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This study presents the authors’ recent research and application of a new visual programming language and its development environment: VIPLE (Visual IoT/Robotics Programming Language Environment) at Arizona State University (ASU). ASU VIPLE supports a variety of IoT devices and robots based on an open architecture. Based on computational thinking, VIPLE supports the integration of engineering design process, workflow, fundamental programming concepts, control flow, parallel computing, event-driven programming, and service-oriented computing seamlessly into a wide range of curricula, such as introduction to computing, introduction to engineering, service-oriented computing, and software integration. It is actively used at ASU in several sections of FSE 100: Introduction to Engineering and in CSE 446: Software Integration and Engineering, as well as in several other universities worldwide.

In this study, we investigated the debugging process that early childhood preservice teachers used during block-based programing. Its purpose was to provide insights into how to prepare early childhood teachers to integrate computer science into instruction. This study reports the types of errors that early childhood preservice teachers commonly made and how they debugged the errors. Findings are discussed in relation to research and practice that could benefit from debugging instruction. This study provides directions for future computer science education research that aims to prepare teachers for programming, computational thinking, and STEM education. Though this study used robotics as a programming context, findings on early childhood preservice teachers' debugging processes could be applicable to other contexts involving block-based programming.

The application of sense-reason-act (SRA) programming in contemporary education can ensure the development of computational thinking (CT) at a more advanced level. SRA-programming has been identified as an instrumental way of thinking for learning to program robots and encourages the development of the more complex concepts of programming. Visual programming environments are diverse in appearance and prove to be an excellent way to teach pupils the basic ideas of programming. It is important to investigate whether the type of output has a characteristic influence on the level of development of CT in visual programming environments. In this research, we therefore explore whether characteristic differences in the development of CT can be measured when SRA-programming is applied in a visual programming environment with an on-screen output or a tangible output. It was expected that the observed effect of pupils' programming actions through the application of SRA would show that the type of output influences the understanding of complex programming concepts at a higher level. Our results indicate that SRA-programming with visual, on-screen output yields a significant increase in the development of CT, as opposed to SRA-programming with a tangible output. The development of complex programming concepts can also be demonstrated.

BackgroundIn the realm of Science, Technology, Engineering, and Mathematic (STEM) education, computer programming stands as a vital discipline, amalgamating cross-disciplinary knowledge and fostering the capacity to solve real-world problems via fundamental concepts and logical methodologies inherent to computer science. Recognizing the important of computer programming, numerous countries have mandated it as a compulsory course to augment the competitiveness of K-12 learners. Nevertheless, the inherent complexity of computer programming for K-12 learners often goes unacknowledged. Constraints imposed by the course format, coupled with a low instructor–learner ratio, frequently inhibit learners’ ability to resolve course-related issues promptly, thereby creating difficulties in the affective domain. While precision education tools do exist to ascertain learners’ needs, they are largely research-oriented, thereby constraining their suitability for deployment in pragmatic educational settings. Addressing this issue, our study introduces the precision education-based timely intervention system (PETIS), an innovative tool conceived to enhance both programming skills and affective learning in K-12 learners. Our research investigates the influence of PETIS on learners’ performance and evaluate its efficacy in facilitating computer programming education in K-12 environments.ResultsQuantitative results demonstrate that the application of the precision education-based timely intervention system (PETIS) proposed by this research significantly improves programming skills and affective-domain learning objectives for K-12 learners. Similarly, qualitative results indicate that PETIS is beneficial for both teaching and learning in K-12 computer programming courses.ConclusionsThese results not only confirm that timely intervention and feedback improve K-12 learners’ programming skills and affective-domain learning objectives in computer programming courses, but also yield implications as to the feasibility of applying precision education in real-world STEM scenarios.

This study examined the developmental process of children’s computational thinking using block-based programming tools, specifically algorithmic thinking and debugging skills. With this aim, a group of children (N  =  191) from two primary schools were studied for two years beginning from the fourth grade, as they engaged in our block-based programming curriculum in their primary schools. A mixed-methods multiple case study was designed with pre- and posttests, classroom observations and postintervention interviews. The statistical results showed that students’ algorithmic thinking and debugging skills significantly increased through our intervention, with girls gaining more on algorithmic thinking. During the students’ learning process, we found that they demonstrated behavioral, affective, and cognitive engagement while acquiring these skills in schools. This study presents the key to student engagement contributing to the process of computational thinking development, with implications for the design of future computational learning in primary school.

Computational thinking (CT) is an essential skill required for every individual in the digital era to become creative problem solvers. The purpose of this research is to investigate the relationships between computational thinking skills, the Big Five personality factors, and learning motivation using structural equation modeling (SEM). The research administered the computational thinking scale, NEO FFI scale, and Motivated Strategies for Learning Questionnaire to a sample of 92 students pursuing degrees in Computer Science and Engineering. Based on the result analysis, it was determined that both personality and learning motivation had positive and significant impacts on computation thinking skills. Personality had a major contribution to the prediction of CT, with consciousness being the most influential predictor. The findings of this study suggest that educators and academics should focus on the significance of the psychological side of CT for the improvement of students’ CT skills.

In the digital age, the range of digital technologies used in mathematics education grows. Since beliefs are affective-cognitive elements that significantly determine teachers' behavior in the classroom, they are an interesting field of research in mathematics education. A review of previous research has identified different groups of beliefs about the use of digital technologies in mathematics classes. These studies are not focused on specific digital technologies. In an empirical case study that is presented in this paper, the aim was to figure out how beliefs that can be described specifically about the use of visual programming relate to general beliefs about the use of digital technologies in mathematics education. A qualitative content analysis of the reflection journals of seven undergraduate mathematics education students on their work with Scratch, a visual programming environment, in a university seminar led to the formation of ten belief categories about the use of visual programming in mathematics classes. Most of the beliefs are associated with a positive attitude towards visual programming in mathematics education. However, some beliefs could also be identified that refer to the limits and challenges of using visual programming and thus demonstrate rather negative associations. Only a few of the categories identified match the list of belief groups about digital technologies in mathematics education identified in previous research. Some possible reasons for these results are discussed and further research interests in the field of beliefs about the use of digital technologies are suggested.

The principles of programming language design for learning and teaching have been described and discussed for several decades. Most influential was the work of Niklaus Wirth, describing principles such as simplicity, modularity, orthogonality, and readability. So why is this still an area of fundamental disagreement among educators? Why can teachers still not agree on suitable languages for novice programming? Why do we not have a programming language that is designed for education and in widespread use across the world? This paper enumerates and describes educational language design principles in the context of current systems and technologies and discusses why interpretation of these principles shifts as our discipline progresses. We evaluate what these principles mean in our current world, and why a common agreement has not developed. We discuss the relative benefits of pedagogical languages vs. industry languages and articulate why every generation of learners needs their own language.

Controlling complexity through the use of abstractions is a critical part of problem solving in programming. Thus, becoming proficient with procedural and data abstraction through the use of user-defined functions is important. Properly using functions for abstraction involves a number of other core concepts, such as parameter passing, scope and references, which are known to be difficult. Therefore, this paper aims to study students’ proficiency with these core concepts, and students’ ability to apply procedural and data abstraction to solve problems. We collected data from two years of an introductory Python course, both from a questionnaire and from two lab assignments. The data shows that students had difficulties with the core concepts, and a number of issues solving problems with abstraction. We also investigate the impact of using a visualization tool when teaching the core concepts.

This study aims to explain the relationships between secondary school students' digital literacy, computer programming self-efficacy and computational thinking self-efficacy. The study group consists of 204 secondary school students. A relational survey model was used in the research method and three different data collection tools were used to collect data. The structural equation model was used in data analysis to reveal a model that explains and predicts the relationships between variables. According to the results of the research, it was determined that digital literacy of secondary school students affected their computer programming self-efficacy, digital literacy affected their computational thinking self-efficacy, and computer programming self-efficacy affected their computational thinking self-efficacy. It was also found that digital literacy skills have an indirect effect on secondary students' computational thinking self-efficacy on computational thinking self-efficacy.