Journal of Educational Computing Research

This research aimed to investigate the structural relationships among teachers’ computational thinking (CT), design thinking (DT), robotics teaching beliefs, and robotics pedagogical content knowledge (RPCK). A total of 98 in-service and pre-service teachers who participated in a robotics teaching professional development workshop served as the sample of the study. A survey including the Computational Thinking Scale, the Design Thinking Disposition Scale, the Robotics Teaching Beliefs Scale and the Technological Pedagogical Content Knowledge–Robotics Scale was conducted after the workshop. A confirmatory factor analysis was employed to validate the measurement constructs, and Partial Least Squares - Structural Equation Modeling (PLS-SEM) analysis was utilized to examine the relationships among the factors. The results revealed that both CT and DT dispositions could positively predict teachers’ robotics teaching beliefs, which subsequently predicted their RPCK. Moreover, a direct positive relationship between CT and RPCK was identified, while such a relationship was not evident for DT. The model demonstrates the critical role of CT in shaping teachers' beliefs and pedagogical strategies of robotics teaching, and provides insights into the indirect influence of DT. Finally, the Model of Robotics Teaching Professional Development (MRTPD) was proposed to profile how to promote teachers’ pedagogical content knowledge of robotics teaching from their CT and DT dispositions.

The interest in Computational Thinking (CT) development among young learners increases with the number of studies located in literature. In this study, a meta-analysis was conducted to address two main objectives: (a) the effectiveness of empirical interventions on the development of CT in children aged of 3–8 years; and (b) the variables that influence the effectiveness of the interventions. Following PRISMA procedures, we identified 17 empirical studies with 34 effect sizes and 1665 participants meeting the inclusion criteria from Web of Science database. Overall, we found a statistically significant large effect size (d = .83 [95% CI: 730, .890]; p < .001) on the CT development of 3–8 years old children, which provides empirical support for having young children to engage in CT experiences. The effect size was significantly influenced by moderating variables including gender, scaffolding, and education level. Intervention length showed a marginally significant effect. Therefore, educators could refer to the significant moderators when designing tailored interventions for CT development in early childhood education while a call for more empirical studies of CT development in young children is proposed.

Abundant research has tried to understand how games can be designed and used effectively to improve the learning process and to examine the correlations between digital learning games and student motivation, engagement, and knowledge retention. The current study examined the correlation between learning through digital game-based learning (DGBL) and students’ achievements, their sense of involvement, and motivation for learning. Using a quantitative approach, data was drawn from questionnaires and exams in two subjects: literature and language. Participants were 320 male and female students aged 12–14 attending a single middle school participated in the study. The students were randomly divided into three groups, each group was given a unique teaching and learning method. Group 1 studied and practiced using the traditional method (a teacher who teaches in the classroom and worksheets for practice), Group 2 studied with the traditional method but practiced with a digital game and Group 3 learned and practiced using a digital game. The findings showed that the students’ attained significantly higher achievements in the group that was taught traditionally but practiced with a digital game. It was also found that when digital learning games are integrated into teaching and learning, the students’ motivation and involvement in the class increased.

The emergence of Generative Artificial Intelligence (GAI) has caused significant disruption to the traditional educational teaching ecosystem. GAI possesses remarkable capabilities in generating human-like text and boasts an extensive knowledge repository, thereby paving the way for potential collaboration with humans. However, current research on collaborating with GAI within the educational context remains insufficient and the methods are relatively limited. This study aims to utilize methods such as Lag Sequential Analysis (LSA) and Epistemic Network Analysis (ENA) to unveil the “black box” of the human-machine collaborative process. In this research, 22 students engaged in collaborative tasks with GAI to refine instructional design schemes within an authentic classroom setting. The results show that the participants significantly improved the quality of instructional design. Leveraging the improvement demonstrated in students’ instructional design performance, we categorized them into high- and low-performance groups. Through the analysis of learning behavior, it was observed that the high-performance group adhered to a structured GAI content application framework: “generate → monitor → apply → evaluate.” Moreover, they adeptly employed communication strategies emphasizing exercising cognitive agency and actively cultivating a collaborative environment. The conclusions drawn from this research may serve as a reference for a series of practical applications in human-machine collaboration and provide directions for subsequent studies.

Addressing cognitive disparities has become a paramount concern in computational thinking (CT) education. The intricate and nuanced relationships between CT and cognitive variations emphasize the needs to accommodate diverse cognitive profiles when fostering CT skills, recognizing that these cognitive functions can manifest as either strengths or limitations in different students. Consequently, understanding the connections between students’ cognitive functions and CT skills assumes pivotal importance in the design of personalized instructional strategies for CT. Despite a general consideration of learning variability in CT education, empirical insights exploring the correlation between cognitive skills and CT competencies remain notably scarce. This study endeavors to bridge this research gap by investigating the links between executive functions and CT skills, as well as the associations between their sub-dimensions. The results reveal a statistically significant correlation coefficient of 0.452 between these two domains, underscoring the notable connection between executive functions and CT abilities. Furthermore, the sub-dimensional analysis offers a comprehensive understanding of how specific executive functions uniquely contribute to certain CT skills. In light of these findings, this research offers a promising pathway for the development of tailored CT education programs that can cater to the unique needs of each individual, ultimately facilitating inclusive CT programs and making significant contributions to broaden STEM education and future workforce.

The use of a pedagogical approach mediated transfer with the bridging method has been successful in facilitating the transitions from block-based to text-based programming languages. Nevertheless, there is a lack of research addressing the impact of this transfer on programming misconceptions during the transition. The way programming concepts are taught to K-12 learners can later result in misconceptions for adult learners. The main objective was to examine the impact of mediated transfer using the bridging method pedagogical approach on the prevalence of programming misconceptions. We conducted a quasi-experimental study in school settings during informatics (computer science) classes among 163 sixth-grade students. The control group received traditional programming lectures using the text-based programming language, Python. Conversely, the experimental group utilized a mediated transfer pedagogical approach by starting with the block-based programming language MakeCode for micro:bit before transitioning to the text-based Python. Our findings indicate that the experimental group significantly reduced programming misconceptions in fundamental programming concepts: variables, sequencing, selection, and loops - compared to the control group. This suggests that the use of block-based programming language as an initial step in programming education, followed by a structured transition to text-based programming language, can effectively mitigate common misconceptions among K-12 learners.

Argumentative writing is a fundamental aspect of undergraduate students’ academic and scientific writing related to critical thinking and problem-solving skills. However, previous studies have shown that students face various difficulties with argumentative writing, such as unclear and illogical ideas, less-structured arguments, and unbalanced interpretation of issues, data, and evidence. This study aims to improve the argumentative writing skills of undergraduate students by integrating computer-supported argumentative writing tools, such as annotation, conversational agents (CAs), and collaborative concept maps, into an online learning management system. Since the study was conducted during the COVID-19 pandemic, these tools can support meaningful learning activities and investigation in argumentative writing. The researchers divided sixty participants into the experimental group (N = 30) and the control group (N = 30). The results showed that the experimental group’s writing achievements outperformed the control group, and the three tools effectively improved the five elements of argumentative writing, including claims, grounds, warrants, backings, and rebuttal. Furthermore, a deep analysis found that the number of annotations, valid CAs’ responses, and argument nodes on collaborative concept maps can significantly predict students’ argumentative writing development. Moreover, students perceived that the incorporated tools could effectively improve their argumentative writing skills.

This study employs meta-analysis to synthesize findings from 30 articles investigating gender differences in computational thinking (CT) among K-12 students. Encompassing 51 independent effect sizes, the meta-analysis involves a participant pool of 9181 individuals from various countries, comprising 4254 males and 4927 females. Results indicate statistically significant gender differences in CT (Hedges’s g = 0.091, p < .05), albeit with a modest effect size, revealing higher CT scores among males compared to females. Further moderation analyses unveil the multifaceted nature of these gender differences. Specifically, while gender differences become significant during high school, recent studies suggest a gradual reduction in CT gender differences with societal progress among K-12 students. Moreover, findings illustrate variations in gender differences across geographical regions. Notably, while the overall gender disparity in CT is non-significant in the “East Asia and Pacific” region, it widens in “North America” and “Europe”, with males scoring higher than females. Conversely, in “Europe and Central Asia”, such gender differences present inconsistent outcomes, with females scoring higher than males. Importantly, assessment tool type does not significantly influence gender differences. Lastly, this study offers recommendations to address CT gender gaps, providing valuable insights for promoting gender equality in education.

Pair Programming is considered an effective approach to programming education, but the synchronous collaboration of two programmers involves complex coordination, making this method difficult to be widely adopted in educational settings. Artificial Intelligence (AI) code-generation tools have outstanding capabilities in program generation and natural language understanding, creating conducive conditions for pairing with humans in programming. Now some more mature tools are gradually being implemented. This review summarizes the current status of educational applications and research on AI-assisted programming technology. Through thematic coding of literature, existing research focuses on five aspects: underlying technology and tool introduction, performance evaluation, the potential impacts and coping strategies, exploration of behavioral patterns in technological application, and ethical and safety issues. A systematic analysis of current literature provides the following insights for future academic research related to the practice of “human-machine pairing” in programming: (1) Affirming the value of AI code-generation tools while also clearly defining their technical limitations and ethical risks; (2) Developing adaptive teaching ecosystems and educational models, conducting comprehensive empirical research to explore the efficiency mechanisms of AI-human paired programming; (3) Further enriching the application of research methods by integrating speculative research with empirical research, combining traditional methods with emerging technologies.