Analysis of Computational Thinking Skills Development from the Implementation of Augmented Reality in Basic Electronics Courses
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The integration of Augmented Reality (AR) in education offers significant potential for enhancing students' Computational Thinking (CT) skills. This study evaluates the im-pact of AR on CT skill development in the Basic Electronics (BEc) course within higher engineering education. The research investigates how AR in practical learning scenarios influences problem-solving abilities, concept understanding, and technical design skills. A comparative analysis was conducted to assess student performance with and without AR over one academic semester. Statistical methods, including Pearson’s correlation coefficient (PCC), were used to evaluate factors such as prior knowledge and intention to use AR. The findings indicate that AR positively impacts CT skills, improving students’ problem-solving and comprehension of material. Despite initial challenges, such as unfamiliarity with the technology, AR has been shown to enhance student engagement and overall learning quality in the BEc course.
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References
A Kassem, M., Khoiry, M. A., & Hamzah, N. (2020). Assessment of the effect of external risk factors on the success of an oil and gas construction project. Engineering, Construction and Architectural Management, 27(9), 2767–2793.
Afthanorhan, A., Ghazali, P. L., & Rashid, N. (2021). Discriminant validity: A comparison of CBSEM and consistent PLS using Fornell & Larcker and HTMT approaches. Journal of Physics: Conference Series, 1874(1), 012085.
AlGerafi, M. A., Zhou, Y., Oubibi, M., & Wijaya, T. T. (2023). Unlocking the potential: A comprehensive evaluation of augmented reality and virtual reality in education. Electronics, 12(18), 3953.
Andrews-Todd, J., & Forsyth, C. M. (2020). Exploring social and cognitive dimensions of collaborative problem solving in an open online simulation-based task. Computers in Human Behavior, 104, 105759.
Andrews-Todd, J., Steinberg, J., Flor, M., & Forsyth, C. M. (2022). Exploring automated classification approaches to advance the assessment of collaborative problem solving skills. Journal of Intelligence, 10(3), 39.
Anwar, M., Hidayat, H., & Sabrina, E. (2023). Exploring the use of Genetic Algorithms Toolbox in Engineering Education: Did it Provide an Interesting Learning Experience for Students? TEM Journal, 12(3), 1719–1724.
Anwar, M., Hidayat, H., Yulistiowarno, I. P., Budayawan, K., Osumah, O. A., & Ardi, Z. (2022). Blended Learning Based Project In Electronics Engineering Education Courses: A Learning Innovation after the Covid-19 Pandemic. International Journal of Interactive Mobile Technologies, 17(14).
Anwar, M., Rahmawati, Y., Yuniarti, N., Hidayat, H., & Sabrina, E. (2024). Leveraging Augmented Reality to Cultivate Higher-Order Thinking Skills and Enhance Students’ Academic Performance. International Journal of Information and Education Technology, 14(10).
Asigigan, S. İ., & Samur, Y. (2021). The effect of gamified stem practices on students’ intrinsic motivation, critical thinking disposition levels, and perception of problem-solving skills. International Journal of Education in Mathematics, Science and Technology, 9(2), 332–352.
Aytekin, A., & Topçu, M. S. (2024). The effect of integrating computational thinking (CT) components into science teaching on 6th grade students’ learning of the circulatory system concepts and CT skills. Education and Information Technologies, 29(7), 8079–8110.
Cai, H., & Gu, X. (2022). Factors that influence the different levels of individuals’ understanding after collaborative problem solving: The effects of shared representational guidance and prior knowledge. Interactive Learning Environments, 30(4), 695–706.
Campo, L., Galindo-Domínguez, H., Bezanilla, M.-J., Fernández-Nogueira, D., & Poblete, M. (2023). Methodologies for fostering critical thinking skills from university students’ points of view. Education Sciences, 13(2), 132.
Chang, C.-Y., Du, Z., Kuo, H.-C., & Chang, C.-C. (2023). Investigating the Impact of Design Thinking-Based STEAM PBL on Students’ Creativity and Computational Thinking. IEEE Transactions on Education.
Chevalier, M., Giang, C., Piatti, A., & Mondada, F. (2020). Fostering computational thinking through educational robotics: A model for creative computational problem solving. International Journal of STEM Education, 7(1), 39.
Choi, H., Crump, C., Duriez, C., Elmquist, A., Hager, G., Han, D., Hearl, F., Hodgins, J., Jain, A., Leve, F., Li, C., Meier, F., Negrut, D., Righetti, L., Rodriguez, A., Tan, J., & Trinkle, J. (2021). On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward. Proceedings of the National Academy of Sciences, 118(1), e1907856118.
del Cerro Velázquez, F., & Morales Méndez, G. (2021). Application in augmented reality for learning mathematical functions: A study for the development of spatial intelligence in secondary education students. Mathematics, 9(4), 369.
Dowell, N. M., Lin, Y., Godfrey, A., & Brooks, C. (2020). Exploring the Relationship between Emergent Sociocognitive Roles, Collaborative Problem-Solving Skills, and Outcomes: A Group Communication Analysis. Journal of Learning Analytics, 7(1), 38–57.
Elford, D., Lancaster, S. J., & Jones, G. A. (2022). Exploring the Effect of Augmented Reality on Cognitive Load, Attitude, Spatial Ability, and Stereochemical Perception. Journal of Science Education and Technology, 31(3), 322–339.
Gunawan, I. G. D., & Danika, I. W. S. G. (2023). Leveraging Advanced Technologies to Enhance Learning Experiences in the Era 5.0. International Proceeding On Religion, Culture, Law, Education, And Hindu Studies, 1, 121–137.
Gyory, J. T., Soria Zurita, N. F., Martin, J., Balon, C., McComb, C., Kotovsky, K., & Cagan, J. (2022). Human versus artificial intelligence: A data-driven approach to real-time process management during complex engineering design. Journal of Mechanical Design, 144(2), 021405.
Hair, J., & Alamer, A. (2022a). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027.
Hair, J., & Alamer, A. (2022b). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027.
Hashim, A. S., Awadh, W. A., & Hamoud, A. K. (2020). Student performance prediction model based on supervised machine learning algorithms. IOP Conference Series: Materials Science and Engineering, 928(3), 032019.
Hidayat, H., Harmanto, D., & Legiman, L. (2024). Analysis of Computational Thinking Skill Through Technology Acceptance Model Approach Using Augmented Reality in Electronics Engineering Education.
Hidayat, H., Zainuddin, Z., Anwar, M., Saputra, H. K., Muji, A. P., & Jasril, I. R. (2024). The Impact of the Learning Mobile Application on Student Performance Using the Technology Acceptance Model. International Journal of Information and Education Technology, 14(5), 657–667.
Iatsyshyn, A. V., Kovach, V. O., Lyubchak, V. O., Zuban, Y. O., Piven, A. G., Sokolyuk, O. M., Iatsyshyn, A. V., Popov, O. O., Artemchuk, V. O., & Shyshkina, M. P. (2020). Application of augmented reality technologies for education projects preparation.
Jalilvand, M. R., & Ghasemi, H. (2024). Augmented reality technology in tourism and hospitality research: A review from 2010 to 2024. Journal of Science and Technology Policy Management.
Jesionkowska, J., Wild, F., & Deval, Y. (2020). Active learning augmented reality for STEAM education—A case study. Education Sciences, 10(8), 198.
Jiang, L., Wu, Z., Xu, X., Zhan, Y., Jin, X., Wang, L., & Qiu, Y. (2021). Opportunities and challenges of artificial intelligence in the medical field: Current application, emerging problems, and problem-solving strategies. Journal of International Medical Research, 49(3), 030006052110001.
Jiang, S., Tatar, C., Huang, X., Sung, S. H., & Xie, C. (2022). Augmented Reality in Science Laboratories: Investigating High School Students’ Navigation Patterns and Their Effects on Learning Performance. Journal of Educational Computing Research, 60(3), 777–803.
Jumani, A. K., Siddique, W. A., Laghari, A. A., Abro, A., & Khan, A. A. (2022). Virtual reality and augmented reality for education. In Multimedia computing systems and virtual reality (pp. 189–210). CRC Press.
Kamińska, D., Zwoliński, G., Laska-Leśniewicz, A., Raposo, R., Vairinhos, M., Pereira, E., Urem, F., Ljubić Hinić, M., Haamer, R. E., & Anbarjafari, G. (2023). Augmented reality: Current and new trends in education. Electronics, 12(16), 3531.
Kang, C., Liu, N., Zhu, Y., Li, F., & Zeng, P. (2023). Developing College students’ computational thinking multidimensional test based on Life Story situations. Education and Information Technologies, 28(3), 2661–2679.
Kiryakova, G. (2020). The immersive power of augmented reality. In Human 4.0-from biology to cybernetic. IntechOpen.
Kong, S., & Lai, M. (2023). Effects of a teacher development program on teachers’ knowledge and collaborative engagement, and students’ achievement in computational thinking concepts. British Journal of Educational Technology, 54(2), 489–512.
Kumar, S. (2023). Integrating Virtual Reality in Instruction: Cutting-Edge Techniques for Enhanced Learning. Journal of Advanced Research in Library and Information Science, 10(4), 30–36.
Kurniadi, D., Hidayat, H., Anwar, M., Budayawan, K., Syaifar Luthfi, A., Zulhendra, Z., Efrizon, E., & Safitri. (2023). Genetic Algorithms for Optimizing Grouping of Students Classmates in Engineering Education. International Journal of Information and Education Technology, 13(12).
Lages, L. F., Ricard, A., Hemonnet‐Goujot, A., & Guerin, A. (2020). Frameworks for innovation, collaboration, and change: Value creation wheel, design thinking, creative problem‐solving, and lean. Strategic Change, 29(2), 195–213.
Lim, K. Y. T., & Lim, R. (2020). Semiotics, memory and augmented reality: History education with learner‐generated augmentation. British Journal of Educational Technology, 51(3), 673–691.
Ma, Y., Zhang, H., Ni, L., & Zhou, D. (2023). Identifying collaborative problem-solver profiles based on collaborative processing time, actions and skills on a computer-based task. International Journal of Computer-Supported Collaborative Learning, 18(4), 465–488.
Manzano-León, A., Camacho-Lazarraga, P., Guerrero-Puerta, M. A., Guerrero-Puerta, L., Alias, A., Aguilar-Parra, J. M., & Trigueros, R. (2021). Development and validation of a questionnaire on motivation for cooperative playful learning strategies. International Journal of Environmental Research and Public Health, 18(3), 960.
Munianday, P., Radzi, A. R., Esa, M., & Rahman, R. A. (2022). Optimal strategies for improving organizational BIM capabilities: PLS-SEM approach. Journal of Management in Engineering, 38(3), 04022015.
Nguyen, C. Q., Nguyen, A. M. T., & Ba Le, L. (2022). Using partial least squares structural equation modeling (PLS-SEM) to assess the effects of entrepreneurial education on engineering students’s entrepreneurial intention. Cogent Education, 9(1), 2122330.
Niederman, F. (2021). Project management: Openings for disruption from AI and advanced analytics. Information Technology & People, 34(6), 1570–1599.
Olim, S. C., & Nisi, V. (2020). Augmented Reality Towards Facilitating Abstract Concepts Learning. In N. J. Nunes, L. Ma, M. Wang, N. Correia, & Z. Pan (Eds.), Entertainment Computing – ICEC 2020 (Vol. 12523, pp. 188–204). Springer International Publishing.
Ouyang, F., Xu, W., & Cukurova, M. (2023). An artificial intelligence-driven learning analytics method to examine the collaborative problem-solving process from the complex adaptive systems perspective. International Journal of Computer-Supported Collaborative Learning, 18(1), 39–66.
Peel, A., Sadler, T. D., & Friedrichsen, P. (2022). Algorithmic Explanations: An Unplugged Instructional Approach to Integrate Science and Computational Thinking. Journal of Science Education and Technology, 31(4), 428–441.
Podgórska, M. (2022). Challenges and perspectives in innovative projects focused on sustainable industry 4.0—A case study on polish project teams. Sustainability, 14(9), 5334.
Putra, A., Sumarmi, S., Sahrina, A., Fajrilia, A., Islam, M., & Yembuu, B. (2021). Effect of mobile-augmented reality (MAR) in digital encyclopedia on the complex problem solving and attitudes of undergraduate student. International Journal of Emerging Technologies in Learning (IJET), 16(7), 119–134.
Raisch, S., & Fomina, K. (2024). Combining Human and Artificial Intelligence: Hybrid Problem-Solving in Organizations. Academy of Management Review, amr.2021.0421.
Reeves, S. M., Crippen, K. J., & McCray, E. D. (2021). The varied experience of undergraduate students learning chemistry in virtual reality laboratories. Computers & Education, 175, 104320.
Refdinal, R., Adri, J., Prasetya, F., Tasrif, E., & Anwar, M. (2023). Effectiveness of Using Virtual Reality Media for Students’ Knowledge and Practice Skills in Practical Learning. JOIV : International Journal on Informatics Visualization, 7(3), 688.
Samaddar, K., & Mondal, S. (2024). AR and VR-based travel: A responsible practice towards sustainable tourism. International Journal of Tourism Cities, 10(1), 105–128.
Samala, A. D., Daineko, Y., Indarta, Y., Nando, Y. A., Anwar, M., & Jaya, P. (2023). Global Publication Trends in Augmented Reality and Virtual Reality for Learning: The Last Twenty-One Years. International Journal of Engineering Pedagogy, 13(2).
Sanabria-Z, J., & Olivo, P. G. (2024). AI platform model on 4IR megatrend challenges: Complex thinking by active and transformational learning. Interactive Technology and Smart Education.
Saritepeci, M. (2020). Developing Computational Thinking Skills of High School Students: Design-Based Learning Activities and Programming Tasks. The Asia-Pacific Education Researcher, 29(1), 35–54.
Sarker, I. H. (2022). AI-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3(2), 158.
Sönmez, E. (2021). Technology-enhanced CT: A systematic review. Thinking Skills and Creativity, 41, 100913.
Sun, C., Shute, V. J., Stewart, A., Yonehiro, J., Duran, N., & D’Mello, S. (2020). Towards a generalized competency model of collaborative problem solving. Computers & Education, 143, 103672.
Tsai, C.-W., Lee, L.-Y., Cheng, Y.-P., Lin, C.-H., Hung, M.-L., & Lin, J.-W. (2024). Integrating online meta-cognitive learning strategy and team regulation to develop students’ programming skills, academic motivation, and refusal self-efficacy of Internet use in a cloud classroom. Universal Access in the Information Society, 23(1), 395–410.
Tsai, M.-J., Liang, J.-C., & Hsu, C.-Y. (2021). The computational thinking scale for computer literacy education. Journal of Educational Computing Research, 59(4), 579–602.
Voon, X. P., Wong, S. L., Wong, L. H., Khambari, M. N. M., & Abdullah, S. I. S. S. (2022). Developing computational thinking competencies through constructivist argumentation learning: A problem-solving perspective. International Journal of Information and Education Technology.
Wang, X.-M., Hu, Q.-N., Hwang, G.-J., & Yu, X.-H. (2023). Learning with digital technology-facilitated empathy: An augmented reality approach to enhancing students’ flow experience, motivation, and achievement in a biology program. Interactive Learning Environments, 31(10), 6988–7004.
Wang, Y., Deng, Y., Ren, F., Zhu, R., Wang, P., Du, T., & Du, Q. (2020). Analysing the spatial configuration of urban bus networks based on the geospatial network analysis method. Cities, 96, 102406.
Yağcı, M. (2022). Educational data mining: Prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9(1), 11.
Yu, Z., Gao, M., & Wang, L. (2021). The Effect of Educational Games on Learning Outcomes, Student Motivation, Engagement and Satisfaction. Journal of Educational Computing Research, 59(3), 522–546.
Yulianto, L. D., Triayudi, A., & Sholihati, I. D. (2020). Implementation Educational Data Mining For Analysis of Student Performance Prediction with Comparison of K-Nearest Neighbor Data Mining Method and Decision Tree C4. 5: Implementation Educational Data Mining For Analysis of Student Performance Prediction with Comparison of K-Nearest Neighbor Data Mining Method and Decision Tree C4. 5. Jurnal Mantik, 4(1), 441–451.
Zakaria, N. I., & Iksan, Z. H. (2020). Computational thinking among high school students. Universal Journal of Educational Research, 8(11).
Zulwisli, Z., Ambiyar, A., Anwar, M., & Herayono, A. (2024). The Effect of Theory of Planned Behavior (TBP) and Creativity-Based Industry Perception on Digital Entrepreneurship: An Innovativeness as Mediator. PaperASIA, 40(3b), 96–105.