Volume 14 · Number 3 · Pages 234–243
Constructionism and De-Constructionism: Opposite yet Complementary Pedagogies

Jean M. Griffin

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Context: Constructionism, Papert’s pedagogy and learning theory, involves experiential learning where students engage in exploration, create things that are personally meaningful, and share them with others. This approach is quite motivating, evidenced by the popularity of maker spaces, hackathons, and educational technologies that promote creative computing. With constructionism, the learner’s choice is important. This means that learning is often serendipitous. It also means that people often abandon their designs when obstacles arise. This is problematic in learning environments where coverage of key concepts is necessary, practice to develop skills is essential, and persistence with troubleshooting errors is required. Problem: How can teachers and instructional designers complement a constructionist approach with one that addresses its limitations? I introduce de-constructionism, a pedagogy and learning theory that emphasizes learning from taking things apart. It is inspired by reverse engineering, cognitive load theory, practice theory, and theories of learning from errors and negative knowledge. This approach is applicable to computer science, as described here, and other disciplines. Method: I report on a design-based research experiment, where university students interacted with Python practice problems during weekly labs. The designs of the individual problems, and series of problem sets, were based on a model for


Griffin J. M. (2019) Constructionism and de-constructionism: Opposite yet complementary pedagogies. Constructivist Foundations 14(3): 234–243. https://constructivist.info/14/3/234

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Abelson H. & DiSessa A. (1992) Turtle geometry. MIT Press, Cambridge MA. ▸︎ Google︎ Scholar
Agogino A. M., Sheppard S. & Oladipupo A. (1992) Making connections to engineering during the first two years. In: Proceedings of the twenty-second annual conference Frontiers in education (FIE ’92) IEEE Press, Piscataway NJ: 563–569. ▸︎ Google︎ Scholar
Astrachan O. & Reed D. (1995) AAA and CS 1: The applied apprenticeship approach to CS 1. In: Papers of the 26th SIGCSE technical symposium on computer science education (SIGCSE ’95) ACM, New York: 1–5. ▸︎ Google︎ Scholar
Ben-Ari M. (1998) Constructivism in computer science education. ACM SIGCSE Bulletin 30(1): 257–261. ▸︎ Google︎ Scholar
Bloom B. S. (1968) Learning for mastery. UCLA Evaluation Comment 1(2): 1–11. https://files.eric.ed.gov/fulltext/ED053419.pdf
Booth J. L., McGinn K. M., Young L. K. & Barbieri C. (2015) Simple practice doesn’t always make perfect: Evidence from the worked example effect. Policy Insights from the Behavioral and Brain Sciences 2(1): 24–32. ▸︎ Google︎ Scholar
Boytchev P. (2015) Constructionism and deconstructionism. Constructivist Foundations 10(3): 355–369 https://constructivist.info/10/3/355
Bransford J. D., Brown A. L., Cocking R. R., Donovan M. S. & Pellegrino J. W. (eds.) (2000) How people learn: Brain, mind, experience, and school. Expanded edition. National Academy Press, Washington DC. ▸︎ Google︎ Scholar
Brown A. L. (1992) Design experiments: Theoretical and methodological challenges in creating complex interventions in classroom settings. Journal of the Learning Sciences 2(2): 141–178. ▸︎ Google︎ Scholar
Brown P. C., Roediger III H. L. & McDaniel M. A. (2014) Make it stick: The science of successful learning. The Belknap Press of Harvard University Press, Cambridge MA. ▸︎ Google︎ Scholar
Cateté V., Snider E. & Barnes T. (2016) Developing a rubric for a creative CS principles lab. In: Proceedings of the 2016 ACM conference on innovation and technology in computer science education (ITiCSE ’16) ACM, New York: 290–295. ▸︎ Google︎ Scholar
Catrambone R. (1998) The subgoal learning model: creating better examples so that students can solve novel problems. Journal of Experimental Psychology: General 127(4): 355–376. ▸︎ Google︎ Scholar
Chi M. T. H., De Leeuw N., Chiu M.-H. & Lavancher C. (1994) Eliciting self-explanations improves understanding. Cognitive Science 18(3): 439–477. ▸︎ Google︎ Scholar
Chikofsky E. J. & Cross J. H. (1990) Reverse engineering and design recovery: A taxonomy. IEEE Software 7: 13–17. ▸︎ Google︎ Scholar
Cobb P., Confrey J., DiSessa A., Lehrer R. & Schauble L. (2003) Design experiments in educational research. Educational Researcher 32(1): 9–13. ▸︎ Google︎ Scholar
Collins A. (1990) Towards a design science of education. Technical Report No. 1. Center for Technology in Education, New York. ▸︎ Google︎ Scholar
Conati C. & VanLehn K. (2000) Toward computer-based support of meta-cognitive skills: A computational framework to coach self-explanation. International Journal of Artificial Intelligence in Education 11(1): 389–415. ▸︎ Google︎ Scholar
Cross J. H., Hendrix T. D. & Barowski L. A. (2011) Combining dynamic program viewing and testing in early computing courses. In: Proceedings of the 35th Annual IEEE computer software and applications conference. IEEE Press, Piscataway NJ: 184–192. ▸︎ Google︎ Scholar
Deimel L. E. & Moffat D. V. (1982) A more analytical approach to teaching the introductory programming course. In: J. Smith & M. Schuster (eds.) Proceedings of the NECC. The University of Missouri, Columbia: 114–118. ▸︎ Google︎ Scholar
Di Eugenio B., Green N., Alzoubi O., Alizadeh M., Harsley R. & Fossati D. (2015) Worked-out examples in a computer science intelligent tutoring system. In: Proceedings of the 16th annual conference on information technology education. ACM, New York: 121. ▸︎ Google︎ Scholar
Douce C., Livingstone D. & Orwell J. (2005) Automatic test-based assessment of programming: A review. ACM Journal of Educational Resources in Computing 5(3): 1–13. ▸︎ Google︎ Scholar
du Boulay B., O’Shea T. & Monk J. (1981) The black box inside the glass box: Presenting computing concepts to novices. International Journal of Man-Machine Studies 14: 237–249. ▸︎ Google︎ Scholar
Duncker K. (1945) On problem-solving. Psychological Monographs 58(5). ▸︎ Google︎ Scholar
Dunlosky J., Rawson K. A., Marsh E. J., Nathan M. J. & Willingham D. T. (2013) Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest 14(1): 4–58. ▸︎ Google︎ Scholar
Ericson B. J., Guzdial M. J. & Morrison B. B. (2015) Analysis of interactive features designed to enhance learning in an ebook. In: Proceedings of the 11th international conference on computing education research (ICER ’15) ACM, New York: 169–178. ▸︎ Google︎ Scholar
Ericsson K. A. & Charness N. (1994) Expert performance: Its structure and acquisition. American Psychologist 49(8): 725–747. ▸︎ Google︎ Scholar
Festinger L. (1957) A theory of cognitive dissonance. Stanford University Press, Stanford CA. ▸︎ Google︎ Scholar
Festinger L. (1962) A theory of cognitive dissonance. Volume 2. Stanford University Press, Stanford CA. ▸︎ Google︎ Scholar
Griffin J. M. (2016) Learning by taking apart: Deconstructing code by reading, tracing, and debugging. In: Proceedings of the 17th annual conference on information technology education (SIGITE’16) ACM, New York: 148–153. ▸︎ Google︎ Scholar
Griffin J. M. (2019) Designing intentional bugs for learning. In: Proceedings of the first UK and Ireland Computing Education Research conference. ACM, Canterbury UK, in press. ▸︎ Google︎ Scholar
Griffin J. M., Kaplan E. & Burke Q. (2012) Debug’ems and other Deconstruction Kits for STEM learning. In: Proceedings of the second IEEE integrated STEM education conference (ISEC ’12) IEEE Press, Piscataway NJ: 1–4. ▸︎ Google︎ Scholar
Griffin J. M., Pirmann T. & Gray B. (2016) Two teachers, two perspectives on CS principles. In: Proceedings of the 47th ACM technical symposium on computer science education (SIGCSE ’16) ACM, New York: 461–466. ▸︎ Google︎ Scholar
Guo P. J. (2013) Online Python tutor: Embeddable web-based program visualization for CS education. In: Proceeding of the 44th ACM technical symposium on computer science education. ACM, New York: 579–584. ▸︎ Google︎ Scholar
Guzdial M. (2003) A media computation course for non-majors. In: Finkel D. (ed.) Proceedings of the 8th annual conference on innovation and technology in computer science education (ITiCSE ’03) ACM, New York: 104–108. ▸︎ Google︎ Scholar
Hamari J., Koivisto J. & Sarsa H. (2014) Does gamification work? A literature review of empirical studies on gamification. In: Proceedings of the 47th Hawaii international conference on system sciences (HICSS ’14) IEEE Computer Society, Washington DC: 3025–3034. ▸︎ Google︎ Scholar
Harsley R. & Morgan S. (2015) Learning together: Expanding the one-to-one ITS model for computer science education. In: Proceedings of the eleventh annual international conference on international computing education research (ICER ’15) ACM, New York: 263–264. ▸︎ Google︎ Scholar
Isotani S., Adams D., Mayer R. E., Durkin K., Rittle-Johnson B. & McLaren B. M. (2011) Can erroneous examples help middle-school students learn decimals? Proceedings of the Sixth European Conference on Technology Enhanced Learning: Towards Ubiquitous Learning (EC-TEL-2011): 1–14. ▸︎ Google︎ Scholar
Kaess W. & Zeaman D. (1960) Positive and negative knowledge of results on a pressey-type punchboard. Journal of Educational Psychology 60(1): 12–17. ▸︎ Google︎ Scholar
Kafai Y. B. & Resnick M. (1996) Constructionism in practice: Designing, thinking, and learning in a digital world. Routledge, London. ▸︎ Google︎ Scholar
Kalyuga S. (2007) Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review 19(4): 509–539. ▸︎ Google︎ Scholar
Kick R. & Trees F. P. (2015) AP CS principles: Engaging, challenging, and rewarding. ACM Inroads 6(1): 42–45. ▸︎ Google︎ Scholar
Kimura T. (1979) Reading before composition. In: Proceedings of the 10th SIGCSE technical symposium on computer science education (SIGCSE ’79) ACM, New York: 162–166. ▸︎ Google︎ Scholar
Kinnunen P. & Malmi L. (2006) Why students drop out CS1 course? In: Proceedings of the second international workshop on computing education research (ICER ’06) ACM, New York: 97–108. ▸︎ Google︎ Scholar
Koedinger K. R., Booth J. L. & Klahr D. (2013) Instructional complexity and the science to constrain it. Science 342(6161): 935–937. ▸︎ Google︎ Scholar
Kranch D. A. (2011) Teaching the novice programmer: A study of instructional sequences and perception. Education and Information Technologies 17(3): 291–313. ▸︎ Google︎ Scholar
Linn M. C. & Clancy M. J. (1992) The case for case studies of programming problems. Communications of the ACM 35(3): 121–132. ▸︎ Google︎ Scholar
Lister R., Adams E. S., Fitzgerald S., Fone W., Hamer J., Lindholm M., McCartney R., Moström J. E., Sanders K., Seppälä O., Simon B. & Thomas L. (2004) A multi-national study of reading and tracing skills in novice programmers. ACM SIGCSE Bulletin 36(4): 119–150. ▸︎ Google︎ Scholar
Lopez M., Whalley J., Robbins P. & Lister R. (2008) Relationships between reading, tracing and writing skills in introductory programming. In: Proceedings of the 4th international workshop on computing education research (ICER ’08) ACM, New York: 101–112. ▸︎ Google︎ Scholar
Macnamara B. N., Hambrick D. Z. & Oswald F. L. (2014) Deliberate practice and performance in music, games, sports, education, and professions: A meta-analysis. Psychological Science 25(8): 1608–1618. ▸︎ Google︎ Scholar
Malan D. J. & Leitner H. H. (2007) Scratch for budding computer scientists. ACM SIGCSE Bulletin 39(1): 223–227. ▸︎ Google︎ Scholar
Margulieux L. E., Catrambone R. & Guzdial M. (2016) Employing subgoals in computer programming education. Computer Science Education 26(1): 44–67. ▸︎ Google︎ Scholar
Margulieux L. E., Morrison B. B., Guzdial M. & Catrambone R. (2016) Training learners to self-explain: Designing instructions and examples to improve problem solving. In: Looi C. K., Polman J. L., Cress U. & Reimann P. (eds.) Transforming learning, empowering learners: The international conference of the learning sciences (ICLS) 2016, Volume 1. International Society of the Learning Sciences, Singapore: 98–105. ▸︎ Google︎ Scholar
Miller B. & Ranum D. (2014) Runestone interactive: Tools for creating interactive course materials. In: Proceedings of the First ACM conference on learning @ scale (L@S’ 14) ACM, New York: 213–214. ▸︎ Google︎ Scholar
Morrison B. B., Margulieux L. E. & Guzdial M. (2015) Subgoals, context, and worked examples in learning computing problem solving. In: Proceedings of the 11th international conference on computing education research (ICER ’15) ACM, New York: 21–29. ▸︎ Google︎ Scholar
Murer M., Fuchsberger V. & Tscheligi M. (2017) Un-crafting: De-constructive engagements with interactive artifacts. In: Proceedings of the ninth international conference on tangible, embedded, and embodied interaction (TEI ’17) ACM, New York: 67–77. ▸︎ Google︎ Scholar
Ohlsson S. (1996) Learning from error and the design of task environments. International Journal of Educational Research 25(5): 419–448. ▸︎ Google︎ Scholar
Oser F., Näpflin C., Hofer C. & Aerni P. (2012) Towards a theory of negative knowledge (NK): Almost-mistakes as drivers of episodic memory amplification. In: Bauer J. & Harteis C. (eds.) Human fallibility: The ambiguity of errors for work and learning. Springer, New York: 53–70. ▸︎ Google︎ Scholar
Palincsar A. S. & Brown A. L. (1984) Reciprocal teaching of comprehension monitoring activities. Cognition and Instruction 1: 117–175. ▸︎ Google︎ Scholar
Papert S. & Harel I. (1991) Situating constructionism. In: Papert S. & Harel I. (eds.) Constructionism. Ablex Publishing, Norwood NJ: 1–11. ▸︎ Google︎ Scholar
Papert S. (1987) Constructionism: A new opportunity for elementary science education. National Science Foundation proposal. MIT, Cambridge MA. ▸︎ Google︎ Scholar
Parsons D. & Haden P. (2006) Parson’s programming puzzles: A fun and effective learning tool for first programming courses. In: Proceedings of the 8th Australasian conference on computing education. Volume 52. Australian Computer Society, Darlinghurst: 157–163. ▸︎ Google︎ Scholar
Patitsas E., Craig M. & Easterbrook S. (2013) Comparing and contrasting different algorithms leads to increased student learning. In: Proceedings of the 9th international conference on computing education research (ICER ’13) ACM, New York: 145–152. ▸︎ Google︎ Scholar
Perkins D. & Martin F. (1989) Fragile knowledge and neglected strategies in novice programmers. In: Soloway E. & Spohrer J. (eds.) Studying the novice programmer. Lawrence Erlbaum Associates, Hillsdale NJ: 213–229. ▸︎ Google︎ Scholar
Regan M. & Sheppard S. (1996) Interactive multimedia courseware and the hands-on learning experience: An assessment study. Journal of Engineering Education 85(2): 123–132. ▸︎ Google︎ Scholar
Rittle-Johnson B. & Star J. R. (2007) Does comparing solution methods facilitate conceptual and procedural knowledge? An experimental study on learning to solve equations. Journal of Educational Psychology 99(3): 561–574.Roediger H. L. & Butler A. C. (2011) The critical role of retrieval practice in long-term retention. Trends in Cognitive Sciences 15(1): 20–27. ▸︎ Google︎ Scholar
Roschelle J. & Linde C. (1996) Toy dissection: Formative in-depth assessment report. Institute for Research on Learning (IRL), Palo Alto CA. http://www-adl.stanford.edu/images/toyasess.pdf
Schulte C., Clear T., Taherkhani A., Busjahn T. & Paterson J. H. (2010) An introduction to program comprehension for computer science educators. In: Proceedings of the 2010 ITiCSE working group reports. ACM: New York: 65–86. ▸︎ Google︎ Scholar
Seabrook J. (2010) How to make it. The New Yorker 20 September 2010. http://www.newyorker.com/magazine/2010/09/20/how-to-make-it
Self J. (1997) From constructionism to deconstructionism: Anticipating trends in educational styles. European Journal of Engineering Education 22(3): 295–307. ▸︎ Google︎ Scholar
Sheppard S. D. (1992) Mechanical dissection: An experience in how things work. In: Proceedings of the conference on engineering education: Curriculum innovation & integration. 6–10 January 1992, Santa Barbara CA. http://www-adl.stanford.edu/images/dissphil.pdf
Siegler R. S. (2002) Microgenetic studies on self-explanation. In: Granott N. & Parziale J. (eds.) Microdevelopment: Transition processes in development and learning. Cambridge University Press, Cambridge UK: 31–58. ▸︎ Google︎ Scholar
Singley M. K. & Anderson J. R. (1989) The transfer of cognitive skill. Harvard University Press, Cambridge MA. ▸︎ Google︎ Scholar
Sorva J. (2013) Notional machines and introductory programming education. ACM Transactions on Computing Education 13(2): 1–31. ▸︎ Google︎ Scholar
Sudol-DeLyser L. A., Stehlik M. & Carver S. (2012) Code comprehension problems as learning events. In: Proceedings of the 17th ACM annual conference on innovation and technology in computer science education (ITiCSE ’12) ACM, New York: 81–86. ▸︎ Google︎ Scholar
Sweller J. & Cooper G. A. (1985) The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction 2(1): 59–89. ▸︎ Google︎ Scholar
Sweller J. (1988) Cognitive load during problem solving: Effects on learning. Cognitive Science 12(2): 257–285. ▸︎ Google︎ Scholar
Sweller J. (2006) The worked example effect and human cognition. Learning and Instruction 16(2): 165–169. ▸︎ Google︎ Scholar
Tsamir P. & Tirosh D. (2005) In-service mathematics teachers’ views of errors in the classroom. Focus on Learning Problems in Mathematics 27(3): 30. ▸︎ Google︎ Scholar
Tsovaltzi D., Melis E., McLaren B. M., Meyer A., Dietrich M. & Goguadze G. (2010) Learning from erroneous examples: When and how do students benefit from them. In: Wolpers M., Kirschner P. A., Scheffel M., Lindstaedt S. & Dimitrova V. (eds.) Proceedings of the 5th European conference on technology enhanced learning. LNCS 6383. Springer, Heidelberg: 357–373. ▸︎ Google︎ Scholar
Van Merriënboer J. & Paas F. G. W. C. (1990) Automation and schema acquisition in learning elementary computer programming: Implications for the design of practice. Computers in Human Behavior 6(3): 273–289. ▸︎ Google︎ Scholar
VanLehn K. (1988) Towards a theory of impasse-driven learning. In: Mandl H. & Lesgold A. (eds.) Learning issues for intelligent tutoring systems. Springer, New York: 19–41. ▸︎ Google︎ Scholar
Ventura Jr. P. R. (2005) Identifying predictors of success for an objects-first CS1. Computer Science Education 15(3): 223–243. ▸︎ Google︎ Scholar
Vishnoi A. (2012) NIC lesson on learning: Tod-Fod-Jod: The Indian Express, New Delhi India. http://archive.indianexpress.com/news/nic-lesson-on-learning-todfodjod/1015042/0
Wood W. H. & Agogino A. M. (1996) Engineering courseware content and delivery: The NEEDS infrastructure for distance-independent education. Journal of the American Society for Information Science 47(11): 863–869. ▸︎ Google︎ Scholar
Wu C. (2008) Some disassembly required. ASEE Prism 18: 56–59. ▸︎ Google︎ Scholar

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