Declaration of Conflict of Interest: No conflict of interest is declared by author(s).

Data sharing statement: Data supporting the findings and conclusions are available upon request from the corresponding author(s).

• Azaiza, I., Bar, V., & Galili, I. (2006). Learning electricity in elementary school. International Journal of Science and Mathematics Education, 4(1), 45-71. https://doi.org/10.1007/s10763-004-6826-9
• Barr, D., Harrison, J., & Conery, L. (2011). Computational thinking: A digital age skill for everyone. Learning & Leading with Technology, 38(6), 20-23. https://files.eric.ed.gov/fulltext/EJ918910.pdf
• Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: what is Involved and what is the role of the computer science education community? AcmInroads, 2(1), 48-54. https://doi.org/10.1145/1929887.1929905
• Basawapatna, A., Koh, K. H., Repenning, A., Webb, D. C., & Marshall, K. S. (2011, March). Recognizing computational thinking patterns [Poster presentation]. 42nd ACM technical symposium on Computer science education, Dallas, USA. https://doi.org/10.1145/1953163.1953241
• Baser, M. (2006). Promoting conceptual change through active learning using open source software for physics simulations. Australasian Journal of Educational Technology, 22(3), 336-354. https://doi.org/10.14742/ajet.1290
• Chatzopoulos, A., Papoutsidakis, M., Kalogiannakis, M., & Psycharis, S. (2020). Innovative Robot for Educational Robotics and STEM. In V. Kumar & C. Troussas (Eds.), Intelligent Tutoring Systems, ITS 2020. Lecture Notes in Computer Science (Vol. 12149, pp. 95-104). Springer. https://doi.org/10.1007/978-3-030-49663-0_13
• Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods approaches (3rd ed.). Sage.
• Dorouka, P., Papadakis, St., & Kalogiannakis, M. (2019). Tablets & apps for promoting Robotics, Mathematics, STEM Education and Literacy in Early Childhood Education, International Journal of Mobile Learning and Organisation, 14(2), 255-274. https://doi.org/10.1504/IJMLO.2020.106179
• Fokides, E., & Papoutsi, A. (2020). Using Makey-Makey for teaching electricity to primary school students. A pilot study. Education and Information Technologies, 25(2), 1193-1215. https://doi.org/10.1007/s10639-019-10013-5
• Glauert, E. B. (2009). How young children understand electric circuits: Prediction, explanation and exploration. International Journal of Science Education, 31(8), 1025-1047, https://doi.org/10.1080/09500690802101950
• Hemmendinger, D. (2010). A plea for modesty. Acm Inroads, 1(2), 4-7. https://doi.org/10.1145/1805724.1805725
• Jaakkola, T., & Nurmi, S. (2008). Fostering elementary school students’ understanding of simple electricity by combining simulation and laboratory activities. Journal of Computer Assisted Learning, 24(4), 271-283. https://doi.org/10.1111/j.1365-2729.2007.00259.x
• Jaakkola, T., Nurmi, S., & Veermans, K. (2011). A Comparison of Students’ Conceptual Understanding of Electric Circuits in Simulation Only and Simulation Laboratory Contexts. Journal of Research in Science Teaching, 48(1), 71-93. https://doi.org/10.1002/tea.20386
• Jin, K. H., Haynie, K., & Kearns, G. (2016, September). Teaching Elementary Students Programming in a Physical Computing Classroom [Poster presentation]. 17th Annual Conference on Information Technology Education, Boston, Massachusetts, USA. https://doi.org/10.1145/2978192.2978238
• Kada, V., & Ravanis, K. (2016). Creating a simple electric circuit with children between the ages of five and six. South African Journal of Education, 36(2), 1-9. https://doi.org/10.15700/saje.v36n2a1233
• Kaliampos, G., Kada, V., Saregar, A., & Ravanis, K. (2020). Preschool pupils’ mental representations on electricity, simple electrical circuit and electrical appliances. European Journal of Education Studies, 7(12), 596-611. https://doi.org/10.46827/ejes.v7i12.3471
• Kalogiannakis, M., Ampartzaki, M., Papadakis, S., & Skaraki, E. (2018). Teaching natural science concepts to young children with mobile devices and hands-on activities. A case study. International Journal of Teaching and Case Studies, 9(2), 171-183. https://doi.org/10.1504/IJTCS.2018.090965
• Kalogiannakis, M., & Papadakis, S. (2017). An evaluation of Greek educational Android apps for preschoolers. In Proceedings of the 12th Conference of the European Science Education Research Association (ESERA), Research, Practice and Collaboration in Science Education (pp. 21-25), Dublin City University and the University of Limerick, Dublin, Ireland.
• Kalogiannakis, M., Papadakis, S., & Zourmpakis, A.-I. (2021). Gamification in Science Education. A Systematic Review of the Literature. Education Sciences, 11(1), 22. https://doi.org/10.3390/educsci11010022
• Kalogiannakis, Μ., Tzagkaraki, E., & Papadakis, St. (2021). A Systematic Review of the Use of BBC Micro:bit in Primary School. In Proceedings of the 10^th Virtual Edition of the International Conference New Perspectives in Science Education, 379-384, Italy-Florence: Filodiritto-Pixel, 18-19 March 2021. https://doi.org/10.26352/F318_2384-9509
• Kanaki, K., & Kalogiannakis, M. (2018). Introducing fundamental object-oriented programming concepts in preschool education within the context of physical science courses, Education and Information Technologies, 23(6), 2673-2698. https://doi.org/10.1007/s10639-018-9736-0
• Klahr, D., Triona, L. M., & Williams, C. (2007). Hands on what? The relative effectiveness of physical versus virtual materials in an engineering design project by middle school children. Journal of Research in Science teaching, 44(1), 183-203. https://doi.org/10.1002/tea.20152
• Koh, K. H., Basawapatna, A., Bennett, V., & Repenning, A. (2010). Towards the automatic recognition of computational thinking for adaptive visual language learning. Poster presented at the Visual languages and human-centric computing (VL/HCC), 2010 IEEE symposium. https://doi.org/10.1109/VLHCC.2010.17
• Korkmaz, Ö., & Altun, H. (2014). Adapting Computer Programming Self-Efficacy Scale and Engineering Students’ Self-Efficacy Perceptions. Participatory Educational Research, 1(1), 20-31. https://doi.org/10.17275/per.14.02.1.1
• Küçüközer, H., & Kocakülah, S. (2007). Secondary School Students’ Misconceptions about Simple Electric Circuits. Online Submission, 4(1), 101-115. https://files.eric.ed.gov/fulltext/ED564331.pdf
• Lee, I., Martin, F., Denner, J., Coulter, B., Allan, W., Erickson, J., & Werner, L. (2011). Computational thinking for youth in practice. Acm Inroads, 2(1), 32-37. https://doi.org/10.1145/1929887.1929902
• Lindstrøm, C., & Sharma, M. D. (2011). Self-efficacy of first year university physics students: Do gender and prior formal instruction in physics matter?. International Journal of Innovation in Science and Mathematics Education (formerly CAL-laborate International), 19(2), 1-19. https://openjournals.library.sydney.edu.au/index.php/CAL/article/view/4770/5767
• Liu, J., & Wang, L. (2010, March). Computational thinking in discrete mathematics [Poster presentation]. 2010 2^nd International Workshop on Education Technology and Computer Science. https://doi.org/10.1109/ETCS.2010.200
• Liu, J., Lin, C.-H., Hasson, E. P., & Barnett, Z. D. (2011). Introducing computer science to K-12 through a summer computing workshop for teachers [Poster presentation]. 42nd ACM technical symposium on Computer science education. https://doi.org/10.1145/1953163.1953277
• Lopez, V., & Hernandez, M. I. (2015). Scratch as a computational modelling tool for teaching physics. Physics Education, 50(3), 310. https://doi.org/10.1088/0031-9120/50/3/310
• Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41, 51-61. https://doi.org/10.1016/j.chb.2014.09.012
• Mannila, L., Dagiene, V., Demo, B., Grgurina, N., Mirolo, C., Rolandsson, L., & Settle, A. (2014). Computational thinking in K-9 education [Poster presentation]. 2014 on innovation & technology in computer science education conference. https://doi.org/10.1145/2713609.2713610
• Maloney, J., Resnick, M., Rusk, N., Silverman, B., & Eastmond, E. (2010). The scratch programming language and environment. ACM Transactions on Computing Education (TOCE), 10(4), 16. https://doi.org/10.1145/1868358.1868363
• Meerbaum-Salant, O., Armoni, M., & Ben-Ari, M. (2013). Learning computer science concepts with scratch. Computer Science Education, 23(3), 239-264. https://doi.org/10.1080/08993408.2013.832022
• Métioui, A., & Trudel, L. (2020). Conceptions about electrical circuits of english and french pupils from nova scotia in Canada: English and French conceptions on electric circuits. Edu Review. International Education and Learning Review, 8(2), 73-82. https://doi.org/10.37467/gka-revedu.v8.2639
• Papadakis, S., & Kalogiannakis, M. (2020). A Research Synthesis of the Real Value of Self-Proclaimed Mobile Educational Applications for Young Children. In St. Papadakis & M. Kalogiannakis (Eds), Mobile Learning Applications in Early Childhood Education, (pp. 1-19). IGI Global. https://doi.org/10.4018/978-1-7998-1486-3.ch001
• Papadakis, St., Kalogiannakis, M., Zaranis, N., & Orfanakis, V. (2016). Using Scratch and App Inventor for teaching introductory programming in secondary education. A case study. International Journal of Technology Enhanced Learning, 8(3/4), 217-233. https://doi.org/10.1504/IJTEL.2016.082317
• Papadakis, St., Kalogiannakis, M., & Zaranis, N. (2016). Developing fundamental programming concepts and computational thinking with ScratchJr in Preschool Education. A case study. International Journal of Mobile Learning and Organisation, 10(3), 187-202. https://doi.org/10.1504/IJMLO.2016.077867
• Papadakis, St., & Kalogiannakis, M. (Eds). (2021). Handbook of Research on Using Education Robotics to Facilitate Student Learning. USA-PA: IGI Global. https://doi.org/10.4018/978-1-7998-6717-3
• Portelance, D. J., & Bers, M. U. (2015). Code and Tell: Assessing young children’s learning of computational thinking using peer video interviews with ScratchJr [Paper presentation]. 14th International Conference on Interaction Design and Children. https://doi.org/10.1145/2771839.2771894
• Przybylla, M., &Romeike, R. (2014). Physical computing in computer science education [Paper presentation]. 9th Workshop in Primary and Secondary Computing Education. https://doi.org/10.1145/2670757.2670782
• Psycharis, S. (2011). The computational experiment and its effects on approach to learning and beliefs on physics. Computers & Education, 56(3), 547-555. https://doi.org/10.1016/j.compedu.2010.09.011
• Psycharis, S. (2013). Examining the effect of the computational models on learning performance, scientific reasoning, epistemic beliefs and argumentation: An implication for the STEM agenda. Computers & Education, 68, 253-265. https://doi.org/10.1016/j.compedu.2013.05.015
• Psycharis, S., Botsari, E., Mantas, P., & Loukeris, D. (2014). The impact of the computational inquiry based experiment on metacognitive experiences, modelling indicators and learning performance. Computers & Education, 72, 90-99. https://doi.org/10.1016/j.compedu.2013.10.001
• Psycharis, S., & Kotzampasaki, E. (2019). The Impact of a STEM Inquiry Game Learning Scenario on Computational Thinking and Computer Self-confidence. Eurasia Journal of Mathematics, Science and Technology Education, 15(4), em1689. https://doi.org/10.29333/ejmste/103071
• Rees, A., García-Peñalvo, F. J., Jormanainen, I., Tuul, M., & Reimann, D. (2016). TACCLE 3, 05: An overview of the most relevant literature on coding and computational thinking with emphasis on the relevant issues for teachers. https://repositorio.grial.eu/bitstream/grial/688/1/TACCLE3O5Literaturereview%20-%20final.pdf
• Resnick, M. (2008). Sowing the seeds for a more creative society. Learning & Leading with Technology, 35(4), 18-22. https://files.eric.ed.gov/fulltext/EJ779952.pdf
• Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., Silverman, B. (2009). Scratch: programming for all. Communications of the ACM, 52(11), 60-67.
• Rotgans, J. (2009). Motivation, achievement-related behaviours, and educational outcomes. Erasmus University Rotterdam. http://hdl.handle.net/1765/15984
• Schraw, G., Crippen, K. J., & Hartley, K. (2006). Promoting self-regulation in science education: Metacognition as part of a broader perspective on learning. Research in science education, 36(1-2), 111-139. https://doi.org/10.1007/s11165-005-3917-8
• Seiter, L., & Foreman, B. (2013). Modeling the learning progressions of computational thinking of primary grade students [Paper presentation]. 9th annual international ACM conference on International computing education research, San Diego, California. https://doi.org/10.1145/2493394.2493403
• Sengupta, P., Dickes, A., Farris, A. V., Karan, A., Martin, D., & Wright, M. (2015). Programming in K-12 science classrooms. Communications of the ACM, 58(11), 33-35. https://doi.org/10.1145/2822517
• Sengupta, P., Kinnebrew, J. S., Basu, S., Biswas, G., & Clark, D. (2013). Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18(2), 351-380. https://doi.org/10.1007/s10639-012-9240-x
• Sezgintürk, M., & Sungur, S. (2020). A multidimensional investigation of students’ science self-efficacy: The role of gender. İlkogretim Online - Elementary Education Online, 19(1), 208-218.
• Shipstone, D. M. (1984). A study of children’s understanding of electricity in simple DC circuits. European Journal of Science Education, 6, 185-198. https://doi.org/10.1080/0140528840060208
• Solomonidou, C. & Kakana, D.-M. (2000). Preschool Children’s Conceptions About the Electric Current and the Functioning of Electric Appliances. European Early Childhood Education Research Journal, 8(1), 95-107. https://doi.org/10.1080/13502930085208511
• Tzagkaraki, E., Papadakis, St., & Kalogiannakis, Μ. (2021). Exploring the Use of Educational Robotics in primary school and its possible place in the curricula. In M. Malvezzi, D. Alimisis, & M. Moro (Eds). Advances in Intelligent Systems and Computing (Under publication).
• Vlasopoulou, M., Kalogiannakis, M., & Sifaki, E. (2021). Investigating Teachers’ Attitude and Behavioral Intentions for the Impending Integration of STEM Education in Primary School. In St. Papadakis & M. Kalogiannakis (Eds.), Handbook of Research on Using Education Robotics to Facilitate Student Learning (pp. 235-256). IGI Global. https://doi.org/10.4018/978-1-7998-6717-3.ch009
• Voskoglou, M. G., & Buckley, S. (2012). Problem solving and computational thinking in a learning environment. Egyptian Computer Science Journal, 36(4), 28-46. https://doi.org/10.12691/ajams-6-6-6
• Weese, J. L. (2017). Bringing computational thinking to K-12 and higher education (Dissertation). Kansas State University. http://hdl.handle.net/2097/35430
• Weese, J.-L., & Feldhausen, R. (2017). STEM Outreach: Assessing Computational Thinking and Problem Solving [Paper presentation]. 2017 ASEE Annual Conference & Exposition. ASEE Conferences, Columbus, Ohio. https://doi.org/10.18260/1-2--28845
• Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35, https://doi.org/10.1145/1118178.1118215
• Xie, C., Tinker, R., Tinker, B., Pallant, A., Damelin, D., & Berenfeld, B. (2011). Computational experiments for science education. Science, 332(6037), 1516-1517, https://doi.org/10.1126/science.1197314
• Zacharia, Z., & Michael, M. (2016). Using Physical and Virtual Manipulatives to Improve Primary School Students’ Understanding of Concepts of Electric Circuits. In M. Riopel & Z. Smyrnaiou (Eds.), New Developments in Science and Technology Education (pp. 125-140). Springer.
• Zacharia, Z. C. (2007). Comparing and combining real and virtual experimentation: an effort to enhance students’ conceptual understanding of electric circuits. Journal of Computer Assisted Learning, 23(2), 120-132. https://doi.org/10.1111/j.1365-2729.2006.00215.x

APA

Ntourou, V., Kalogiannakis, M., & Psycharis, S. (2021). A Study of the Impact of Arduino and Visual Programming In Self-Efficacy, Motivation, Computational Thinking and 5th Grade Students’ Perceptions on Electricity. Eurasia Journal of Mathematics, Science and Technology Education, 17(5), em1960. https://doi.org/10.29333/ejmste/10842

Vancouver

Ntourou V, Kalogiannakis M, Psycharis S. A Study of the Impact of Arduino and Visual Programming In Self-Efficacy, Motivation, Computational Thinking and 5th Grade Students’ Perceptions on Electricity. EURASIA J Math Sci Tech Ed. 2021;17(5):em1960. https://doi.org/10.29333/ejmste/10842

AMA

Ntourou V, Kalogiannakis M, Psycharis S. A Study of the Impact of Arduino and Visual Programming In Self-Efficacy, Motivation, Computational Thinking and 5th Grade Students’ Perceptions on Electricity. EURASIA J Math Sci Tech Ed. 2021;17(5), em1960. https://doi.org/10.29333/ejmste/10842

Chicago

Ntourou, Vassiliki, Michail Kalogiannakis, and Sarantos Psycharis. "A Study of the Impact of Arduino and Visual Programming In Self-Efficacy, Motivation, Computational Thinking and 5th Grade Students’ Perceptions on Electricity". Eurasia Journal of Mathematics, Science and Technology Education 2021 17 no. 5 (2021): em1960. https://doi.org/10.29333/ejmste/10842

Harvard

Ntourou, V., Kalogiannakis, M., and Psycharis, S. (2021). A Study of the Impact of Arduino and Visual Programming In Self-Efficacy, Motivation, Computational Thinking and 5th Grade Students’ Perceptions on Electricity. Eurasia Journal of Mathematics, Science and Technology Education, 17(5), em1960. https://doi.org/10.29333/ejmste/10842

MLA

Ntourou, Vassiliki et al. "A Study of the Impact of Arduino and Visual Programming In Self-Efficacy, Motivation, Computational Thinking and 5th Grade Students’ Perceptions on Electricity". Eurasia Journal of Mathematics, Science and Technology Education, vol. 17, no. 5, 2021, em1960. https://doi.org/10.29333/ejmste/10842