Exploring and comparing computational thinking skills in students who take GCSE Computer Science and those who do not



  • Lauren Gillott Mulberry Academy Shoreditch
  • Andrew Joyce-Gibbons Grupo de Análisis para el Desarrollo de Perú
  • Elizabeth Hidson University of Sunderland http://orcid.org/0000-0001-7387-5666


computational thinking, computer science, computing, automation, GCSE


This study compares computational thinking skills evidenced by two groups of students in two different secondary schools: one group per school was studying a qualification in Computer Science. The aim was to establish which elements of computational thinking were more prevalent in students studying Computer Science to a higher level. This in turn would evidence those elements likely to be present from their earlier computing education or through their complementary studies in Science or Mathematics, which all students also studied. Understanding this difference was important to identify any increased competence in computational thinking that was present in the Computer Science groups. Artefact-based interviews were carried out using questions and practical computing problems designed to elicit and demonstrate the students’ computational thinking skills based on the Brennan and Resnick (2016) model of computational concepts, practices and perspectives. Analysis of students’ responses showed surprisingly little difference between the computational thinking practices of the two groups in relation to abstraction, decomposition, evaluation, generalisation/reusing, logical reasoning and debugging/testing. The study concludes that general computational thinking skills can be developed either at a lower level of study or in cognate curriculum areas, leaving computer science as the rightful locus of computational thinking for automation.  


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Author Biographies

Lauren Gillott, Mulberry Academy Shoreditch

Lauren Gillott is a Computer Science teacher at Mulberry Academy Shoreditch, London; she researches and teaches Computer Science and computational thinking.

Andrew Joyce-Gibbons, Grupo de Análisis para el Desarrollo de Perú

Andrew Joyce-Gibbons is an Honorary Fellow at Durham University School of Education; his research interests focus on pedagogies for collaborative learning, CSCL and Computational Thinking.

Elizabeth Hidson, University of Sunderland

Elizabeth Hidson is a Senior Lecturer in Education at the University of Sunderland; her research interests include technology-enhanced learning and computing education.


Allsop, Y., (2019). Assessing computational thinking process using a multiple evaluation approach. International journal of child-computer interaction, 19, pp.30-55.

Barr, V. & Stephenson, C. (2011). Bringing computational thinking to K-12. ACM Inroads, 2(1), 48-54. doi:10.1145/1929887.1929905

Berry, M., (2019). ‘What I’m thinking about computational thinking’. Hello World #8. p. 97. Available online: https://helloworld.raspberrypi.org/issues/8/pdf. (Accessed 20/04/2020)

Bilbao, J., Bravo, E., García, O., Varela, C. & Rebollar, C. (2017), "Assessment of Computational Thinking Notions in Secondary School", Baltic Journal of Modern Computing, vol. 5, no. 4, pp. 391-397.

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, Canada (pp. 1-25).

Brown, N. C., Sentance, S., Crick, T., & Humphreys, S. (2014). Restart: The resurgence of computer science in UK schools. ACM Transactions on Computing Education (TOCE), 14(2), 9.

Burden, R. (2015). ‘Evidence for the efficacy of thinking skills approaches in affecting learning outcomes: the need for a broader perspective.’ In Wegerif, L. and Kaufman J. (Eds.) The Routledge International Handbook of Research on Teaching Thinking. Abingdon: Routledge, pp. 291-304.

Burke, Q. (2012). The markings of a new pencil: Introducing programming-as-writing in the middle school classroom. Journal of Media Literacy Education, 4(2), 121-135.

Cansu, S. K., & Cansu, F. K. (2019). An Overview of Computational Thinking. International Journal of Computer Science Education in Schools, 3(1).

Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS quarterly, 189-211.

Computing at School (2020). “Resources”. Available online at https://community.computingatschool.org.uk/resources/. Last accessed 24/04/2020.

Crick, T. (2017). Computing Education: An Overview of Research in the Field. London: Royal Society

CSTA and ISTE. (2011). Computational Thinking in K–12 Education leadership toolkit. http://csta.acm.org/Curriculum/sub/CurrFiles/471.11CTLeadershiptToolkit-SP-vF.pdf

Denning, P. J. (2017). Remaining trouble spots with computational thinking. Communications of the ACM, 60(6), 33-39.

Department for Education. (2013). Computing programmes of study: key stages 3 and 4 National curriculum in England. Retrieved from https://www.gov.uk/government/publications/national-curriculum-in-england-computing-programmes-of-study

Department for Education (2015), Computer science: GCSE Subject Content, retrieved from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/397550/GCSE_subject_content_for_computer_science.pdf

diSessa, A. (2018). Computational Literacy and “The Big Picture” Concerning Computers in Mathematics Education, Mathematical Thinking and Learning, 20:1, 3-31, DOI: 10.1080/10986065.2018.1403544

Doyle, E., Stamouli, I., & Huggard, M. (2005). Computer anxiety, self-efficacy, computer experience: An investigation throughout a computer science degree. In Frontiers in Education, 2005. FIE'05. Proceedings 35th Annual Conference (pp. S2H-3). IEEE.

Fessakis, G., Gouli, E., & Mavroudi, E. (2013). Problem solving by 5–6 years old kindergarten children in a computer programming environment: A case study. Computers & Education, 63, 87-97.

Fields, D. A., Searle, K. A., Kafai, Y. B., & Min, H. S. (2012). Debuggems to assess student learning in e-textiles. In Proceedings of the 43rd ACM technical symposium on Computer Science Education (pp. 699-699). ACM.

Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38-43.

Hidson, E, (2018). Challenges to Pedagogical Content Knowledge in lesson planning during curriculum transition: a multiple case study of teachers of ICT and Computing in England, Durham theses, Durham University. Available at Durham E-Theses Online: http://etheses.dur.ac.uk/12623/

Kallia, M. (2017). Assessment in Computer Science courses?: A Literature Review. London: Royal Society.

Kemp, P.E.J., Berry, M.G. & Wong, B. (2018). The Roehampton Annual Computing Education Report: Data from 2017. London: University of Roehampton.

Korkmaz, Ö., Cakir, R. and Özden, M.Y., (2017). A validity and reliability study of the computational thinking scales (CTS). Computers in Human Behavior, 72, pp.558-569.

Lee, I., Martin, F., Denner, J., Coulter, B., Allan, W., Erickson, J., Malyn-Smith, J. & Werner, L. (2011). Computational thinking for youth in practice. ACM Inroads, 2(1), 32-37. doi:10.1145/1929887.1929902

Livingston, K., Hayward, L., Higgins, S., Wyse, D., (2015). Multiple influences on curriculum decisions in a supercomplex world. Curriculum Journal, 26(4), 515-517.

Lockwood, J., & Mooney, A. (2018). Computational Thinking in Secondary Education: Where Does It Fit? A Systematic Literary Review. International Journal of Computer Science Education in Schools, 2(1).

Lye, S. & Koh, J. (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

Moseley, D. (2005). Frameworks for thinking: A handbook for teaching and learning. Cambridge University Press.

Noone, M., & Mooney, A. (2018). Visual and textual programming languages: a systematic review of the literature. Journal of Computers in Education, 5(2), 149-174.

Ofqual. (2018a). GCSE Subject Level Conditions and Requirements for Computer Science. Department for Education. Retrieved from: https://www.gov.uk/government/publications/gcse-9-to-1-subject-level-conditions-and-requirements-for-computer-science

Ofqual.(2018b). Entries for GCSE, AS and A level Summer 2018 exam series. Retrieved from https://www.gov.uk/government/statistics/entries-for-gcse-as-and-a-level-summer-2018-exam-series

Papert, S. (1980). Mindstorms. Children, computers and powerful ideas. New York: Basic Books

Ramsden, P. (2003). Learning to Teach in Higher Education (2nd ed., pp. 43-). Oxon: RoutledgeFalmer.

Riley, D. & Hunt, K. (2014). Computational thinking for the modern problem solver (1st ed.). Boca Raton, Fla: CRC Press.

Román-González, M., Moreno-León, J., & Robles, G. (2017). Complementary tools for computational thinking assessment. In Proceedings of International Conference on Computational Thinking Education (CTE 2017), S. C Kong, J Sheldon, and K. Y Li (Eds.). The Education University of Hong Kong (pp. 154-159).

Sam, H. K., Othman, A. E. A., & Nordin, Z. S. (2005). Computer self-efficacy, computer anxiety, and attitudes toward the Internet: A study among undergraduates in Unimas. Educational Technology & Society, 8(4), 205-219.

Selby, C., Dorling, M., & Woollard, J. (2014). Evidence of assessing computational thinking. Author's original, 1-11.

Selby, C. & Woollard, J. 2013. Computational Thinking: The Developing Definition. Available: http://eprints.soton.ac.uk/356481/

Sentance, S., and Selby, C. (2015) A classification of research into computer science education in school from 2005-2014: Initial report, Retrieved from https://community.computingatschool.org.uk/resources/4119/single.

Tedre, M. and Denning, P. J. (2016) The Long Quest for Computational Thinking. In Proceedings of the 16th Koli Calling Conference on Computing Education Research, pages 120-129.

The Royal Society (2012). Shut down or restart? The way forward for computing in UK schools. The Royal Society, London.

Royal Society. (2017). After the reboot?: computing education in UK schools. London: Royal Society. Retrieved from https://royalsociety.org/topics-policy/projects/computing-education/

Waite, J. (2017). Pedagogy in teaching Computer Science in schools: A Literature Review. London: Royal Society.

Webb, D. C. (2010). Troubleshooting assessment: an authentic problem solving activity for it education. Procedia-Social and Behavioral Sciences, 9, 903-907.

Webb, M., Davis, N., Bell, T., Katz, Y. J., Reynolds, N., Chambers, D. P., & Sys?o, M. M. (2017). Computer science in K-12 school curricula of the 2lst century: Why, what and when? Education and Information Technologies, 22(2), 445-468.

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.

Werner, L., Denner, J., & Campe, S. (2015). Children Programming Games: A Strategy for Measuring Computational Learning. ACM Transactions On Computer Education, 14(4), 24:1-24:22. doi:10.1145/2677091

Wing, J. (2006). Computational thinking. Communications Of The ACM, 49(3), 33-35. http://dx.doi.org/10.1145/1118178.1118215

Wing, J., (2014). ‘Computational thinking benefits society’. Social Issues In Computing 40th Anniversary Blog. January 10th, 2014. Available online at: http://socialissues.cs.toronto.edu/2014/01/computational-thinking/

Zhong, B., Wang, Q., Chen, J., & Li, Y. (2015). An Exploration of Three-Dimensional Integrated Assessment for Computational Thinking. Journal Of Educational Computing Research, 53(4), 562-590. http://dx.doi.org/10.1177/0735633115608444



How to Cite

Gillott, L., Joyce-Gibbons, A., & Hidson, E. (2020). Exploring and comparing computational thinking skills in students who take GCSE Computer Science and those who do not. International Journal of Computer Science Education in Schools, 3(4), 3–22. https://doi.org/10.21585/ijcses.v3i4.77