Learning basic Python concepts via self-explanation: A preliminary python ACT-R model
This paper presents a cognitive modelling approach to investigating student learning of computer programming concepts via self-explanation. Self-explanation involves explaining instructional material to oneself by generating inferences about the material. Here, we discuss the potential of self-explanation for the domain of programming and present a preliminary Python ACT-R model of novice and experienced students learning basic Python concepts via self-explanation. The model adds to knowledge of learning via self-explanation in the domain by formalizing processes involved and by acting as a base model that can be expanded to explore and simulate more aspects of this type of student learning.