Notional Machines for Inclusive Learning

Understanding computing concepts is one of the foundational elements of computational thinking, which helps students formulate logic and algorithms for effectively developing and designing codes. However, novice learners often struggle to understand basic computing concepts, such as variables, loops, arrays, conditionals, and functions. This is often caused by the invisibility of the computing mechanism and the complexity of language syntax and conventions. Moreover, challenges in learning computing concepts can be compounded for learners with atypical conditions (e.g. visual, cognitive, and learning impairment, attention deficit, and neurodivergence). To tackle this, teachers of typical ability learners frequently use notional machines (NMs) - simplified representations (e.g. diagrams, flow charts, and analogies) created to assist novice programmers in understanding abstract concepts, program execution, and dynamics. In addition, traditional learning activities may need assistive learning interventions (ALIs) and learner-oriented practices like physical tactile models, computing artifacts, and interactive learning tools. NM approaches tend to focus on the explanation of concepts from instructor to learner, while ALIs tend to focus on learner support in activities. Both methods can provide advantages for learning, and we expect that a combined approach would provide even greater synergy for all learners. However, little consideration has been given to how NM and ALI approaches could be effectively combined. This research investigates how NM and ALI can be effectively brought together more holistically as an inclusive pedagogical tool - notional machines for inclusive learning (NMIL). In this full paper, we develop the conceptualization of NMIL, grounded in previous NM and ALI research. We also propose a structured method for creating NMIL. Finally, we also develop a physical NMIL model, based on the structured NMIL creation method, to teach lists in Python, as part of an introductory computer science course.