A Conceptual Framework for Computational Pedagogy in STEAM education: Determinants and perspectives

Abstract views: 2233 / PDF downloads: 1699





STEM Education, Computational Pedagogy, Computational Thinking, Epistemology


Computational Pedagogy is an instructional approach based on Computational Science and the Computational Experiment as well as on the CPACK model. Computational Science in Education engages students in computational modeling and simulation technology in alignment with the essential features of Inquiry based teaching and learning approach and the Computational Thinking dimensions (practices and skills). STEAM –content based epistemology- education is connected to Computational Pedagogy through the Computational Experiment leading to a proposed model called ‘Computational STEAM Content Pedagogy’ as a teaching and learning approach which can be implemented in a STEAM holistic interdisciplinary/trans-disciplinary epistemology approach to the curriculum for solving real computational problems.


Download data is not yet available.

Author Biography

Apostolos Xenakis, University of Thessaly




Aho, A. V. (2012). Computation and computational thinking. Computer Journal, 55(7), 832 – 835.

Antola Crowe, H., Brandes, K., Davison Avilés, B., Erickson, D., & Hall, D. (2013). Transdisciplinary teaching: Professionalism across cultures. International Journal of Humanities and Social Science, 3(13), 194–205.

Assay, L. D., & Orgill, M. K. (2010). Analysis of Essential Features of Inquiry Found in Articles Published in The Science Teacher, 1998–2007. Journal of Science Teacher Education, 21(1), 57-79, DOI: 10.1007/s10972-009-9152-9

Ates, S., & Cataloglu, E. (2007). The effects of students’ reasoning abilities on conceptual understandings and problem-solving skills in introductory mechanics. European Journal of Physics, 28(6), 1161–1171.

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. doi:10.1145/1929887.1929905

Bell, P. L., Hoadley, C., & Linn, M. C. (2004). Design-based research as educational inquiry. In M. C. Linn, E. A. Davis & P. L. Bell (Eds.), Internet environments for science education. Mahwah, NJ: Lawrence Erlbaum Associates.

Bell, T., Urhahne, D., Schanze, S., & Ploetzner, R. (2010). Collaborative inquiry learning: models, tools and challenges. International Journal of Science Education, 32(3), 349-377.

Bequette, M., & Bequette, J. (2011). STEM plus arts make STEAM? Effective integration of aesthetic-based problem solving across topic areas. STEM Colloquium. Minnesota.

Beth, E. W., & Piaget, J. (1966). Mathematical Epistemology and Psychology. Reidel: Dordrecht, The Netherlands.

Bienkowski, M., Snow, E., Rutstein, D. W., & Grover, S. (2015). Assessment design patterns for computation-al thinking practices in secondary computer science: A first look (SRI technical report). Menlo Park, CA: SRI International.

Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., & Engelhardt, K., (2016). Developing computational thinking in compulsory education - Implications for policy and practice. JRC Science for Policy Report. doi:10.2791/792158.

Borrego, M., & Newswander, L. K. (2008). Characteristics of Successful Cross-disciplinary Engineering Education Collaborations. Journal of Engineering Education, 97(2), 123-134.

Breiner, J. M., Harkness, S. S., Johnson, C. C., & Koehler, C. M. (2012). What is STEM? A discussion about conceptions of STEM in education and partnerships. School Science and Mathematics, 112(1), 3–11.

Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 Annual Meeting of the American Educational Research Association, Vancouver, BC, Canada, 13–17 April 2012.

Bundy, A. (2007). Computational thinking is pervasive. Journal of Scientific and Practical Computing, 1(2), 67–69.

Bybee, R. W., Trowbridge, L. W. & Powell, J. C. (2008). Teaching Secondary School Science: Strategies for Developing Scientific Literacy. New Jersey Merrill.

Campbell, D. & Samsel, F. (2015) Pursuing value in art-science collaborations. IEEE Comput. Graph. Appl., 35(1):6-11. DOI: 10.1109/MCG.2015.18.

Carlson, L. E., & Sullivan, J. F. (2004). Exploiting design to inspire interest in engineering across the K-16 engineering curriculum. International Journal of Engineering Education, 20(3), 372-378.

Cetin, I., & Dubinsky, E. (2017). Reflective abstraction in computational thinking. The Journal of Mathematical Behavior, 47, 70-80.

Chande, S. (2015). A Conceptual Framework for Computational Thinking as a Pedagogical Device. International Journal of Innovative Research in Computer and Communication Engineering, 3(11), 11682-11688.

Chandler, J., Fontenot, A. D., & Tate, D. (2011). Problems associated with a lack of cohesive policy in K-12 precollege engineering. Journal of Pre-College Engineering Education Research (J-PEER) 1, 5.

Chittleborough, G., & Treagust, D. (2007). The modelling ability of non-major chemistry students and their understanding of the sub-microscopic level. Chemistry Education Research and Practice, 8 (3), 274-292.

Computing at School Working Group (2012). Computer Science: A Curriculum for Schools. Available at https://www.computingatschool.org.uk/data/uploads/ComputingCurric.pdf

Cross, J. (2017). Doctoral dissertation). Technical Report CMU-RI-TR-17-30. Pittsburgh, PA: The Robotics Institute, Carnegie Mellon University.

Daugherty, M. (2013). The Prospect of an ‘A’ in STEM Education. The Journal of STEM Education, April-June 2013,


Dede, C., Mishra, P., & Voogt, J. (2013). Working group 6: Advancing computational thinking in 21st century learning. http://www.curtin.edu.au/edusummit/local/docs/Advancing_computational_thinking_in_21st century_learning.pdf Accessed 28 January 2017.

Denning, P. J. (2003). Great Principles of Computing. Communications of the ACM, 46(11). 15-20.

Denning, P. J. (2007). Computing is a natural science. Communications of the ACM, 50, 13-18.

Egodawatte, G. (2011). Secondary School students’ misconceptions in Algebra. Unpublished Doctoral Dissertation. University of Toronto, Toronto, CA.

Eisner, E. W. (2002). The arts and the creation of mind. Yale University Press.

Feldhausen, R., Weese, J. L., & Bean. N. H. (2018). Increasing Student Self-Efficacy in Computational Thinking via STEM Outreach Programs. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education, February (pp. 302–307).

Ghanbari, S. (2015). Learning across disciplines: A collective case study of two university programs that integrate the arts with STEM. International Journal of Education & the Arts, 16(7). Retrieved from http://www.ijea.org/v16n7/

Goris, T. V., & Dyrenfurth, M. J. (2010) Students’ Misconceptions in Science, Technology and Engineering. ASEE Illinois/Indiana Section Conference Proceeding, Purdue University, West Lafayette, IN. Retrieved from http://ilin.asee.org/Conference2010/Papers2010.html

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

Hestenes, D. (1999). The scientific method. American Journal of Physics, 67, 273-276.

Hußmann, S., Schacht, F., & Schindler, M. (2019). Tracing conceptual development in mathematics: epistemology of webs of reasons. Mathematics Education Research Journal, 31(2), 133-149.

International Technology Education Association (ITEA) (2007). Standards for Technological Literacy: Content for the Study of Technology. ITEA: Reston, VA.

Jona, K., Wilensky, U., Trouille, L., Horn, M. S., Orton, K., Weintrop, D., & Beheshti, E. (2014). Embedding computational thinking in science, technology, engineering, and math (CT-STEM). In Future Directions in Computer Science Education Summit Meeting, Orlando, FL.

Justi, R. S. & Gilbert, J. K., (2002). Models and modelling in chemical education, In J. Gilbert, O. De Jong, R. Justi, D. F. Treagust and J. H. Van Driel (Eds.), Chemical education: towards research-based practice (pp. 213 - 234), Dordrecht: Kluwer Academic Publishers.

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 DOI: 10.5604/23920092.1159113

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.

Klahr, D., & Dunbar, K. (1998). Dual space search during scientific reasoning. Cognitive Science, 12, 1-48.

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.

Kroes, P., & Van de Poel, I. (2009). Problematizing the notion of Social Context of Technology. In S. H. Christensen, B. Delahousse, & M. Meganck (Eds.), Engineering in Context (pp. 61-74). Denmark: Academica.

Landau, R. H., Páez, J. & Bordeianu, C. (2008). A Survey of Computational Physics: Introductory Computational Science. Princeton and Oxford: Princeton University Press.

Lawson, A. E., Banks, D. L., & Logvin, M. (2007). Self-efficacy, reasoning ability, and achievement in college biology. Journal of Research in Science Teaching, 44(5), 706–724.

Libow Martinez, S. & Stager, G. (2013). Invent to Learn - Making, Tinkering, and Engineering in the Classroom. Torrance, CA: Constructing Modern Knowledge Press.

Magnani, L., & Nersessian, N. (eds.) (2002). Model-Based Reasoning: Science, Technology, Values. Dordrecht: Kluwer

Martin, F., Greher, G., Heines, J, Jeffers, Kim, H.J., Kuhn, S., Roehr, K.,. Selleck, N., Silka, L., & Yanco, H. (2009): Joining computing and the arts at a mid-size university. J. Comput. Sci. Coll. 24(6), 87–94.

McGregor, S. (2015). Transdisciplinary knowledge creation. In P. T. Gibbs (Ed.), Transdisciplinary professional learning and practice (pp. 9-24). New York, NY: Springer.

Mendonça, P.C.C. & Justi, R. (2013). An instrument for analyzing arguments produced in modeling-based chemistry lessons. Journal of Research in Science Teaching, 51(2), 192–218. doi:10.1002/tea.21133.

Mishra, P. & Yadav, A. (2013). Of art and algorithms: Rethinking technology & creativity in the 21st century. TechTrends, 57(3), 11.

Mitcham, C. (1994). Thinking through technology: the path between engineering and philosophy. Chicago: the University of Chicago Press.

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.

Moreno-León, J., Robles, G., & Román-González, M.D. (2015). Scratch: Automatic analysis of scratch projects to assess and foster computational thinking. RED. Revista de Educación a Distancia, 46, 1–23.

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, DC.

National Academies of Sciences, Engineering and Medicine, (2014). STEM Integration in K-12 Education: Status, Prospects, and an Agenda for Research. Retrieved from http://nap.edu/18612

National Research Council. (2010). Report of a workshop on the scope and nature of computational thinking. The National Academies Press, Washington, DC.

National Research Council (2011a) Learning science through computer games and simulations. The National Academies Press, Washington, DC.

National Research Council. (2011b) Report of a workshop of pedagogical aspects of computational thinking. The National Academies Press, Washington, DC.

NGSS Lead States (2013). Next generation science standards: for states, by states. The National Academies Press, Washington, DC.

Nersessian, N. (1992). How do scientists think? Capturing the dynamics of conceptual change in science. In R. Giere (Ed.), Cognitive models of science (pp. 3-44). Minneapolis: University of Minnesota Press.

Nicolescu, B. (2002). Manifesto of transdisciplinarity [Trans. K-C. Voss]. New York, NY: SUNY.

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

Nievergelt, J. (1974). Computers and Mathematics Education. Compo & Moths. with Appls, 1, 121·132, Pergamon Press.

Okabe, A., Boots, B., &, Sugihara, K. (1992). Spatial Tessellations: Concepts and Applications of Voronoi Diagrams. Wiley, New York, 1992

OpenLearn from The Open University. (n.d. a). Retrieved from https://www.open.edu/openlearn/science-maths-technology/computing-and-ict/introduction-computational-thinking/content-section-2

OpenLearn from The Open University. (n.d. b). Retrieved from https://www.open.edu/openlearn/science-maths-technology/computing-and-ict/introduction-computational-thinking/content-section-2.5

Pedaste, M., & Palts, T. (2017). Tasks for Assessing Skills of Computational Thinking. The 2017 ACM Conference.

Piaget, J. (1970). Genetic epistemology. New York: Columbia University Press.

Przybylla, M., & Romeike, R. (2014). Physical computing and its scope - towards a constructionist computer science curriculum with physical computing. Informatics in Education, 13(2), 225-240.

Psycharis, S. (2013). The Effects of the Computational Models on Learning Performance, Scientific Reasoning, Epistemic Beliefs and Argumentation. Computers & Education, 68, 253–265. DOI: 10.1016/j.compedu.2013.05.015

Psycharis, S. (2016a). The Impact of Computational Experiment and Formative Assessment in Inquiry Based Teaching and Learning Approach in STEM Education. Journal of Science Education, and Technology, 25(2), 316-326

DOI: 10.1007/s10956-015-9595-z

Psycharis, S., (2016b). Inquiry Based- Computational Experiment, Acquisition of Threshold Concepts and Argumentation in Science and Mathematics Education. Educational Technology & Society, 19(3), 282–293.

Psycharis,S., Kalovrektis, K., Sakelalridi, E., & Korres, K., Mastorodimos, D. (2018). Unfolding the Curriculum: Physical Computing, Computational Thinking and Computational Experiment in STEM’s Transdisciplinary Approach. European Journal of Engineering Research and Science. Special Issue: CIE 2017, 19-27. https://doi.org/10.24018/ejers.2018.0.CIE.639

Psycharis, S (2018). STEAM in Education: A Literature review on the role of Computational Thinking, Engineering Epistemology and Computational Science. Computational STEAM Pedagogy (CSP). Scientific Culture, 4(2), 51-72. https://sci-cult.com

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

Psycharis, S. (2019). Relogia Interview, Art-sci-technology- relogia.net. Sofia.

Qiu, K., Buechley, L., Baafi, E. & Dubow, W. (2013). A curriculum for teaching computer science through computational textiles. Proc. 12th Int. Conf. Interact. Des. Child. - IDC ’13, pp. 20–27.

Repenning, A., Webb, D., &Ioannidou, A. (2010). Scalable game design and the development ofa checklist for getting computational thinking into public schools. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education (pp. 265–269).

Rubio, M.A., ManosoHierro, C. & Perez de Madrid y Pablo, A. (2013). Using Arduino to enhance computer programming courses in science and engineering. Proceedings of EDULEARN13 Conference 1st-3rd July 2013, Barcelona, Spain.

Schnittka, C. G., Bell, R. L., & Richards, L. G. (2010). Save the penguins: Teaching the science of heat transfer through engineering design. Science Scope, 34(3), 82-91.

Schulz, S. & Pinkwart, N. (2015). Physical Computing in STEM Education. Retrieved from: https://cses.informatik.hu-berlin.de/pubs/2015/wipsce/physical-computing-in-stem-education.pdf

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.

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

Shunn, C., & Klahr, D. (1995). A 4-space model for scientific discovery. In Paper presented at the AAAI symposium systematic methods of scientific discovery, Menlo Park, CA.

Sloot, P. (August 1994). Lecture on parallel scientific computing and simulations. Sopron, Hungary: CERN School on Computing.

Swoyer, C. (1991). Structural Representation and Surrogative Reasoning. Synthese, 87, 449-508.

Toomey, A.H., Markusson, N., Adams, E. & Brockett, B. (2015). Inter- and trans-disciplinary research: a critical perspective. GSDR 2015 Brief. Retrieved from https://sustainabledevelopment.un.org/content/documents/612558-Inter-%20and%20Trans-disciplinary%20Research%20-%20A%20Critical%20Perspective.pdf

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

Weese, J., & Feldhausen, R. (2017). STEM Outreach: Assessing Computational Thinking and Problem Solving. 2017 ASEE Annual Conference & Exposition. ASEE Conferences, Columbus, Ohio. https://peer.asee.org/28845

Weese, J. L., Feldhausen, R., & Bean, N. H. (2016). The Impact of STEM Experiences on Student Self-Efficacy in Computational Thinking. Proceedings of the 123rd American Society for Engineering Education Annual Conference and Exposition (ASEE 2016). New Orleans, LA, USA.

Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining Computational Thinking for Mathematics and Science Classrooms. Journal of Science Education and Technology, 25(1), 127-147.

DOI: 10.1007/s10956-015-9581-5

White, B., Frederiksen, J., & Spoehr, K. (1993). Conceptual models for understanding the behavior of electrical circuits. In M. Caillot (Ed.) learning Electricity and Electronics with Advanced Educational Technology (pp. 77-95). New York: Springer-Verlag.

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.

Wolfram, S. (2002). A New Kind of Science (1st ed.) Wolfram Media: Tokyo.

Xie, C., Tinker, R., Tinker, B., Pallant, A., Damelin, D., & Berenfeld, B. (2011). Computational experiments for science education. Science, 332, 1516–1517.

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, ACM, pp 465–470.

Yaşar, O. & Landau, R. (2003): Elements of CSE Education. SIAM Review, 45(4), 787–805.

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.

Yaşar, O. (2013). Teaching Science through Computation. International Journal of Science, Technology and Society, 1(1), 9-18. DOI: 10.11648/j.ijsts.20130101.12

Yaşar, O., Veronesi, P., Maliekal, J. and Little, L. (2015). Computational Pedagogical Content Knowledge (CPACK). In D. Slykhuis & G. Marks (Eds.), Proceedings of Society for Information Technology & Teacher Education Conference 2015 (pp. 3514-3521).

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.

Zendler, A. & Spannagel, C. (2008). Empirical Foundation of Central Concepts for Computer Science Education. ACM Journal on Educational Resources in Computing, 8(2), 6.

Zhang, Y. & Luo, C. (2012). Training for Computational Thinking Capability on Programming Language Teaching. The 7th International Conference on Computer Science & Education (ICCSE 2012), 1804-1809. IEEE.

DOI: 10.1109/ICCSE.2012.6295420




How to Cite

Psycharis, S., Kalovrektis, K., & Xenakis, A. (2020). A Conceptual Framework for Computational Pedagogy in STEAM education: Determinants and perspectives. Hellenic Journal of STEM Education, 1(1), 17–32. https://doi.org/10.51724/hjstemed.v1i1.4