners engage in data science practices and understand how they perceive their engagement during activities and learning.

Findings suggest student engagement in data science is enhanced when data problems are contextualized and connected to students’ lived experiences; data analysis and data-based decision-making is practiced in multiple ways; and students are given choices to communicate patterns, interpret graphs and tell data stories. The authors note challenges students experienced with data practices including conflict between inconsistencies in data patterns and lived experiences and focusing on data visualization appearances versus relationships between variables.

Data science instruction in elementary schools is an understudied, emerging and important area of data science education. Most elementary schools offer limited data science instruction; few elementary schools offer data science curriculum with embedded CT practices integrated across disciplines. This research assists elementary educators in fostering children's data science engagement and agency while developing their ability to reason, visualize and make decisions with data.