The Impact of a STEM Inquiry Game Learning Scenario on Computational Thinking and Computer Self-confidence

Sarantos Psycharis; Evangelia Kotzampasaki

doi:10.29333/ejmste/103071

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Sarantos Psycharis ¹ ^* , Evangelia Kotzampasaki ²

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¹ Professor School of Pedagogical and Technological Education, ASPETE, Athens, GREECE² School of Pedagogical and Technological Education, ASPETE, Athens, GREECE^* Corresponding Author

Abstract

Computational thinking is an ability which is considered to be essential for the process of problem solving in every science. The current empirical research aims to study the impact of a STEM content Inquiry based scenario using computational tools and educational games, regarding computational thinking (CT) and confidence for “computers use” of 115 students of Greek public schools of the 5^th-6^th grade. For the needs of this research, a didactic scenario was developed and implemented, using computational tools, such as the Arduino microcontroller, RGB Led’s while a computational model was designed and implemented. The assessment of computational thinking improvement and confidence for computers use was conducted with the use of questionnaires that were administered before and after the intervention. The findings indicate a positive influence of the intervention on the dimensions of computational thinking in the experimental group. The findings can be applied to educational settings that integrate STEM in the teaching sequence in order to enhance students’ confidence with computational experiments.

Keywords

computational thinking
STEM
game learning
Arduino
computational pedagogy
self-confidence

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

EURASIA J Math Sci Tech Ed, Volume 15, Issue 4, April 2019, Article No: em1689

https://doi.org/10.29333/ejmste/103071

Publication date: 21 Jan 2019

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References

Aesaert, K., & vanBraak, J. (2014). Exploring factors related to primary school pupils’ ICT self-efficacy: A multilevel approach. Computers in Human Behavior, 41, 327-341. https://doi.org/10.1016/j.chb.2014.10.006.
Angeli, C., Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smith, J., & Zagami, J. (2016). A K-6 computational thinking curriculum framework: implications for teacher knowledge. Journal of Educational Technology & Society, 19(3), 47.
Barr, D., Harrison, J., & Conery, L. (2011). Computational thinking: A digital age skill for everyone. Learning & Leading with Technology, 38(6), 20-23.
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? ACM Inroads, 2(1), 48-54. https://doi.org/10.1145/1929887.1929905.
Berland, L. K., Martin, T. H., Ko, P., Peacock, S. B., Rudolph, J. J. & Golubski, C. (2013). Student learning in challenge-based engineering curricula. Journal of Pre-College Engineering Education Research (JPEER), 3(1), 5. https://doi.org/10.7771/2157-9288.1080.
Berta R., Bellotti F., van der Spek E., Winkler T. (2017) A Tangible Serious Game Approach to Science, Technology, Engineering, and Mathematics (STEM) Education. In: Nakatsu R., Rauterberg M., Ciancarini P. (eds) Handbook of Digital Games and Entertainment Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-4560-50-4_32.
Beth, E. W., & Piaget, J. (1966). Mathematical epistemology and psychology. Dordrecht, The Netherlands: Reidel.
Bower, M., Wood, L. N., Lai, J. W., Howe, C., Lister, R., Mason, R., Highfield, K., & Veal, J. (2017). Improving the Computational Thinking Pedagogical Capabilities of School Teachers. Australian Journal of Teacher Education, 42(3). https://doi.org/10.14221/ajte.2017v42n3.4.
Brown, J.S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32-42. https://doi.org/10.3102/0013189X018001032.
Bundy, A. (2007). Computational Thinking is Pervasive. Journal of Scientific and Practical Computing, 1(2), 67- 69.
Carin, A. A., Bass, J. E., & Contant, T. L. (2005). Methods for teaching science as inquiry (9th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.
Cetin, I., & Dubinsky, E. (2017). Reflective abstraction in computational thinking. The Journal of Mathematical Behavior, 47, 70-80. https://doi.org/10.1016/j.jmathb.2017.06.004.
Cheng, H., Hao, L., Luo, Z., &Wang, F. (2016). Establishing the Connection between Control Theory Education and Application: An Arduino Based Rapid Control Prototyping Approach. International Journal of Learning and Teaching, 2(1). https://doi.org/10.18178/ijlt.2.1.67-72.
Collins, A. (1988). Cognitive apprenticeship and instructional technology (Technical Report 6899): BBN Labs Inc., Cambridge, MA.
Cretchley, P. (2007). Does computer confidence relate to levels of achievement in ICT-enriched learning models?. Education and Information Technologies, 12(1), 29-39. https://doi.org/10.1007/s10639-006-9004-6.
Dagiene, V. & Stupuriene, G. (2016). Bebras-a sustainable community building model for the concept based learning of informatics and computational thinking. Informatics in Education, 15(1), 25. https://doi.org/10.15388/infedu.2016.02.
Denning, P. J. 2007. Computing is a natural science. Commun. ACM, 50, 13-18. https://doi.org/10.1145/1272516.1272529.
Djambong, T., & Freiman, V. (2016). Task-Based Assessment of Students’ Computational Thinking Skills Developed through Visual Programming or Tangible Coding Environments. International Association for Development of the Information Society.
Einhorn, S. (2012). Microworlds, computational thinking, and 21st century learning. LCSI White Paper. Retrieved from http://www.microworlds.com.
Fogarty, G., Cretchley, P., Harman, C., Ellerton, N., & Konki, N. (2001). Validation of a questionnaire to measure mathematics confidence, computer confidence, and attitudes towards the use of technology for learning mathematics. Mathematics Education Research Journal, 13(2), 154-160. https://doi.org/10.1007/BF03217104.
Goh, K. S. A., Wee, L. K., Yip, K. W., Toh, P. Y. J., & Lye, S. Y. (2013). Addressing learning difficulties in Newtons 1st and 3rd Laws through problem based inquiry using Easy Java Simulation. arXiv preprint arXiv:1303.0081.
Gravemeijer, K., & Doorman, M. (1999). Context problems in realistic mathematics education: A calculus course as an example. Educational Studies in Mathematics, 39(1), 111–129. https://doi.org/10.1023/A:1003749919816.
Greenwald, J. M. (2010). Antecedents of core confidence latent construct: Direct and reciprocal links. Dissertation Abstracts International Section A, 72, 270.
Guzdial, M. (2008). Education Paving the way for computational thinking. Communications of the ACM, 51(8), 25–27, https://doi.org/10.1145/1378704.1378713.
Herrington, J., & Oliver, R. (2000). An instructional design framework for authentic learning environments. Educational technology research and development, 48(3), 23-48. https://doi.org/10.1007/BF02319856.
Hu, C. (2011, June). Computational thinking: what it might mean and what we might do about it. In Proceedings of the 16th annual joint conference on Innovation and technology in computer science education (pp. 223-227). ACM. https://doi.org/10.1145/1999747.1999811.
Juszczak, Μ. D. (2015). From Towards a Computational Pedagogy – Analysis of ABM Deployment in Pedagogical Instances. International Journal of Pedagogy Innovation and New Technologies, 2(1), 2-13. https://doi.org/10.5604/23920092.1159113.
Kalelioglu, F., & Gülbahar, Y. (2014). The effects of teaching programming via Scratch on problem solving skills: a discussion from learners’ perspective. Informatics in Education, 13(1), 33.
Katehi, L., Pearson G., & Feder M. (2009). Engineering in K-12 education: Understanding the status and improving the prospects. Washington, DC: National Academy of Engineering and National Research Council.
Kazimoglu, C., Kiernan, M., Bacon, L. & MacKinnon, L. (2012). Learning Programming at the Computational Thinking Level via Digital Game-Play. Procedia Computer Science, 9, 522-531. https://doi.org/10.1016/j.procs.2012.04.056.
Kotsopoulos, D., Floyd, L., Khan, S., Namukasa, I. K., Somanath, S., Weber, J., & Yiu, C. (2017). A pedagogical framework for computational thinking. Digital Experiences in Mathematics Education, 3(2), 154- 171. https://doi.org/10.1007/s40751-017-0031-2.
Landau, RH., Páez, J. & Bordeianu, C. (2008). A Survey of Computational Physics: Introductory Computational Science. Princeton and Oxford: Princeton University Press.
Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511815355.
L’Heureux, J., Boisvert, D., Cohen, R. & Sanghera, K. 2012. IT problem solving: an implementation of computational thinking in information technology. Proceedings of the 13th annual conference on Information technology education. Calgary, Alberta, Canada: ACM. https://doi.org/10.1145/2380552.2380606.
Lo, J. J., Ji, N. W., Syu, Y. H., You, W. J., Chen, Y. T. (2008) Developing a Digital Game-Based Situated Learning System for Ocean Ecology. In: Pan Z., Cheok A.D., Müller W., El Rhalibi A. (eds) Transactions on Edutainment I. Lecture Notes in Computer Science, vol 5080. Springer, Berlin, Heidelberg, pp 51-61. https://doi.org/10.1007/978-3-540-69744-2_5.
Martinez, S. L., & Stager, G. (2013). Invent to learn. Retrieved on May 2018 from http://courseshare.com/pdfs/Education_3-0__Chula_Bonk.pdf.
Moore, T. J. (2008). STEM integration: Crossing disciplinary borders to promote learning and engagement. Invited presentation to the faculty and graduate students of the UTeachEngineering, UTeachNatural Sciences, and STEM Education program area at University of Texas at Austin, December 15, 2008.
National Academy of Engineering (NAE) & National Research Council (NRC). (2014). STEM integration in K-12 education: status, prospects, and an agenda for research. The National Academies Press, Washington NGSS, 2013.
National Research Council (2010) Report of a Workshop on the Scope and Nature of Computational Thinking. Retrieved from http://www.nap.edu/catalog.php?record_id=12840.
National Research Council (2011). National Research Council Report of a Workshop of Pedagogical Aspects of Computational Thinking. Retrieved on 10-20-2011 from http://www.nap.edu/catalog.php?record_id=13170.
National Research Council. (2012a) A framework for K-12 science education: practices, crosscutting concepts, and core ideas. National Academies Press, Washington, DC.
National Research Council. (2012b) Discipline-based education research: understanding and improving learning in undergraduate science and engineering. National Academies Press, Washington, DC.
Nicolescu, B. (2004). Gurdjieff’s philosophy of nature. In J. Needleman & G. Baker (Eds.), Gurdjieff (pp. 37- 69). New York, NY: The Continuum International Publishing Group.
Nowotny, H. (2003). Democratising expertise and socially robust knowledge. Science and public policy, 30(3), 151-156. https://doi.org/10.3152/147154303781780461.
Przybylla, M., & Romeike, R. (2014). Physical computing and its scope-towards a constructionist computer science curriculum with physical computing. Informatics in Education, 13(2), 225. https://doi.org/10.15388/infedu.2014.05.
Psycharis, S. (2015). The Impact of Computational Experiment and Formative Assessment in Inquiry Based Teaching and Learning Approach in STEM Education. Journal of Science Education, and Technology (JOST), 25(2), 316-326. https://doi.org/10.1007/s10956-015-9595-z.
Psycharis, S. (2016). Inquiry Based- Computational Experiment, Acquisition of Threshold Concepts and Argumentation in Science and Mathematics Education. Educational Technology & Society, 19(3), 282-293.
Psycharis, S. (2018). STEAM In Education: Literature review on the role of computational thinking, engineering epistemology and computational science. Computational STEAM Pedagogy (CSP). Scientific Culture, 4(2), 51-72. https://doi.org/10.5281/zenodo.1214565.
Psycharis, S., Kalovrektis, K., Sakelalridi, E., Korres, K., & Mastorodimos, D. (2017). Unfolding the Curriculum: Physical Computing, Computational Thinking and Computational Experiment in STEM’s Transdisciplinary Approach. European Journal of Engineering Research and Science (EJERS). https://doi.org/10.24018/ejers.2018.0.
Psycharis, S., & Kotzampasaki, E. (2017). A didactic Scenario for Implementation of Computational Thinking using Inquiry Game Learning. In Proceedings of the 2017 International Conference on Education and E-Learning, Bangkok Thailand 2-4 November 2017 (pp. 26-29). ACM. https://doi.org/10.1145/3160908.3160918.
Qiu, K., Buechley, L., Baafi, E., & Dubow, W. (2013, June). A curriculum for teaching computer science through computational textiles. In Proceedings of the 12th International Conference on Interaction Design and Children (pp. 20-27). ACM. https://doi.org/10.1145/2485760.2485787.
Rubio, M. A., Hierro, C. M., & Pablo, A. P. D. M. (2013, July). Using arduino to enhance computer programming courses in science and engineering. In Proceedings of EDULEARN13 conference (pp. 5127-5133).
Rugarcia, A., Felder, R. M., Woods, D. R. & Stice, J. E. (2000). The future of engineering education: I. A vision for a new century. Chemical Engineering Education, 34, 16–25.
Schulz, S., & Pinkwart, N. (2015, November). Physical computing in stem education. In Proceedings of the Workshop in Primary and Secondary Computing Education (pp. 134-135). ACM. https://doi.org/10.1145/2818314.2818327.
Selby, C., & Woollard, J. (2014). Refining an understanding of computational thinking. Author’s Original, 1-23.
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.
Sentance, S., & Csizmadia, A. (2015). Teachers’ perspectives on successful strategies for teaching Computing in school. Paper presented at IFIP TCS, 2015. http://community.computingatschool.org.uk/files/6769/.
original.pdf.
Shirey, K. (2017). Teacher Productive Resources for Engineering Design Integration in High School Physics Instruction (Fundamental). In Proceedings of the 2017 ASEE Annual Conference, Columbus, OH, June 2017. https://doi.org/10.18260/1-2--28908.
Stankov, L., Lee, J., Luo, W., & Hogan, D. J. (2012). Confidence: A better predictor of academic achievement than self-efficacy, self-concept and anxiety?. Learning and Individual Differences, 22(6), 747-758. https://doi.org/10.1016/j.lindif.2012.05.013.
Voogt, J., Fisser, P., Good, J., Mishra, P., & Yadav, A. (2015). Computational thinking in compulsory education: Towards an agenda for research and practice. Education and Information Technologies, 20(4), 715-728. https://doi.org/10.1007/s10639-015-9412-6.
Weinberg, B. A. (2009). A model of over-confidence. Pacific Economic Review, 14, 502–515. https://doi.org/10.1111/j.1468-0106.2009.00466.x.
Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., &Wilensky, U. (2016). Defining Compu-tational Thinking for Mathematics and Science Classrooms. Journal of Science Education and Technol-ogy, 25(1), 127-147. https://doi.org/10.1007/s10956-015-9581-5.
Wing, J. M .(2006). Computational thinking. Communications of the ACM, 49, 33-35. https://doi.org/10.1145/1118178.1118215.
Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical transactions of the royal society of London A: mathematical, physical and engineering sciences, 366(1881), 3717-3725. https://doi.org/10.1098/rsta.2008.0118.
Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., &Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education (TOCE), 14(1), 5. https://doi.org/10.1145/2576872.
Yadav, A., Zhou, N., Mayfield, C., Hambrusch, S., &Korb, J. T. (2011). Introducing computational thinking in education courses. In Proceedings of the 42nd ACM technical symposium on Computer science education (pp. 465-470). ACM. https://doi.org/10.1145/1953163.1953297.
Yasar O., Veronesi P., Maliekal J., Little L. J., Vattana S. E., & Yeter I. H. (2016). Computational Pedagogy: Fostering a New Method of Teaching. Presented at: ASEE Annual Conference and Exposition. Presented: June 2016. Project: SCOLLARCIT.
Yaşar, O. (2004). Computational math, science and technology: A new pedagogical approach to math and science education. In: Lagana, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) ICCSA 2004. LNCS, 3045, 807-816. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24767-8_85.
Yaşar, O. (2013). Teaching Science through Computation. International Journal of Science, Technology and Society, 1(1), 9-18. https://doi.org/10.11648/j.ijsts.20130101.12.
Yaşar, O., & Landau, R (2003): Elements of CSE Education. SIAM Review, 45(4), 787–805. 72.
Zieris, H., Gerstberger, H., & Müller, W. (2015). Using Arduino-Based Experiments to Integrate Computer Science Education and Natural Science (Eds.). KEYCIT 2014: key competencies in informatics and ICT (Vol. 7). Universitätsverlag Potsdam.

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APA

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

Vancouver

Psycharis S, Kotzampasaki E. The Impact of a STEM Inquiry Game Learning Scenario on Computational Thinking and Computer Self-confidence. EURASIA J Math Sci Tech Ed. 2019;15(4):em1689. https://doi.org/10.29333/ejmste/103071

AMA

Psycharis S, Kotzampasaki E. The Impact of a STEM Inquiry Game Learning Scenario on Computational Thinking and Computer Self-confidence. EURASIA J Math Sci Tech Ed. 2019;15(4), em1689. https://doi.org/10.29333/ejmste/103071

Chicago

Psycharis, Sarantos, and Evangelia Kotzampasaki. "The Impact of a STEM Inquiry Game Learning Scenario on Computational Thinking and Computer Self-confidence". Eurasia Journal of Mathematics, Science and Technology Education 2019 15 no. 4 (2019): em1689. https://doi.org/10.29333/ejmste/103071

Harvard

Psycharis, S., and 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

MLA

Psycharis, Sarantos et al. "The Impact of a STEM Inquiry Game Learning Scenario on Computational Thinking and Computer Self-confidence". Eurasia Journal of Mathematics, Science and Technology Education, vol. 15, no. 4, 2019, em1689. https://doi.org/10.29333/ejmste/103071