The Impact of Solving Adaptive Parsons Problems with Common and Uncommon Solutions

Traditional introductory computer programming practice such as code-tracing and code-writing can be time-intensive, frustrating, and decrease students’ engagement and motivation. Parsons problems, which require learners to place mixed-up code blocks in the correct order, usually improve problem-solving efficiency, lower cognitive load, and most undergraduates find them useful for learning how to program. Parsons problems can also be adaptive—meaning the difficulty of a problem is based on a learner’s performance. To become proficient at computer programming, it is critical for novice learners to be explicitly taught how to recognize and apply programming patterns/solutions. But how do we help them to acquire this knowledge efficiently and effectively? Our prior research revealed that an adaptive Parsons problem with an uncommon solution was not significantly more efficient to solve than writing the equivalent code. Interestingly, 77% of the students used the unusual Parsons problem solution to later solve an equivalent write-code problem. Hence, we hypothesized that changing the unusual Parsons problem solution to the most common student-written solution would make that problem significantly more efficient to solve. To test our hypothesis, we conducted a mixed within-between-subjects experiment with 95 undergraduates. The results confirmed our hypothesis and its inverse. Students were significantly more efficient at solving the modified Parsons problem (made with a common solution) than writing the equivalent code. Students were not significantly more efficient at solving a different Parsons problem with an uncommon solution. We also explored the impact on cognitive load ratings for each problem type. There was a significant difference in cognitive load ratings for students who solved the modified Parsons problem first versus those who wrote the equivalent code first. To understand how students solve Parsons problems and the impact of changing the adaptation process, we also report on three think-aloud observations with undergraduates. Results revealed that some students could benefit from help with self-regulated learning (planning), more explanation of distractors, and that there were no new problems due to modifications of the adaptation process. Our findings have implications for how to automatically generate and sequence adaptive Parsons problems.