Evaluating the efficiency and effectiveness of adaptive parsons problems

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Abstract

Practice is essential for learning. There is evidence that solving Parsons problems (putting mixed up code blocks in order) is a more efficient, but just as effective, form of practice than writing code from scratch. However, not all students successfully solve every Parsons problem. Making the problems adaptive, so that the difficulty changes based on the learner's performance, should keep the learner in Vygotsky's zone of proximal development and maximize learning gains. This paper reports on a study comparing the efficiency and effectiveness of learning from solving adaptive Parsons problems vs non-adaptive Parsons problem vs writing the equivalent code. The adaptive Parsons problems used both intraproblem and inter-problem adaptation. Intra-problem adaptation means that if the learner is struggling to solve the current problem, the problem can dynamically be made easier. Inter-problem adaptation means that the difficulty of the next problem is modified based on the learner's performance on the previous problem. This study provides evidence that solving intra-problem and inter-problem adaptive Parsons problems is a more efficient, but just as effective, form of practice as writing the equivalent code.

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Ericson, B. J., Foley, J. D., & Rick, J. (2018). Evaluating the efficiency and effectiveness of adaptive parsons problems. In ICER 2018 - Proceedings of the 2018 ACM Conference on International Computing Education Research (pp. 60–68). Association for Computing Machinery, Inc. https://doi.org/10.1145/3230977.3231000

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