Algorithm Visualization and its Impact on Self-efficacy, Metacognition and Computational Thinking Concepts Using the Computational Pedagogy Model in STEM Content Epistemology


  • Sarantos Psycharis School of Pedagogical and Technological Education (ASPETE)
  • Dimitris Mastorodimos Greek Ministry of Education Research and Religion, Greece
  • Konstantinos Kalovrektis University of Thessaly, Greece
  • Panagiotis Papazoglou Technological Educational Institute of Central Greece, Greece
  • Lampros Stergioulas University of Surrey, UK
  • Munir Abbasi University of Surrey, UK


Visualization, Algorithms, self-efficacy, metacognition, Computational Thinking, STEM, Computational Pedagogy


The objective of this article is twofold. One objective is the development of models of visualized algorithms (VAs) for three fundamental algorithms, the bubble sort algorithm, the selection sort algorithm and the insertion sort algorithm, using the Easy Java simulations software (Ejs) and the Computational Pedagogy model. The second objective is to investigate: a) VAs impact on learners’ self-efficacy as a general structure, metacognitive experience, critical thinking and motives and b) VAs impact on learners’ self-efficacy relative to Computational Thinking. An intervention in the form of a didactic model was implemented that utilized VAs and the Computational Pedagogy approach. Finally, we argument how VAs can be embedded in the Computational STEM pedagogy approach in teaching and learning sequences through applications related to authentic problems.


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

Anastasiadou, S.D., & Karakos, A.S. (2011). The beliefs of electrical and computer engineering students regarding computer programming. The International Journal of Technology, Knowledge and Society, 7(1), 37-51.

Andrienko, N., Andrienko, G., Barrett, L., Dostie, M., Henzi, S.P. (2013). Space Transformation for Understanding Group Movement. IEEE Trans Vis Comput Graph., 19(12), 2169–78.

Armoni, M. (2011). The nature of CS in K-12 curricula: the roots of confusion. ACM Inroads, 2(4), 19-20. doi:10.1145/2038876.2038883

Aşkar, P., & Davenport, D. (2009). An investigation of factors related to self-efficacy for java Programming among engineering students. The Turkish Online Journal of Educational Technology TOJET, 8(1): 26-32.

Baecker, R. (1998). Sorting Out Sorting: A Case Study of Software Visualization for Teaching Computer Science. In J. Stasko, J. Domingue, M. H. Brown, and B. A. Price (Eds.) Software Visualization, MIT Press, pp. 369--381.

Baird, J.R. (1990). Metacognition, purposeful enquiry and conceptual change. In E. Hegarty-Hazel (Ed.), The student laboratory and the science curriculum (pp. 183–200). London: Routledge.

Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: W.H. Freeman & Company.

Bean, N., Weese, J., Feldhausen, R., & Bell, R. S. (2015). Starting from scratch: Developing a pre-service teacher training program in computational thinking. In Frontiers in Education Conference (FIE), 2015 IEEE (pp. 1-8). IEEE.

Bell, R. S. (2014). Low Overhead Methods for Improving Capacity and Outcomes in Computer Science. Manhattan, KS: Kansas State University.

Bienkowski, M., Snow, E., Rutstein, D. W., & Grover, S. (2015). Assessment design patterns for computational thinking practices in secondary computer science: A first look. SRI International

Blank, L.M. (2000). A metacognitive learning cycle: A better warranty for student understanding? Science Education, 84(4), 486–506.

Boticki, I., Barisic, A., Martin, S., & Drljevic, N. (2013). Teaching and learning computer science sorting algorithms with mobile devices: A case study. Computer Applications in Engineering Education, 21(S1), E41-E50.

Brennan, K., & Resnick, M. (2012). Using artifact-based interviews to study the development of computational thinking in interactive media design. In Annual American Educational Research Association meeting, Vancouver, BC, Canada.

Brusilovsky, P., & Su, H. D. (2002, June). Adaptive visualization component of a distributed web-based educational system. In 6th International Conference on Intelligent Tutoring Systems (pp. 229-238). Springer Berlin Heidelberg.

Cetin, I., & Andrews-Larson, C. (2016). Learning sorting algorithms through visualization construction. Computer Science Education, 26(1), 27-43.

Chartier, T., & Kreutzer, E. (2010). How easy is ‘easy java simulations’ programming? Retrieved on December 28, 2016 from:

Chemers, M. M., Hu, L. T., & Garcia, B. F. (2001). Academic self-efficacy and first year college student performance and adjustment. Journal of Educational psychology, 93(1), 55.

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

DfE Computing programmes of study Key Stages 1 and 2 National Curriculum in England (2013). Department of Education. Retrieved from

Erdogan, Y., Aydin, E., & Kabaca, T. (2008). Exploring the Psychological Predictors of Programming Achievement. Journal of Instructional Psychology, 35(3).

Fife-Schaw, C. (2000). Quasi-experimental designs. In G. M. Breakwell, J. A. Smith, & D. B. Wright (Eds.), Research methods in psychology (pp. 74–87). California: SAGE Publications Ltd.

Fouh, E., Akbar, M., & Shaffer, C. A. (2012). The role of visualization in computer science education. Computers in the Schools, 29(1-2), 95-117.

Futschek, G. (2006, November). Algorithmic thinking: the key for understanding computer science. In International conference on informatics in secondary schools-evolution and perspectives (pp. 159-168). Springer, Berlin, Heidelberg.

Gilbert, J.K. (2005). Visualization: A metacognitive skill in science and science education. In J.K. Gilbert (Ed.), Visualization in Science Education (pp. 9–27).Dordrecht, The Netherlands, Springer.

Grover, Pea, & Cooper. (2015). Designing for deeper learning in a blended computer science course for middle school students. Computer Science Education, 25(2), 199–237.

Gokcearslan, Ş., & Alper, A. (2015). The effect of locus of control on learners' sense of community and academic success in the context of online learning communities. The Internet and Higher Education, 27, 64-73. Doi: 10.1016/j.iheduc.2015.06.003

Hundhausen, C. D., Douglas, S. A., & Stasko, J. T. (2002). A meta-study of algorithm visualization effectiveness. Journal of Visual Languages & Computing, 13(3), 259-290.

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

Kalelioğlu, F. (2015). A new way of teaching programming skills to K-12 students: Computers in Human Behavior, 52, 200-210. doi:10.1016/j.chb.2015.05.047

Katai, Z. (2014).Intercultural computer science education. In Proceedings of the 2014 conference on Innovation & technology in computer science education (pp. 183-188). ACM. 19(1) (2014): 183-188. doi: 10.1145/2591708.2591744

Kölling, M. (2015). Lessons from the Design of Three Educational Programming Environments: Blue, BlueJ and Greenfoot. International Journal of People-Oriented Programming (IJPOP), 4(1), 5–32.

Konecki, M. & Mrkela, V. (2014). Students’ acceptance of animated interactive presentation of sorting algorithms. In Proceedings of the 17th International Multiconference Information Society - Human-Computer Interaction in Information Society (pp. 18-21), Ljubljana, Slovenia.

Korkmaz, Ö., & Altun, H. (2014). Adapting Computer Programming Self-Efficacy Scale and Engineering Students’ Self-Efficacy Perceptions. Participatory Educational Research (PER), 1(1), 20-31.

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

Lu, J. J., & Fletcher, G. H. (2009). Thinking about computational thinking. ACM SIGCSE Bulletin, 41(1), 260-264.Proceedings of the 40th ACM technical symposium on Computer science education, March 04-07, 2009, Chattanooga, TN, USA. Doi:10.1145/1508865.1508959

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.doi:10.1016/j.chb.2014.09.012

Millsap, R. E., & Maydeu-Olivares, A. (2009). The SAGE handbook of quantitative methods in psychology. Thousand Oaks: SAGE.

Naps, T. L., Rößling, G., Almstrum, V., Dann, W., Fleischer, R., Hundhausen, C., & Velázquez-Iturbide, J. Á. (2002, June). Exploring the role of visualization and engagement in computer science education. In ACM Sigcse Bulletin, 35(2), 131-152.

National Research Council. (2013). Next generation science standards: For states, by states.

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, CAE2501, PII: S0360-1315(13)00278-9, DOI: 10.1016/j.compedu.2013.10.001

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 (JOST) 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 Journal. Educational Technology & Society, 19(3).

Psycharis, S (2018a) 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.

Psycharis, S. (2018b). Computational Thinking, Engineering Epistemology and STEM Epistemology: A primary approach to Computational Pedagogy. International Conference on Interactive Collaborative Learning, ICL 2018: The Challenges of the Digital Transformation in Education, 689-698.

Ramalingam, V., & Wiedenbeck, S. (1998). Development and validation of scores on a computer programming self-efficacy scale and group analyses of novice programmer self-efficacy. Journal of Educational Computing Research, 19(4), 367-381.

Ramalingam, V., LaBelle, D., & Wiedenbeck, S. (2004). Self-efficacy and Mental Models in Learning to Program. ACM SIGCSE Bulletin, 36(3), 171-175.

Reif, I., & Orehovacki, T. (2012, May). ViSA: Visualization of sorting algorithms. In Proceedings of the 35th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (pp. 1146-1151). Opatija, Croatia: IEEE.

Resnick, M., Maloney, J., Monroy-Hernandez, A., Rusk, N., Eastmond, E., Brennan, K., et. al. (2009). Scratch: Programming for all. Communications of the ACM, 52(11), 60-67.

Seiter, L., & Foreman, B. (2013). Modeling the learning progressions of computational thinking of primary grade students. Proceedings of the ninth annual international ACM conference on International computing education research (pp. 59–66). ACM.

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.

Shaffer, C. A., Cooper, M., & Edwards, S. H. (2007). Algorithm visualization: a report on the state of the field. ACM SIGCSE Bulletin, 39(1), 150-154.

Shaffer, C. A., Cooper, M. L., Alon, A. J. D., Akbar, M., Stewart, M., Ponce, S., & Edwards, S. H. (2010). Algorithm visualization: The state of the field. ACM Transactions on Computing Education (TOCE), 10(3), 9.

Shaffer, C. A., Akbar, M., Alon, A. J. D., Stewart, M., & Edwards, S. H. (2011, March). Getting algorithm visualizations into the classroom. In Proceedings of the 42nd ACM technical symposium on Computer science education (pp. 129-134). ACM.

Shaw, K., Gurkas, P., & Webster, Z. (2012). An Analysis of Factors Expected to Impact Student End-of-Course Grades in Introductory College Science Classes. Perspectives in Learning, 13(1), 5.

Staley, J. D. (2006). Imagining the multisensory classroom. Campus Technology, 6(6).

Thomas, K., & Velthouse, B. (1990). Cognitive elements of empowerment: An interpretive model intrinsic task motivation. Academy of Management Review, 15, 666–681.

Thomas, G.P., & McRobbie, C.J. (2001). Using a metaphor for learning to improve students’ metacognition in the chemistry classroom. Journal of Research in Science Teaching, 38(2), 222–259.

Thomas, G., Anderson, D., & Nashon, S. (2008). Development of an instrument designed to investigate elements of science students’ metacognition, self‐efficacy and learning processes: The SEMLI‐S. International Journal of Science Education, 30(13), 1701-1724.

Törley, G. (2014). Algorithm visualization in teaching practice. Acta Didactica Napocensia, 7(1), 1.

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.

Weinberg, A. E. (2013). Computational thinking: An investigation of the existing scholarship and research. (Unpublished Doctoral Thesis), Colorado State University, School of Education, Colorado.

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.

Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.

Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical transactions of the royal society 366(1881), 3717-3725.

Wing, J. (2011). Research notebook: Computational thinking-What and why? The Link Magazine. Retrieved from

Yasar, O., Veronesi, P., Maliekal, J., Little, L. J., Vattana, S. E., Yeter, I. H. (2016). Presented at: ASEE Annual Conference and Exposition. Presented: June 2016. Project: SCOLLARCIT

Wolber, D., Abelson, H., Spertus, E., & Looney, L. (2015). App Inventor 2: Create your own Android apps. Sebastopol, CA: O’Reilly

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




How to Cite

Psycharis, S., Mastorodimos, D., Kalovrektis, K. ., Papazoglou, P., Stergioulas, L., & Abbasi, M. (2018). Algorithm Visualization and its Impact on Self-efficacy, Metacognition and Computational Thinking Concepts Using the Computational Pedagogy Model in STEM Content Epistemology. International Journal of Physics &Amp; Chemistry Education, 10(4), 71–84. Retrieved from