loweMisconceptionsNotionalMachine2018

2018

Report, 76, School of Engineering Education Graduate Student Series

Misconceptions and the Notional Machine in Very Young Programming Learners

This study looks at very young learners make mistakes and possibly form misunderstanding when learning to programming. A variety of national efforts are extending programming education to younger learners who are materials many adults struggle to learn. For decades literature has captured common misconceptions in using programming constructs (e.g. conditionals, loops, and recursion) in older learners, but early learners may wait years before they tackle these complex concepts. Many model misconceptions as a missing or inaccurate notional machine. The notional machine is an individual’s mental model, representing how a programming language executes on a real device. The notional machine aligns with traditional learning models from several educational theorists, particularly Bruner’s three stages of representations and Kahneman’s neuroscience-based modeling of the mind. To better understand the early thought process of and learning theory for teaching novices, this study looks at videos of early elementary students working to create basic navigational programs for simple robots. We observed students in K-2 and categorized the mistakes made and strategies used to achieve their goals. Our findings align with prior misconception literature in very young learners around the ‘problem’ being the source of more misconceptions than the language. We also find promising cases which support learning theory around the notional machine, Bruner’s representations and Kahneman’s two mind model. Using this theory suggests possible approaches to consider in teaching young learners to program.