Integrating Computational Thinking Into Immersive Construction Education: A Framework for VR‐Based Learning

This research study presents our work focused on studying the development of introductory computational thinking in undergraduate biomedical engineering students. In response to the growing computational intensity of the healthcare industry, biomedical engineering (BME) undergraduate education is starting to emphasize computation and computational thinking. Computational thinking is a way of thinking that uses concepts and methodologies of computing to solve problems in interdisciplinary and multidisciplinary subjects. In broader terms, computational thinking is not only associated with using computational tools but also with the thought process of solving a problem by data representation, problem decomposition, and algorithm design. Despite being so important, there is little research work or information available on the development of computational thinking in BME undergraduate students. Our research focuses on how BME undergraduate students develop computational thinking skills while performing group activities related to problem-solving. In order to conduct this study, we incorporate a teaching methodology that prompts computational thinking in a thermodynamics course being taught at a public mid-western university to approximately 120 sophomore students. We observe classroom activities involving analytical problem solving followed by pseudo code generation for computational coding. In order to investigate computational thinking, we collect classroom observations of small groups of students as they come up with a solution to an analytical problem with each other. We complement the observation notes of the classroom activities with follow up semi structured interviews with individual students from five groups. Thematic analysis of the student interviews is used in order to analyze student responses towards the incorporation of computation intensive teaching methodology. This Work in Progress helps us expand our understanding of computational thinking development and the challenges involved in performing computational thinking activity in BME undergraduate students.

Problem-based learning integrated with Digital Argumentation (PBL-DA) is a learning strategy for optimizing innovative learning in the digital era. This research aimed to investigate whether the application of PBL-DA can foster the Computational Thinking (CT) and Higher Order Thinking Skills (HOTs). A quasi-experimental design measured three aspects: skill (decomposition, algorithm design, evaluation), attitude (confidence, communication, flexibility), and approach (tinkering, creating, collaborating). The students' HOTs were measured through eight aspects: critical thinking, argumentation, problem-solving, problem-identifying, understanding concepts, analysing, making decisions, and creative thinking. The students' CT and HOTs scores of control and experimental classes were analyzed using Hotelling’s T² test and Tukey’s post hoc test. The Hotelling’s T² test revealed a significant difference between the experimental and control classes for both CT and HOTs (T² = 0.340, p < 0.001 for CT; T² = 0.718, p < 0.001 for HOTs). Tukey’s test further showed that PBL-DA significantly impacted the CT skill and attitude aspects (p < 0.01), while the approach aspect was not significant (p > 0.05). For HOTs, critical thinking, argumentation, problem-identifying and analyzing were significantly improved (p < 0.01), but problem-solving, understanding concepts, making decisions, and creative thinking showed no significant improvement (p > 0.05). Pearson’s correlation analysis indicated a strong positive correlation (r = 0.651, p < 0.001) between students' CT and HOTs skills. These findings provide evidence of the effectiveness of the PBL-DA model in improving students' CT and HOTs, demonstrating its potential for fostering critical and higher-order thinking skills in the digital era.

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.

During the period of the digital revolution, computational thinking (CT) has become a crucial skill that is not only important in the fields of programming and computer science, but also in developing problem-solving abilities, designing systems, and understanding human behavior, all of which are essential for success in the contemporary world. This research examines the crucial significance of CT in K-12 education by doing a thorough assessment and analysis of existing material. The argument posits that CT surpasses conventional educational limitations by equipping students with vital skills to navigate and actively participate in an ever-expanding digital society. This research illustrates practical methods for improving students' computational and statistical thinking abilities through the analysis of two real-life case studies. The aforementioned case studies offer valuable insights into successful approaches to incorporating CT into educational curricula. Furthermore, they underscore the favorable effects of such integration on students' cognitive development. The report additionally examines the difficulties associated with the implementation of CT instruction and puts forth suggestions for educators and policymakers. The primary objective is to emphasize the imperative nature of CT within the K-12 educational framework, establishing it as a fundamental element in equipping young individuals with the cognitive tools required to navigate the intricate challenges of the contemporary, technology-driven society.

[EMBARGOED UNTIL 8/1/2023] Computational thinking helps students solve problems by applying concepts drawn from computer science. One of the most popular strategies to develop computational thinking is to solve computer programming problems using block-based coding commands. One way to engage students in solving programming challenges is by embedding them in digital games. Game-based learning is gaining popularity in the educational community due to its ability to integrate knowing and doing in a situated virtual environment. In addition, the affordances of virtual reality (VR), like high-resolution 3D graphics and a highly immersive environment, make this technology promising for developing games intended to help high school students develop computational thinking featuring a quantitative research approach. A review of the literature reveals that the use of VR game-based learning to develop computational thinking in school-age students is still in the early stages. It also shows that the results obtained in empirical studies so far have been inconclusive. This study tested the effects of VR game-based learning on students' computer programming self-efficacy, engagement, and the development of computational thinking. A total of 73 high school students participated in the study. Participants filled out pre-test questionnaires and a test, played a VR coding game, filled out questionnaires at two moments of the game time, and then filled out post-test questionnaires and a test. The results showed that students exhibited significantly higher computational thinking scores after playing a virtual reality coding game. They also showed that their computer programming self-efficacy levels increased significantly as the level of difficulty also increased while playing the game and that their levels of engagement remained constant throughout the game. The current study deepened the understanding of using a VR game-based learning strategy to develop computational thinking in high school students and the role of computer programming self-efficacy and engagement when developing computational thinking using VR games.

Our research is based on an innovative approach that integrates computational thinking and creative thinking in computer science courses to improve student learning and performance. Referencing Epstein's Generativity Theory, we designed and deployed Computational Creativity Exercises (CCEs) with linkages to concepts in computer science and computational thinking. Prior studies with earlier versions of the CCEs in CS1 courses found that completing more CCEs led to higher grades and increased learning of computational thinking principles. In this study, we extended the examination of CCEs to by deploying revised CCEs across two lower division (freshmen, sophomore) and three upper division (junior, senior) CS courses. We found a linear of increasingly higher grades and computational thinking/CS knowledge test scores with completion of each additional CCE. This dosage effect was consistent across lower and upper division courses. Findings supported our contention that the merger of computational and creative thinking can be realized in computational creativity exercises that can be implemented and lead to increased student learning across courses from freshmen to senior level. The effect of the CCEs on learning was independent of student general academic achievement and individual student motivation. If students do the CCEs, they appear to benefit, whether or not they are self-aware of the benefit or personally motivated to do them. Issues in implementation are discussed.

Formative evaluation of immersive virtual reality expedition mini-games to facilitate computational thinking

Computational thinking (CT) skills are increasingly important in education to prepare students for the challenges of the digital age. Augmented Reality (AR) and Virtual Reality (VR) have been introduced as immersive technologies that have the potential to enhance CT skills through more interactive learning experiences. However, there is still a gap in understanding the effectiveness of these technologies in supporting the development of CT, particularly in different levels of education and disciplines. Although several studies have highlighted the benefits of AR and VR in education, no systematic review integrates these findings to identify advantages, challenges, and opportunities for further implementation. Therefore, this study conducted a systematic review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines by analyzing 25 empirical studies (AR=17, VR=8) obtained from the Scopus database (2008-2024). The analysis addresses four key research questions: (1) the current state of AR/VR in CT development, (2) their advantages, (3) implementation challenges, and (4) future research directions. The results show that AR is more widespread than VR at various levels of education, with dominance in higher education followed by secondary and primary schools. Computer science is the main field of application of AR and VR, while AR is also widely applied in mathematics to increase interest and problem-solving. A total of 11 studies reported significant impacts of these technologies on CT, with AR being superior in increasing student motivation and engagement, as well as aiding in problem-solving and debugging. In contrast, VR provides a more immersive learning experience by strengthening concept understanding, especially in programming and recursion. However, several obstacles in the application of AR and VR, such as hardware limitations, costs, and user skills, affect the effectiveness of these technologies in the learning environment. This study also identified potential future research, including the exploration of VR in primary and kindergarten education, the application of VR in non-computer science fields, and the efficient use of these technologies in supporting the CT process. This study provides more precise insights into the optimal ways of utilizing AR and VR in developing CT skills. It is a reference for educators, policymakers, and researchers in supporting CT learning.

This study presents the development of a student-centric framework for utilizing virtual reality (VR) technologies in education, specifically focusing on enhancing computational thinking skills. While numerous frameworks exist in this domain, they often lack consideration of student preferences, which are integral for fostering learner autonomy. Our proposed framework, with components developed from the constructivist learning theory, emphasises creating knowledge through interaction with the environment, focusing on autonomy, mastery and purpose as drivers of intrinsic outcomes. Through a survey administered to hundred and fifty-seven participants, we sought to identify student-preferred strategies for learning computational thinking skills via VR interventions. Results highlighted key challenges students face when working on computational tasks are related to algorithmic and abstraction thinking. To ease the aforementioned challenges, our findings suggest a preference among students for situated-based learning approaches within VR environments. Additionally, participants recognized the importance of motivational outcomes in improving autonomy and mastery within VR-based learning tasks. Students also preferred tasks that enhanced self-efficacy, contributing to a greater sense of purpose in their learning endeavours. Overall, this investigation sets a foundation for more student-centric, constructivist and intrinsically-based VR frameworks in education.

This study seeks to explore the relationship between science teachers’ computational thinking skills, collaborative learning attitudes, and their creativity in the context of sustainable education. The study adopted an explanatory sequential design, which is one of the designs used in mixed-method research. A total of 369 science teachers were included in the quantitative phase of the study. Quantitative data were collected using three different scales. These scales included the “Computational Thinking Scale”, “Online Cooperative Learning Attitude Scale (OCLAS)”, and “Creative Self-Efficacy Scale”. Structural Equation Modelling (SEM), confirmatory factor analysis, and path analysis were conducted to analyze the quantitative data. The qualitative phase of the study consisted of nine science teachers. Data were collected with a semi-structured interview form by considering the scores obtained from the scales. Qualitative data were analyzed through descriptive analysis. It was found that science teachers’ computational thinking skills and collaborative learning attitudes significantly predicted their creativity within the framework of sustainable education. As a result of the interviews conducted, it was concluded that science teachers lacked computational thinking skills. It is critical to provide teachers with guidance on how to integrate computational thinking skills into their subject areas. Science teachers’ knowledge of computational thinking skills can be enhanced, and computational thinking skills can be included in all teacher education programs.

Computational thinking is a process of problems solving and designing systems using concepts in computer science. Computational thinking is based on a computational principle that involves a wide range of approaches and skills with applications in several disciplines. Computational thinking has been widely applied in the curriculum of various levels of education while still basing on the principles and characteristics of a strong computational thinking. To train the computational thinking to students it must include the computational thinking material in the compulsory material. This material needs to be held since the level of basic education with a simple problem level, the level of the material will increase along with the increase of education level. This article discusses the implementation of computational thinking on information and communication technology learning using a simple programming language that is Scratch. This programming language will make it easier for students to understand the concept of programming by using blocks so that students will easily learn. As a basis for information and communication technology learning, a measurement scale has been developed to measure students’ computational thinking ability. Computational thinking uses a five-point Likert scale consisting of 29 items from 5 developed indicators.

Computational thinking is the process of solving a problem in a way that can be readily automated by a computer. Decomposition, pattern recognition, abstraction, and algorithm design are four major components of computational thinking. We conducted an experiment to study the effectiveness of smartphone apps for teaching computational thinking to 7‐year‐old children. The first experimental group was taught computational thinking using four apps, one for each component. The second experimental group was taught computational thinking using the chalk and board approach. The control group was not imparted any lesson in computational thinking. The intervention period lasted 8 weeks and was followed by a posttest and a retention test conducted after another 2 weeks. The children showed interest in learning computational thinking and could solve problems that are inspired by real‐world engineering problems during the intervention period. They could analyze and debug (77%) their own work. The performance of the children in the two experiment groups was significantly better than those in the control group in the posttest and the retention test (F = 26.470, p < .05). This means that 7‐year‐old children can learn computational thinking from suitable mediums. No significant difference was observed in the performance of the children in the posttest and the retention test (p > .05), denoting that children can retain the concepts of computational thinking. Including computational thinking lessons at the K‐12 level can encourage children to enroll in engineering programs in the future. We recommend standardization of content and development of age‐specific apps for teaching computational thinking.

From previous research, it can be inferred that students already show their capabilities in developing and applying characteristics of computational thinking (CT) in a variety of educational settings at a very young age. Unfortunately, less attention appears to be paid to students with special educational needs, learning disorders, developmental disadvantages, or students suffering from other limitations who can benefit from acquiring and applying skills attributed to CT from an diversity and inclusion perspective. Remarkably in this context, it is reflected from practical experience that students with special needs can achieve high CT performance, but this is not always reflected in curricula, facilities or available learning and testing methods. This is despite growing evidence showing that CT activities are ideally suited for students with learning disabilities and specific educational needs, where unplugged and technology-enhanced CT learning applications can act as reinforcing elements. To capture opportunities, as well gaps and unexplored areas regarding facilitation of CT for students with special educational needs, an inventory survey was therefore conducted in six different countries in primary education and childcare. This to identify perceptions of headmasters/managers regarding the implementation possibilities of CT in curricula, whether and how teachers/child attendants have already integrated CT into their teaching, and what activities are available in schools/child centers to facilitate CT development. Despite the fact that CT enables a development of both generic and specific skills from which all students can benefit, our survey revealed when it concerns teaching, learning and acquiring CT-skills, the pedagogical provision is often not attuned to specific needs of students, and the educational offer is often approached from a purely didactic perspective in order to teach specific (digital) skills for programming. Moreover, students sometimes appear excluded from CT activities based on their personal requirements; that such activities are only reserved for excellent students, or where facilities are not optimal. This is notable because managers and teachers/childcare workers indicate they believe it is important to integrate CT practices into schools, and developing CT-skills can equip students with skills for our future society. It also becomes apparent that, although materials for facilitating CT development are frequently available, they are often incompletely used, or teachers/childcare workers are not sufficiently equipped to use them effectively. Our survey also reveals that the assessment of CT seems to be insufficiently attuned to specific needs and required preferences of students to make concise statements on the demonstrated added value of CT for learning and development. It also appears that teachers are often still unfamiliar with the possibilities that CT contains, or teachers are insufficiently equipped to teach or integrate CT into a development-oriented provision. It is further indicated for teachers, childcare workers and staff it is important to receive training on diversity and inclusion. The results of our exploration shed light on the development, implementation, testing and training for new forms of pedagogy in primary education and childcare, and for professionalization of managers, teachers and childcare workers regarding a developmental focus on CT for children with special educational needs.

Background The development of generative artificial intelligence (GenAI) technology has triggered ripple effects in teaching, learning and assessment in higher education, and college students' GenAI attitudes are key in determining its effective integration. Computational thinking (CT), as an amalgam of skills such as algorithmic thinking and problem‐solving, has become an essential skill for adapting to the digital age represented by GenAI technologies. Objectives While previous research has confirmed the impact of CT on college students' technological attitudes, it has not clarified the relationship between CT and GenAI attitudes among college students and whether this relationship is affected by grade level, gender and major factors. This research gap limits our understanding of how CT contributes to the effective integration of GenAI in higher education. Methods This study conducted a cross‐sectional survey of 1089 college students from China. Descriptive statistical analyses were conducted to present college students' GenAI attitudes and CT scores; sub‐group linear regression was employed to explore the predictive role of CT on GenAI attitudes; parametric t ‐tests were used to analyse the differential effects of gender and major on GenAI attitudes and CT; and moderated models were applied to explore the mechanisms of influence among multiple factors. However, a cross‐sectional study can only reflect the static relationships among variables rather than their dynamic developmental processes. Future research could further explore these relationships through a longitudinal design. Results and Conclusion First, this study found that CT positively predicted GenAI attitudes in all grades, showing an ‘inverted U‐shaped’ predictive curve, with the strongest predictive effect in the sophomore year. Second, the study also found that although female students and college students majoring in science and engineering had significantly higher GenAI attitudes and CT levels than male students and college students majoring in literature and history, the differences in GenAI attitudes and CT caused by gender and major changed with grade level. The differences caused by college students' gender were more significant in their freshman and sophomore years, and the differences caused by their major were more significant in their junior and senior years. Furthermore, the study discovered that the moderating effects of gender and major on the relationship between CT and GenAI became more significant as college students increased in grade level, and that female students and college students majoring in science and engineering were better predictors of changes in GenAI attitudes with respect to CT. Implications for Practice This study revealed the non‐linear relationship between college students' CT and GenAI attitudes and revealed the dynamic differences caused by gender and major, providing precise evidence to support GenAI and CT phased education in higher education.

This research is motivated by the habits we often encounter in learning, especially in mathematics. Each student has different computational thinking abilities. Computational thinking ability is a thinking ability that supports problem-solving solutions. Computational thinking components include decomposition, pattern recognition, abstraction, and algorithm design. This research aims to: 1) Describe the computational thinking abilities of students with high self-regulated learning in solving trigonometry problems, 2) Describe the computational thinking abilities of students with moderate self-regulated learning in solving trigonometry problems, 3) Describe the computational thinking abilities of students with low self-regulated learning in solving trigonometry problems. This research used a qualitative approach with a case study type of research. This research was conducted at SMKN 2 Tulungagung which was attended by all students of class XI TKRO 3, totaling 32 students. Of the 32 students, 6 students will be selected as subjects who are classified based on the level of self-regulated learning. Data collection techniques used are observation, tests, interviews, and documentation. Data analysis techniques were carried out through the stages of data collection, data presentation, and conclusion. The results of this research indicate that: 1) students with high self-regulated learning can fulfill 3-4 indicators of computational thinking skills in solving trigonometry problems, 2) students with moderate self-regulated learning can fulfill 2-3 indicators of computational thinking skills in solving trigonometry problems, 3) and students with low self-regulated learning can fulfill 0-1 indicators of computational thinking skills in solving trigonometry problems