Assessing first graders’ code literacy: confidence, capability, and gender in robotics education

Educational robotics or unplugged coding activities in kindergartens?: Comparison of the effects on pre-school children's computational thinking and executive function skills

This study investigates how digital game co-creation promotes Computational Thinking (CT) skills among children in sub-urban primary schools. Understanding how CT skills can be fostered in learning programming concepts through co-creating digital games is crucial to determine instructional strategies that match the young students' interests and capacities. The empirical study has successfully produced a new checklist that can be used as a tool to describe the learning of CT skills when children co-create digital games. The checklist consists of 10 core CT skills: abstraction, decomposition, algorithmic thinking, generalisation, representation, socialisation, code literacy, automation, coordination, and debugging. Thirty-six 10–12 year-olds from sub-urban primary schools in Borneo participated in creating games in three separate eight-hour sessions. In addition, one pilot session with five participants was conducted. The game co-creation process was recorded to identify and determine how these young, inexperienced, untrained young learners collaborated while using CT skills. Analysis of their narratives while co-creating digital games revealed a pattern of using CT while developing the games. Although none of the groups demonstrated the use of all ten CTs, conclusively, all ten components of the CT were visibly present in their co-created digital games.

This study investigates the impact of AI-generated contexts on preservice teachers’ computational thinking (CT) skills and their acceptance of educational robotics. This article presents a methodology for teaching robotics based on AI-generated contexts aimed at enhancing CT. An experiment was conducted with 122 undergraduate students enrolled in an Early Childhood Education program, aged 18–19 years, who were training in the Computer Science and Digital Competence course. The experimental group utilized a methodology involving AI-generated practical assignments designed by their lecturers to learn educational robotics, while the control group engaged with traditional teaching methods. The research addressed five key factors: the effectiveness of AI-generated contexts in improving CT skills, the specific domains of CT that showed significant improvement, the perception of student teachers regarding their ability to teach with educational robots, the enhancement in perceived knowledge about educational robots, and the overall impact of these methodologies on teaching practices. Findings revealed that the experimental group exhibited higher engagement and understanding of CT concepts, with notable improvements in problem-solving and algorithmic thinking. Participants in the AI-generated context group reported increased confidence in their ability to teach with educational robots and a more positive attitude toward technology integration in education. The findings highlight the importance of providing appropriate context and support when encouraging future educators to build confidence and embrace educational technologies. This study adds to the expanding research connecting AI, robotics, and education, emphasizing the need to incorporate these tools into teacher training programs. Further studies should investigate the lasting impact of such approaches on computational thinking skills and teaching methods in a variety of educational environments.

To date, extensive work has been devoted to incorporating computational thinking in K‐12 education. Recognizing students' computational thinking stages in game‐based learning environments is essential to capture unproductive learning and provide appropriate scaffolding. However, few reliable and valid computational thinking measures have been developed, especially in games, where computational knowledge acquisition and computational skill construction are implicit. This study introduced an innovative approach to explore students' implicit computational thinking through various explicit factors in game‐based learning, with a specific focus on Zoombinis , a logical puzzle‐based game designed to enhance students' computational thinking skills. Our results showed that factors such as duration, accuracy, number of actions and puzzle difficulty were significantly related to students' computational thinking stages, while gender and grade level were not. Besides, findings indicated gameplay performance has the potential to reveal students' computational thinking stages and skills. Effective performance (shorter duration, fewer actions and higher accuracy) indicated practical problem‐solving strategies and systematic computational thinking stages (eg, Algorithm Design ). This work helps simplify the process of implicit computational thinking assessment in games by observing the explicit factors and gameplay performance. These insights will serve to enhance the application of gamification in K‐12 computational thinking education, offering a more efficient method to understanding and fostering students' computational thinking skills. Practitioner notes What is already known about this topic Game‐based learning is a pedagogical framework for developing computational thinking in K‐12 education. Computational thinking assessment in games faces difficulties because students' knowledge acquisition and skill construction are implicit. Qualitative methods have widely been used to measure students' computational thinking skills in game‐based learning environments. What this paper adds Categorize students' computational thinking experiences into distinct stages and analyse recurrent patterns employed at each stage through sequential analysis. This approach serves as inspiration for advancing the assessment of stage‐based implicit learning with machine learning methods. Gameplay performance and puzzle difficulty significantly relate to students' computational thinking skills. Researchers and instructors can assess students' implicit computational thinking by observing their real‐time gameplay actions. High‐performing students can develop practical problem‐solving strategies and exhibit systematic computational thinking stages, while low‐performing students may need appropriate interventions to enhance their computational thinking practices. Implications for practice and/or policy Introduce a practical method with the potential for generalization across various game‐based learning to better understand learning processes by analysing significant correlations between certain gameplay variables and implicit learning stages. Allow unproductive learning detection and timely intervention by modelling the reflection of gameplay variables in students' implicit learning processes, helping improve knowledge mastery and skill construction in games. Further investigations on the causal relationship between gameplay performance and implicit learning skills, with careful consideration of more performance factors, are expected.

Computational Thinking (CT) has been defined as an important skill for students to have in learning, both from early childhood to college. Besides, computational thinking has a correlates with Taxonomy Bloom. Bloom’s Taxonomy is the basis of learning in Indonesia, so computational thinking needs to be developed further because it is relevant to learning in Indonesia. Computational thinking skills include thinking logically, analyzing the problem-solving process, and evaluating. One tool that can facilitate CT skills is Robotics. In the industrial revolution 4.0, educational robotics became an innovation. Robotics in learning can provide many benefits and motivate students. Then, a systematic literature review is conducted which analyzes previous studies to find information about benefit using robotics based learning and at the level of students’ computational thinking. In the end there are several findings, namely (1) the influence of Computational Thinking Skill in education; (2) the effectiveness of robot-based learning; (3) robotics-based learning can facilitate the development of CT skills in students; (4) robotics-based learning activity design to support computational thinking in early childhood.Computational Thinking (CT) has been defined as an important skill for students to have in learning, both from early childhood to college. Besides, computational thinking has a correlates with Taxonomy Bloom. Bloom’s Taxonomy is the basis of learning in Indonesia, so computational thinking needs to be developed further because it is relevant to learning in Indonesia. Computational thinking skills include thinking logically, analyzing the problem-solving process, and evaluating. One tool that can facilitate CT skills is Robotics. In the industrial revolution 4.0, educational robotics became an innovation. Robotics in learning can provide many benefits and motivate students. Then, a systematic literature review is conducted which analyzes previous studies to find information about benefit using robotics based learning and at the level of students’ computational thinking. In the end there are several findings, namely (1) the influence of Computational Thinking Skill in education; (2) the effectiveness of rob...

With the growing demand for high-tech careers in the 4.0 Industrial Revolution, the 2018 general education curriculum in Vietnam emphasizes career orientation and science, technology, engineering, and mathematics (STEM) education, and integrating robotics into education is crucial for preparing students for future careers. This study examines the impact of a block-based Arduino robotics course on computational thinking (CT) skills and STEM career interests. This study also investigates the perceptions of robotics among Vietnamese upper-secondary students. With a mixed method approach, this study surveyed students’ CT skills and STEM career interests before and after the course, analyzed their products, and interviewed students about the course. Quantitative results indicate significant improvements in all CT areas and STEM career interests. Qualitative data reveal that the course enhanced students’ engagement, allowing them to connect academic concepts with real-world applications, and effectively inspiring their career aspirations in the science and engineering fields. This research supports the value of robotics in STEM education and provides recommendations to enhance course design for better results in CT skills and students’ interest in STEM careers.

Using robot programming activities for learning in the classroom is one way to drive interest and engagement in the STEM field among students, especially girls. And this is a field that is particularly characterized by an underrepresentation of women. Accordingly, many countries are increasingly integrating activities related to computer science concepts into their education systems. The EU also sets the goal of considering the connections between STEM disciplines in schools and having students gain experience with robots as well. The use of robots for teaching purposes creates opportunities for motivating and meaningful mathematics lessons that are linked to the fundamental concepts of computer science. Mathematics teaching in such a context offers possibilities for an experimental and problem-oriented approach to the content and a deep insight into mathematical concepts. Research in this area shows that the use of robots can promote understanding of mathematical concepts, change attitudes and motivation, and develop metacognitive and problem-solving skills. However, as for gender differences in this context, little is known to date. Addressing this gap, for this work, we investigated learners' performance, mathematical and computational ideas and experiences, problem-solving strategies, and help used in an ER (Educational Robotics) activity. In addition, the learners’ mathematical competence and computational thinking skills as well as possible correlations of these measures with the learners’ performance on an ER activity were examined. For these purposes, an ER activity on the topic of plane geometric figures was designed, which was carried out in a 6th grade (11-12 years) class (n=24) of an Austrian middle school in the city of Salzburg using the TI-Innovator Rover. The comparison of six female and six male student groups, each consisting of two students, made it possible to address the above research questions. For this purpose, a mixed-methods approach was chosen. Qualitative data, consisting of the audio recordings of the student groups' conversations during the ER activity, the constructions made on the posters, the student notes, and the saved programs, form the basis for thematic analysis. The quantitative data include the number of tasks solved during the ER activity by the student groups, the mathematics grade of the last school year by the students, and the results of a test on the students' computational thinking skills with the related self-assessments. Appropriate quantitative methods for analysis include the Wilcoxon rank-sum test (Mann-Whitney test), the Welch Two Sample t-test, and Kendall's tau and Pearson's correlation coefficient to test for differences and correlations. The main results indicate that groups with female students perform better while showing high engagement in the activity, exhibit a more systematic approach to problem-solving and at the same time use less intensive help from the teachers than their male counterparts in this class. The paper concludes by giving future directions for research and the limits of the present work.

Currently, educational robotics are becoming an important trend in education, introducing transformative elements into the classroom to improve the learning environment. Educational robotics in STEM-integrated learning can develop computational thinking skills. Educational robotics has begun to be widely adopted and is expected to enhance computational thinking skills in early childhood education, secondary school, and higher education. In this study, we examine the role of educational robotics in integrated STEM learning environments and its impact on the development of computational thinking. The method used was a systematic literature review. Initial search returned 541 articles from various journals indexed in Scopus. Subsequently, 351 articles published between 2020 2024 were sorted out, and only 37 articles were included in the final analysis. Studies show that educational robotics effectively promotes STEM education and facilitates the development of computational thinking skills. The importance of project-based learning and the integration of STEM disciplines in educational robotics inform educators and policymakers about the potential benefits of educational robotics in promoting STEM education and developing computational thinking skills.

This study aims to examine the effects of a Novel Engineering (NE)-based AI education program on elementary school students’ computational thinking and learning engagement. A 12-session instructional program was developed and implemented in a fourth-grade classroom at an elementary school. A mixed-methods research design was employed, incorporating both quantitative and qualitative data. Pre- and post-tests were administered to assess changes in students' computational thinking and learning engagement. In addition, qualitative data was collected through instructional materials and in-depth interviews. Interview participants were selected based on high, medium, and low levels of performance in computational thinking and learning engagement. The interviews focused on how students demonstrated competencies in analysis/design and implementation/inference throughout the learning process. The results indicated that the NE-based AI education program had a statistically significant positive impact on both computational thinking and learning engagement with the students. The students actively engaged in problem-solving activities and found the tasks stimulating, which contributed to the enhancement of their computational thinking skills. Furthermore, the appropriate level of task difficulty, opportunities for autonomous participation, and active communication were identified as factors that fostered greater learning engagement. This study demonstrates the potential for developing and applying effective AI education programs to enhance elementary students’ computational thinking and learning engagement, offering valuable implications for future educational practices.

This paper investigates the relationship between the use of robotics in learning towards self-efficacy and computational thinking skills. This paper also examines whether there is a relationship between computational thinking skills and self-efficacy. This paper employed a systematic literature review method by examining forty articles drawn from ‘computational thinking’, ‘self-efficacy’ and ‘educational robotics’ keywords in the literature. The majority of research indicates that the use of robotics in learning facilitates the development of computational thinking skills as well as learners’ self-efficacy. The paper advocates that future research should examine the extent of computational thinking skills and self-efficacy influence on student behavior.

Educational technologists and practitioners have made substantial strides in developing affordable digital and tangible resources to support both formal and informal computer science instruction. However, there is a lack of research on practice-based assignments, such as Internet of Things (IoT) projects, that allow undergraduate students to design and demonstrate educational robots using digital or physical assistance, especially when it comes to computational thinking (CT) and programming skills development in association with their psycho-emotional experience. This study compares the impact of Scratch and LEGO ® WeDo robotic kits on students' CT and programming skills development. A quasi-experimental approach was conducted, involving two hundred forty-six participants ( n = 246), who were equally divided between Scratch and LEGO ® WeDo groups. Results indicate that the LEGO ® WeDo group showed greater improvement in CT and programming skills development, while designing and presenting IoT projects. Nevertheless, no significant association between motivation, grit, and CT skills was observed. The findings highlight the potential of tangible robotics in facilitating students’ hands-on learning and enhancing motivation to foster CT and programming skills. This study provides a wide range of implications for instructional designers on how to use tangible robotics to support hands-on IoT projects in computer science courses.

Background and Context Traditional text and blockbased programming often limits the development of computational thinking (CT) skills, while educational robotics (ER) provides a handson alternative that can better foster these skills. However, how robotics environments promote CT remains unclear. This study focuses on Chinese high school students engaged in programming activities using ER. Objective This study aims to develop and validate an integrative model that explains learner-centered and technology-centered determinants of students’ CT skills. Method A cross‐sectional empirical study was conducted with 339 high school students who completed both a questionnaire and a programming fundamentals test. We applied Partial Least Squares Structural Equation Modeling (PLSSEM) to assess the measurement and structural models, Importance-Performance Matrix Analysis (IPMA) to identify priority factors, and Artificial Neural Network (ANN) analysis to capture non‐linear relationships and rank variable importance. Findings Task‐Technology fit (TTF), learning motivation (LM), cognitive engagement, and prior programming knowledge each exhibited significant positive effects on CT, with LM (β = 0.333, p < .001) and TTF (β = 0.251, p < .01) emerging as the most influential factors. The integrated model accounted for 54.1% of the variance in CT. IPMA and ANN results corroborated the centrality of motivation and fit in enhancing CT. Implications By extending the Cognitive Affective Theory of Multimedia Learning through incorporation of TAM and TTF constructs, this study highlights the dual importance of aligning robotics features with task requirements and fostering student motivation.

This dissertation examines the impact of a robotics-based intervention on elementary-aged students’ interest in STEM subjects and careers and development of computational thinking skills. Previous research suggests educational robotics programs integrate a wide array of skills projected to be essential for success in the workforce of the future. The current research was motivated by two research questions: (1) What is the impact of a robotics-based intervention on elementary-aged students’ interest in STEM subjects and careers? (2) What is the impact of a robotics-based intervention on elementary-aged students' computational thinking skills? To answer these questions, action research was used to examine a multifaceted, constructionist, robotics-based intervention that included weekly WeDo Lego Robotics building and coding sessions facilitated by trained, STEM-speaking adults, the use of the Use-Modify-Create learning progression (Lee, et al., 2011) to scaffold student development of computational thinking skills, a classroom STEM learning center, and student participation in a robotics showcase. Participants were thirty-seven second and third grade students from two classrooms at a rural, Title I elementary school in the Southeastern United States. The intervention was found to have a positive impact on students’ interest in STEM subjects and careers and development of computational thinking skills. Critical intervention elements included: STEM-speaking adults, constructionist building and coding opportunities, opportunities to work with and learn from peers, classroom learning center activities including access to robotics and STEM reading materials and opportunities for student reflection, use of the Use-Modify-Create learning progression, and student participation in a robotics showcase. Based on the findings of this research, elementary schools should strive to incorporate educational robotics into the regular school day. This research provides practitioners with a multifaceted robotics-based intervention that can be integrated into elementary classrooms in as little as two hours per week for sixteen weeks and result in student acquisition of positive attitudes toward STEM subjects and careers and computational thinking skills. These are attitudes and skills which are valuable to students’ future school and career success.

Aim/Purpose: This study aims to implement and evaluate a personalized digital learning environment (PDLE) that delivers differentiated instruction for enhancing computational thinking competencies through robotics education. Background: The background emphasizes the growing demand for computational thinking skills in the modern workforce and the need for flexible learning approaches that accommodate diverse student needs. Methodology: A mixed-methods research approach was employed, utilizing a pre-experimental design with One-Group Pretest-Posttest assessments to evaluate students’ computational thinking development, complemented by classroom observations and instructor feedback to provide deeper insights into the learning process within the PDLE system. Contribution: The key contribution of this study is the integration of PDLE with DI to provide personalized learning pathways that promote deeper engagement and skill development in computational thinking. Findings: Findings suggest that the PDLE significantly enhances students’ computational thinking abilities, particularly in problem-solving and algorithmic thinking, while also improving abstraction, pattern recognition, and fostering greater student independence through self-regulated learning. Recommendations for Practitioners: Recommendations for practitioners include adopting personalized and differentiated learning environments to accommodate diverse learners. Recommendation for Researchers: Recommendations for researchers suggest further exploration of adaptive learning technologies in robotics education. Impact on Society: The impact on society lies in equipping students with essential computational skills that are crucial for success in the digital economy. Future Research: Should explore the scalability of PDLE in other STEM disciplines and investigate long-term impacts on students’ cognitive and career development.

In this chapter, the authors present their research on how P12 students apply computational thinking (CT) skills when they are assigned simple science, technology, engineering, mathematics (STEM) problems, which they are called upon to solve with the help of educational robotics (ER) activities. The reason for this research was the high participation and increased interest shown in an ER event, where distributed questionnaires recorded students' views on ER, STEM, and CT. Their answers were the spark to conduct a pilot study on primary school students in the form of an experiential seminar to investigate the possibility of developing their CT skills by applying ER activities when they are asked to solve authentic STEM problems. The results showed that students may develop CT skills when involved in ER activities and that educational robots enhance students' engagement with programming and create a more favorable environment for developing students' CT skills.