Abstraction in action: K-5 teachers' uses of levels of abstraction, particularly the design level, in teaching programming.
Research indicates that understanding levels of abstraction (LOA) and being able to move between the levels is essential to programming success. For K-5 contexts we rename the LOA levels: problem, design, code and running the code. Â In our qualitative exploratory study, we interviewed five K-5 teachers on their uses of LOA, particularly the design level, in teaching programming and other subjects. Using PCK elements to analyse responses we found our teachers used design as an instructional strategy and for assessment. Our teachers used design as an aide memoire and the expert teachers used design: as a contract for pair-programming; to work out what they needed to teach; for learners to annotate with code snippets (to transition across LOA); for learners to self-assess and to assess â€˜do-abilityâ€™. Teachers used planning in teaching writing to scaffold learning and promote self-regulation revealing their understanding of student understanding. One issue was of our teachers' knowledge of terms including algorithm and code; we propose a concept of â€˜emergent algorithmsâ€™. Our findings suggest design helps learners learn to program in the same way that planning helps learners learn to write and that LOA, particularly the design level, may provide an accessible exemplar of abstraction in action. Further work is needed to verify whether our results are generalisable more widely.
Aharoni, D., 2000. Cogito, Ergo sum! cognitive processes of students dealing with data structures. ACM SIGCSE Bulletin, 32(1), pp.26â€“30. Available at: https://doi.org/10.1145/331795.331804.
Armoni, M., 2013. On Teaching Abstraction in Computer Science to Novices. Journal of Computers in Mathematics and Science Teaching, 32(3), pp.265â€“284.
Armoni, M., 2012. Teaching CS in Kindergarten: How Early Can the Pipeline Begin? ACM Inroads, 3(4), pp.18â€“19.
Barendsen, E. et al., 2015. Concepts in K-9 Computer Science Education. In Proceedings of the 2015 ITiCSE on Working Group Reports. ITICSE-WGR â€™15. Vilnius, Lithuania: ACM, pp. 85â€“116. Available at: http://doi.acm.org/10.1145/2858796.2858800.
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), pp.48â€“54. Available at: http://doi.acm.org/10.1145/1929887.1929905.
Barsalou, L.W. et al., 2003. Social embodiment. Psychology of learning and motivation, 43, pp.43â€“92. Available at: https://doi.org/10.1016/S0079-7421(03)01011-9.
Berry, M. et al., 2015. Barefoot computing resources. Available at: http://barefootcas.org.uk/.
Bienkowski, M. et al., 2015. Assessment Design Patterns for Computational Thinking Practices in Secondary Computer Science: A First Look, Menlo Park, CA: http://pact.sri.com/resources.html: SRI International.
Biggs, J. & Collis, K., 1982. Origin and description of the SOLO taxonomy. Evaluating the quality of learning: The SOLO Taxonomy. New York: Academic Press Inc, pp.17â€“30. Available at: https://doi.org/10.1016/B978-0-12-097552-5.50007-7.
Bloom, B.S., 1956. Taxonomy of Educational Objectives, Handbook I: The Cognitive Domain, New York: David McKay Co Inc.
Du Boulay, B., 1986. Some difficulties of learning to program. Journal of Educational Computing Research, 2(1), pp.57â€“73. Available at: https://doi.org/10.2190/3LFX-9RRF-67T8-UVK9.
Bruner, J.S., 1963. Needed: A theory of instruction. Educational Leadership, 20(8), pp.523â€“532.
Carlisle, J.F., Fleming, J.E. & Gudbrandsen, B., 2000. Incidental word learning in science classes. Contemporary Educational Psychology, 25(2), pp.184â€“211. Available at: https://doi.org/10.1006/ceps.1998.1001.
Cohen, L., Manion, L. & Morrison, K., 2011. Research methods in education, Routledge.
Cook, C.T. et al., 2012. A systematic approach to teaching abstraction and mathematical modeling. In Proceedings of the 17th ACM annual conference on Innovation and technology in computer science education. ACM, pp. 357â€“362. Available at: https://doi.org/10.1145/2325296.2325378.
Csizmadia, A. et al., 2015. Computational Thinking a Guide for Teachers. Available at: http://community.computingatschool.org.uk/files/6695/original.pdf.
CSTA, 2011. Computational Thinking Teacher Resources 2nd Edition. Available at: https://www.iste.org/explore/articleDetail?articleid=152&category=Solutions&article=Computational-thinking-for-all.
Curzon, P. & McOwan, P.W., 2008. Engaging with Computer Science Through Magic Shows. In Proceedings of the 13th Annual Conference on Innovation and Technology in Computer Science Education. ITiCSE â€™08. Madrid, Spain: ACM, pp. 179â€“183. Available at: https://doi.org/10.1145/1384271.1384320.
Cutts et al., 2012. The abstraction transition taxonomy: developing desired learning outcomes through the lens of situated cognition. In Proceedings of the ninth annual international conference on International computing education research. ACM, pp. 63â€“70. Available at: https://doi.org/10.1145/2361276.2361290.
DfE, 2013a. Computing programmes of study key stages 1 and 2 National Curriculum in England, Department of Education. Available at: https://www.gov.uk/government/publications/national-curriculum-in-england-computing-programmes-of-study.
DfE, 2013b. Computing programmes of study: key stages 3 and 4 National curriculum in England, Department for Education. Available at: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/239067/SECONDARY_national_curriculum_-_Computing.pdf.
Dockrell, J., Marshall, C. & Wyse, D., 2015. Education Endowment Fund Talk for Writing Evaluation report and Executive Summary. Available at: https://www.google.co.uk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiZ1vDQs43SAhUHkRQKHUlgBLwQFggcMAA&url=https%3A%2F%2Fv1.educationendowmentfoundation.org.uk%2Fuploads%2Fpdf%2FTalk_for_Writing.pdf&usg=AFQjCNFlLAFouweXrL_CMwMe1slCiRCPvA.
Drever, 1995. Using semi-structured interview in small-scale research A teacherâ€™s guide., The Scottish Council for Research in Education.
Dweck, C., 2015. Carol Dweck Revisits theâ€™Growth Mindset. Education Week, 35(5), pp.20â€“4.
Fuller, U. et al., 2007. Developing a computer science-specific learning taxonomy. In ACM SIGCSE Bulletin. ACM, pp. 152â€“170. Available at: https://doi.org/10.1145/1345443.1345438.
Gibson, J.P., 2008. Formal Methods: Never Too Young to Start. Formal Methods in Computer Science Education (FORMED 2008), pp.151â€“160.
Gibson, J.P., 2012. Teaching graph algorithms to children of all ages. In Proceedings of the 17th ACM annual conference on Innovation and technology in computer science education. ACM, pp. 34â€“39. Available at: https://doi.org/10.1145/2325296.2325308.
Glaser, C. & Brunstein, J.C., 2007. Improving fourth-grade studentsâ€™ composition skills: Effects of strategy instruction and self-regulation procedures. Journal of educational psychology, 99(2), p.297. Available at: https://doi.org/10.1037/0022-0618.104.22.1687.
Graham, S. et al., 2012. Teaching elementary school students to be effective writers. What Works Clearinghouse, US Department of Education.
Graham, S. & Perin, D., 2007. A meta-analysis of writing instruction for adolescent students. Journal of educational psychology, 99(3), p.445. Available at: https://doi.org/10.1037/0022-0622.214.171.1245.
Grover, S. & Pea, R., 2013. Using a discourse-intensive pedagogy and androidâ€™s app inventor for introducing computational concepts to middle school students. In Proceeding of the 44th ACM technical symposium on Computer science education. ACM, pp. 723â€“728. Available at: https://doi.org/10.1145/2445196.2445404.
Hattie, J. & Yates, G., 2014. Visible learning and the science of how we learn, Rout.
Hazzan, B. & Hadar, I., 2005. Reducing abstraction when learning graph theory. Journal of Computers in Mathematics and Science Teaching, 24(3), pp.255â€“272.
Hazzan, O., 2002. Reducing abstraction level when learning computability theory concepts. In ACM SIGCSE Bulletin. ACM, pp. 156â€“160. Available at: https://doi.org/10.1145/544414.544461.
Hazzan, O. & Kramer, J., 2007. Abstraction in computer science & software engineering: A pedagogical perspective. Frontier Journal, 4(1), pp.6â€“14.
Higgins, S. et al., 2013. The Sutton Trust-Education Endowment Foundation Teaching and Learning Toolkit: Technical Appendices. Education Endowment Foundation, London, available at: http://educationendowmentfoundation. org. uk/uploads/pdf/Technical_Appendices_ (June_2013). pdf.
Hmelo-Silver, C.E. & Pfeffer, M.G., 2004. Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cognitive Science, 28(1), pp.127â€“138. Available at: https://doi.org/10.1207/s15516709cog2801_7.
Hsiao, I.-H. & Brusilovsky, P., 2011. The role of community feedback in the student example authoring process: An evaluation of annotex. British Journal of Educational Technology, 42(3), pp.482â€“499. Available at: https://doi.org/10.1111/j.1467-8535.2009.01030.x.
Kramer, J., 2007. Is abstraction the key to computing? Communications of the ACM, 50(4), pp.36â€“42.
Kuckartz, U., 2014. Qualitative text analysis: A guide to methods, practice and using software, Sage. Available at: https://doi.org/10.4135/9781446288719.
De La Paz, S. & Graham, S., 1997. Effects of dictation and advanced planning instruction on the composing of students with writing and learning problems. Journal of Educational Psychology, 89(2), p.203.
Lee, I. et al., 2011. Computational thinking for youth in practice. ACM Inroads, 2(1), pp.32â€“37. Available at: https://doi.org/10.1145/1929887.1929902.
Lewis, M., 2000. Extending literacy: pupilsâ€™ interactions with texts, with particular emphasis on the use of non-fiction texts. Available at: http://hdl.handle.net/10026.1/518.
Lister, R. et al., 2004. A multi-national study of reading and tracing skills in novice programmers. In ACM SIGCSE Bulletin. ACM, pp. 119â€“150. Available at: https://doi.org/10.1145/1044550.1041673.
Lister, R., 2011. Concrete and other neo-Piagetian forms of reasoning in the novice programmer. In Proceedings of the Thirteenth Australasian Computing Education Conference-Volume 114. Australian Computer Society, Inc., pp. 9â€“18.
Magnusson, S., Krajcik, J. & Borko, H., 1999. Nature, sources, and development of pedagogical content knowledge for science teaching. In Examining pedagogical content knowledge. Springer, pp. 95â€“132.
Mannila, L. et al., 2014. Computational thinking in k-9 education. In Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference. ACM, pp. 1â€“29. Available at: https://doi.org/10.1145/2713609.2713610.
Margulieux, L.E. & Catrambone, R., 2016. Improving problem solving with subgoal labels in expository text and worked examples. Learning and Instruction, 42, pp.58â€“71. Available at: https://doi.org/10.1016/j.learninstruc.2015.12.002.
Mason, L.H., Harris, K.R. & Graham, S., 2011. Self-regulated strategy development for students with writing difficulties. Theory into practice, 50(1), pp.20â€“27.
Mayring, P., 2000. Forum: Qualitative Social Research Sozialforschung, 2. History of Content Analysis. In Forum: Qualitative Social Research. Sozialforschung.
Van Merrienboer, J.J. & Sweller, J., 2005. Cognitive load theory and complex learning: Recent developments and future directions. Educational psychology review, 17(2), pp.147â€“177. Available at: https://doi.org/10.1007/s10648-005-3951-0.
National Research Council, 2011. Committee for the workshops on computational thinking: Report of a workshop of pedagogical aspects of computational thinking., National Research Council.
Nind, M., Curtin, A. & Hall, K., 2016. Research methods for pedagogy, Bloomsbury Publishing.
Papert, S., 1980. Mindstorms: Children, computers, and powerful ideas, Basic Books, Inc.
Perrenet, J., Groote, J.F. & Kaasenbrood, E., 2005. Exploring studentsâ€™ understanding of the concept of algorithm: levels of abstraction. ACM SIGCSE Bulletin, 37(3), pp.64â€“68. Available at: https://doi.org/10.1145/1151954.1067467.
Perrenet, J. & Kaasenbrood, E., 2006. Levels of abstraction in studentsâ€™ understanding of the concept of algorithm: the qualitative perspective. ACM SIGCSE Bulletin, 38(3), pp.270â€“274. Available at: https://doi.org/10.1145/1140123.1140196.
Piaget, J. & Campell, R.L., 2001. Studies in reflecting abstraction, Psychology Press.
Plonka, L. et al., 2011. Collaboration in pair programming: driving and switching. In International Conference on Agile Software Development. Springer, pp. 43â€“59. Available at: https://doi.org/10.1007/978-3-642-20677-1_4.
Punch, S., 2002. Research with Children: The same or different from research with adults? Childhood, 9(3), pp.321â€“341. Available at: https://doi.org/10.1177/0907568202009003005.
Santangelo, T. & Olinghouse, N.G., 2009. Effective writing instruction for students who have writing difficulties. Focus on exceptional children, 42(4), p.1.
Sapir, E., 1921. An introduction to the study of speech. Language.
Schulte, C. et al., 2017. The design and exploration cycle as research and development framework in computing education. In Global Engineering Education Conference (EDUCON), 2017 IEEE. IEEE, pp. 867â€“876. Available at: https://doi.org/10.1109/EDUCON.2017.7942950.
Schunk, D.H. & Swartz, C.W., 1993. Goals and progress feedback: Effects on self-efficacy and writing achievement. Contemporary Educational Psychology, 18(3), pp.337â€“354. Available at: https://doi.org/10.1006/ceps.1993.1024.
Seehorn, D. et al., 2016. Interim CSTA K-12 Computer Science Standards. Available at: https://c.ymcdn.com/sites/www.csteachers.org/resource/resmgr/Docs/Standards/2016StandardsRevision/INTERIM_StandardsFINAL_07222.pdf.
Seeratan, K. & Mislevy, R., 2009. Design patterns for assessing internal knowledge representations. Retrieved October, 26, p.2011.
Selby, C., Dorling, M. & Woollard, J., 2014. Evidence of assessing computational thinking. , pp.1â€“11. Available at: http://eprints.soton.ac.uk/372409/1/372409EvidAssessCT.pdf.
Sfard, A., 1991. On the dual nature of mathematical conceptions: Reflections on processes and objects as different sides of the same coin. Educational studies in mathematics, 22(1), pp.1â€“36. Available at: https://doi.org/10.1007/BF00302715.
Sheard, J. et al., 2008. Going SOLO to assess novice programmers. In ACM SIGCSE Bulletin. ACM, pp. 209â€“213. Available at: https://doi.org/10.1145/1384271.1384328.
Statter, D. & Armoni, M., 2017. Learning Abstraction in Computer Science: A Gender Perspective. In Proceedings of the 12th Workshop on Primary and Secondary Computing Education. WiPSCE â€™17. Nijmegen, Netherlands: ACM, pp. 5â€“14. Available at: http://doi.acm.org/10.1145/3137065.3137081.
Statter, D. & Armoni, M., 2016. Teaching Abstract Thinking in Introduction to Computer Science for 7th Graders. In Proceedings of the 11th Workshop in Primary and Secondary Computing Education. ACM, pp. 80â€“83. Available at: https://doi.org/10.1145/2978249.2978261.
Strickland, D.S. & Morrow, L.M., 1989. Emerging literacy: Young children learn to read and write., ERIC.
Su, A. et al., 2014. Investigating the role of computer-supported annotation in problem-solving-based teaching: An empirical study of a Scratch programming pedagogy. British Journal of Educational Technology, 45(4), pp.647â€“665. Available at: https://doi.org/10.1111/bjet.12058.
Syslo, M.M. & Kwiatkowska, A.B., 2014. Playing with computing at a childrenâ€™s university. In Proceedings of the 9th Workshop in Primary and Secondary Computing Education. ACM, pp. 104â€“107.
Taub, R., Armoni, M. & Ben-Ari, M.M., 2014. Abstraction as a bridging concept between computer science and physics. In Proceedings of the 9th Workshop in Primary and Secondary Computing Education. ACM, pp. 16â€“19. Available at: https://doi.org/10.1145/2670757.2670777.
Tedre, M. & Denning, P.J., 2016. The long quest for computational thinking. In Proceedings of the 16th Koli Calling Conference on Computing Education Research. pp. 24â€“27. Available at: https://doi.org/10.1145/2999541.2999542.
Turkle, S. & Papert, S., 1992. Epistemological pluralism and the revaluation of the concrete. Journal of Mathematical Behavior, 11(1), pp.3â€“33.
Waite, J. et al., 2016. Abstraction and common classroom activities. In Proceedings of the 11th Workshop in Primary and Secondary Computing Education. ACM, pp. 112â€“113. Available at: https://doi.org/10.1145/2978249.2978272.
Werner, L. et al., 2013. Pair programming for middle school students: does friendship influence academic outcomes? In Proceeding of the 44th ACM technical symposium on Computer science education. ACM, pp. 421â€“426. Available at: https://doi.org/10.1145/2445196.2445322.
Whitebread, D. & Basilio, M., 2012. The emergence and early development of self-regulation in young children. Profesorado, Revista de Currðš¤culum y FormaciÃ³n del Profesorado, 16(1), pp.15â€“34.
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), pp.3717â€“3725. Available at: https://doi.org/10.1098/rsta.2008.0118.
Wing, J.M. & Barr, V., 2011. Jeannette M. Wing @ PCAST; Barbara Liskov Keynote. Commun. ACM, 54(9), pp.10â€“11. Available at: http://doi.acm.org/10.1145/1995376.1995380.
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