Phil Steinhorst
ABSTRACT
Productive Failure is an instructional setting where learners are confronted with a problem-solving task prior to recieving an introduction to canonical solution methods. This approach has its roots in secondary school mathematics education and has been studied widely in this field. To the best of our knowledge, however, little if anything is known about its feasibility and efficacy in computer science at a tertiary level. In my PhD project, I work towards reducing this gap by comparing the outcome of Productive Failure settings with those of Direct Instruction approaches where students are introduced to required methods first. Is it possible to design educational settings in computer science that elicit the positive effects of Productive Failure on students’ learning? By designing interventions that allow for collecting and analyzing of both qualitative and quantitative data, I hope to find answers to this question and its corrolaries.
- Manu Kapur. 2008. Productive Failure. Cognition and Instruction 26, 3 (2008), 379–424. https://doi.org/10.1080/07370000802212669Google Scholar
Cross Ref
- Manu Kapur. 2014. Productive Failure in Learning Math. Cognitive Science 38, 5 (2014), 1008–1022. https://doi.org/10.1111/cogs.12107Google ScholarCross Ref
- Manu Kapur and Katerine Bielaczyc. 2012. Designing for Productive Failure. Journal of the Learning Sciences 21, 1 (2012), 45–83. https://doi.org/10.1080/10508406.2011.591717Google ScholarCross Ref
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- Katharina Loibl, Ido Roll, and Nikol Rummel. 2017. Towards a Theory of When and How Problem Solving Followed by Instruction Supports Learning. Educational Psychology Review 29, 4 (2017), 693–715. https://doi.org/10.1007/s10648-016-9379-xGoogle ScholarCross Ref
- Lauren Margulieux, Paul Denny, Kathryn Cunningham, Michael Deutsch, and Benjamin R. Shapiro. 2021. When Wrong is Right: The Instructional Power of Multiple Conceptions. In Proceedings of the 17th ACM Conference on International Computing Education Research(ICER 2021). Association for Computing Machinery, New York, NY, USA, 184–197. https://doi.org/10.1145/3446871.3469750Google ScholarDigital Library
- Chris Mayfield, Sukanya Kannan Moudgalya, Aman Yadav, Clif Kussmaul, and Helen H. Hu. 2022. POGIL in CS1: Evidence for Student Learning and Belonging. In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 1(SIGCSE 2022). Association for Computing Machinery, New York, NY, USA, 439–445. https://doi.org/10.1145/3478431.3499296Google ScholarDigital Library
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- Tanmay Sinha and Manu Kapur. 2019. When Productive Failure Fails. In Proceedings of the 41st Annual Conference of the Cognitive Science Society, Ashok Goel, Colleen Seifert, and Christian Freksa (Eds.). Vol. 41. Cognitive Science Society, Montreal, Quebec, Canada, 2811–2817.Google Scholar
- Tanmay Sinha, Manu Kapur, Robert West, Michele Catasta, Matthias Hauswirth, and Dragan Trninic. 2021. Differential benefits of explicit failure-driven and success-driven scaffolding in problem-solving prior to instruction. Journal of Educational Psychology 113, 3 (2021), 530–555. https://doi.org/10.1037/edu0000483Google ScholarCross Ref
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