ABSTRACT
This study explores the relationship between computational thinking, teaching programming, and Bloom's Taxonomy. Data is collected from teachers, academics, and professionals, purposively selected because of their knowledge of the topics of problem solving, computational thinking, or the teaching of programming. This data is analysed following a grounded theory approach. A computational thinking taxonomy is developed. The relationships between cognitive processes, the pedagogy of programming, and the perceived levels of difficulty of computational thinking skills are illustrated by a model.
Specifically, a definition for computational thinking is presented. The skills identified are mapped to Bloom's Taxonomy: Cognitive Domain. This mapping concentrates computational skills at the application, analysis, synthesis, and evaluation levels. Analysis of the data indicates that abstraction of functionality is less difficult than abstraction of data, but both are perceived as difficult. The most difficult computational thinking skill is reported as decomposition. This ordering of difficulty for learners is a reversal of the cognitive complexity predicted by Bloom's model. The plausibility of this inconsistency is explored.
The taxonomy, model, and the other results of this study may be used by educators to focus learning onto the computational thinking skills acquired by the learners, while using programming as a tool. They may also be employed in the design of curriculum subjects, such as ICT, computing, or computer science.
- Bell, T., Andreae, P., and Lambert, L., 2010. Computer Science in New Zealand High Schools. In Proceedings of the Twelfth Australasian Conference on Computing Education Australian Computer Society, Inc., Brisbane, Australia, 15--22. Google ScholarDigital Library
- Biggs, J., n.d. SOLO Taxonomy.Google Scholar
- Biggs, J. and Collis, K., 1982. Evaluating the Quality of Learning, The SOLO Taxonomy Academic Press, Sydney.Google Scholar
- Bloom, B., 1956. Taxonomy of Educational Objectives: The Classification of Educational Goals, Handbook 1 Cognitive Domain McKay, New York.Google Scholar
- Butler, M. and Morgan, M., 2007. Learning challenges faced by novice programming students studying high level and low feedback concepts. In Proceedings of the ICT: Providing choices for learners and learning. Proceedings ascilite (Singapore2007), www.ascilite.org.au, 99--107.Google Scholar
- CBI, 2014. Gateway to Growth: CBI/Pearson education and skills survey 2014(2014).Google Scholar
- Chan, C. C., Tsui, M. S., Chan, M. Y. C., and Hong, J. H., 2010. Applying the Structure of the Observed Learning Outcomes (SOLO) Taxonomy on Student's Learning Outcomes: An empirical study. Assessment & Evaluation in Higher Education 27, 6 (2002/12/01), 511--527. DOI= http://dx.doi.org/10.1080/0260293022000020282.Google Scholar
- Chick, H., 1998. Cognition in the Formal Modes: Research Mathematics and the SOLO Taxonomy. Mathematics Education Research Journal 10, 24, 4--26.Google ScholarCross Ref
- Churches, A., 2009a. Bloom's Digital Taxonomy (v3.01), 75.Google Scholar
- Cohen, L., Manion, L., and Morrison, K., 2007. Research Methods in Education. Routledge, Abingdon, England.Google Scholar
- Computing at School Working Group, 2012. Computer Science: A curriculum for schools Computing At School.Google Scholar
- Dijkstra, E., 1988. On the cruelty of really teaching computing science The University of Texas at Austin.Google Scholar
- Du Boulay, B., 1989. Some difficulties of learning to program. In Studying the novice programmer, E. SOLOWAY and J. G. SPOHRER Eds. Lawrence Erlbaum, Hillsdale, NJ, 293--299.Google Scholar
- Fitzgerald, S., Simon, B., and Thomas, L., 2005. Strategies that students use to trace code: an analysis based in grounded theory. In Proceedings of the Proceedings of the first international workshop on Computing education research (Seattle, WA, USA2005), ACM, 1089793, 69--80. DOI= http://dx.doi.org/10.1145/1089786.1089793. Google ScholarDigital Library
- Fuller, U., Johnson, C. G., Ahoniemi, T., Cukierman, D., Hernán-Losada, Jackova, J., Lahtinen, E., Lewis, T. L., Thompson, D. M., Riedesel, C., and Thompson, E., 2007. Developing a computer science-specific learning taxonomy. SIGCSE Bull. 39, 4, 152--170. DOI= http://dx.doi.org/10.1145/1345375.1345438. Google ScholarDigital Library
- Gal-Ezer, J., Beeri, C., Harel, D., and Yehudai, A., 1995. A High School Program in Computer Science. Computer 28, 10, 73--80. DOI= http://dx.doi.org/10.1109/2.467599. Google ScholarDigital Library
- Glaser, B., 2009. Jargonizing: The use of the grounded theory vocabulary. In The Grounded Theory Review, J. HOLTON Ed. Sociology Press, Mill Valley, CA, USA.Google Scholar
- Glaser, B. G., 2002. Constructivist Grounded Theory? Forum: Qualitative Social Research 3 (3)(September 2002).Google Scholar
- Google, 2011. Exploring Computational Thinking.Google Scholar
- Guzdial, M., 2008. Education: Paving the way for computational thinking. Commun. ACM 51, 8, 25--27. DOI= http://dx.doi.org/10.1145/1378704.1378713. Google ScholarDigital Library
- Guzdial, M., 2011. A Definition of Computational Thinking from Jeannette Wing. In Computing Education Blog, Atlanta.Google Scholar
- Guzdial, M., 2012. A nice definition of computational thinking, including risks and cyber-security. In Computing Education Blog, Atlanta. Google ScholarDigital Library
- Jenkins, T., 2002. On the Difficulty of Learning to Program. In Proceedings of the 3rd Annual LTSN-ICS Conference (Loughborough University2002), The Higher Education Academy.Google Scholar
- Johnson, C. G. and Fuller, U., 2006. Is Bloom's taxonomy appropriate for computer science? In Proceedings of the Proceedings of the 6th Baltic Sea conference on Computing education research: Koli Calling 2006 (Uppsala, Sweden2006), ACM, 1315825, 120--123. DOI= http://dx.doi.org/10.1145/1315803.1315825. Google ScholarDigital Library
- L'heureux, J., Boisvert, D., Cohen, R., and Sanghera, K., 2012. IT problem solving: an implementation of computational thinking in information technology. In Proceedings of the 13th Annual Conference on Information Technology Education ACM, Calgary, Alberta, Canada, 183--188. DOI= http://dx.doi.org/10.1145/2380552.2380606. Google ScholarDigital Library
- Lahtinen, E., Ala-Mutka, K., and Järvinen, H.-M., 2005. A study of the difficulties of novice programmers. In Proceedings of the Proceedings of the 10th annual SIGCSE conference on Innovation and technology in computer science education (Caparica, Portugal2005), ACM, 1067453, 14--18. DOI= http://dx.doi.org/10.1145/1067445.1067453. Google ScholarDigital Library
- Lister, R., 2000. On blooming first year programming, and its blooming assessment. In Proceedings of the Proceedings of the Australasian conference on Computing education (Melbourne, Australia2000), ACM, 359393, 158--162. DOI= http://dx.doi.org/10.1145/359369.359393. Google ScholarDigital Library
- Lister, R., Fidge, C., and Teague, D., 2009. Further evidence of a relationship between explaining, tracing and writing skills in introductory programming. In Proceedings of the Proceedings of the 14th annual ACM SIGCSE conference on Innovation and technology in computer science education (Paris, France2009), ACM, 1562930, 161--165. DOI= http://dx.doi.org/10.1145/1562877.1562930. Google ScholarDigital Library
- Lister, R., Simon, B., Thompson, E., Whalley, J. L., and Prasad, C., 2006. Not seeing the forest for the trees: novice programmers and the SOLO taxonomy. In Proceedings of the Proceedings of the 11th annual SIGCSE conference on Innovation and technology in computer science education (Bologna, Italy2006), ACM, 1140157, 118--122. DOI= http://dx.doi.org/10.1145/1140124.1140157. Google ScholarDigital Library
- Lopez, M., Whalley, J., Robbins, P., and Lister, R., 2008. Relationships between reading, tracing and writing skills in introductory programming. In Proceedings of the Proceeding of the Fourth international Workshop on Computing Education Research (Sydney, Australia2008), ACM, 1404531, 101--112. DOI= http://dx.doi.org/10.1145/1404520.1404531. Google ScholarDigital Library
- Ma, L., Ferguson, J., Roper, M., and Wood, M., 2011. Investigating and improving the models of programming concepts held by novice programmers. Computer Science Education 21, 1, 57--80.Google ScholarCross Ref
- Meerbaum-Salant, O., Armoni, M., and Ben-Ari, M., 2010. Learning computer science concepts with scratch. In Proceedings of the Proceedings of the Sixth international workshop on Computing education research (Aarhus, Denmark2010), ACM, 1839607, 69--76. DOI= http://dx.doi.org/10.1145/1839594.1839607. Google ScholarDigital Library
- Milne, I. and Rowe, G., 2002. Difficulties in Learning and Teaching Programming - Views of Students and Tutors. Education and Information Technologies 7, 1, 55--66. DOI= http://dx.doi.org/10.1023/a:1015362608943. Google ScholarDigital Library
- Muller, O., 2005. Pattern oriented instruction and the enhancement of analogical reasoning. In Proceedings of the Proceedings of the first international workshop on Computing education research (Seattle, WA, USA2005), ACM, 1089792, 57--67. DOI= http://dx.doi.org/10.1145/1089786.1089792. Google ScholarDigital Library
- National Research Council, 2010. Report of a Workshop on the Scope and Nature of Computational Thinking The National Academies Press.Google Scholar
- National Research Council, 2011. Report of a Workshop of Pedagogical Aspects of Computational Thinking The National Academies Press.Google Scholar
- Pólya, G., 1985. How To Solve It, 2nd ed. Penguin, London.Google Scholar
- Prosser, J., 2004. Ensuring Quality in Qualitative Data. In Research Methods (part 1 and 2), J. SWANN Ed. University of Southampton, School of Education, Southampton, England, 6a.1--6a.27.Google Scholar
- Sakhnini, V. and Hazzan, O., 2008. Reducing Abstraction in High School Computer Science Education: The Case of Definition, Implementation, and Use of Abstract Data Types. J. Educ. Resour. Comput. 8, 2, 1--13. DOI= http://dx.doi.org/http://doi.acm.org/10.1145/1362787.1362789. Google ScholarDigital Library
- Strauss, A. and Corbin, J., 1998. Basics of qualitative research: Techniques and procedures for developing grounded theory. Sage Publications Ltd., London.Google Scholar
- The Royal Academy of Engineering, 2009. ICT for the UK's Future: the implications of the changing nature of Information and Communications Technology The Royal Academy of Engineering, London.Google Scholar
- The Royal Society, 2012. Shut down or restart? The way forward for computing in UK schools, London.Google Scholar
- Thompson, E., Luxton-Reilly, A., Whalley, J. L., Hu, M., and Robbins, P., 2008. Bloom's taxonomy for CS assessment. In Proceedings of the Proceedings of the tenth conference on Australasian computing education - Volume 78 (Wollongong, NSW, Australia2008), Australian Computer Society, Inc., 1379265, 155--161. Google ScholarDigital Library
- Ubiquity, 2007. An Interview with Peter Denning on the great principles of computing. Ubiquity 2007, June, 1. DOI= http://dx.doi.org/10.1145/1276162.1276163. Google ScholarDigital Library
- Usher, R., Bryant, I., and Johnston, R., 1997. Adult education and the postmodern challenge: learning beyond the limits. Routledge, London.Google Scholar
- Wing, J., 2006. Computational thinking. Commun. ACM 49, 3, 33--35. DOI= http://dx.doi.org/10.1145/1118178.1118215. Google ScholarDigital Library
- Wing, J., 2008. Computational thinking and thinking about computing. Philosophical Transactions of The Royal Society A 366, 3717--3725. DOI= http://dx.doi.org/10.1098/rsta.2008.0118.Google ScholarCross Ref
- Wing, J., 2011. Research Notebook: Computational Thinking - What and Why? In The Link Carneige Mellon, Pittsburgh, PA, 6.Google Scholar
Index Terms
- Relationships: computational thinking, pedagogy of programming, and Bloom's Taxonomy
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