Students in introductory computing courses often struggle with fundamental computing topics and related concepts. Researchers have linked this with the challenge of conceptualizing the underlying mechanisms of basic computing processes and the multilayered interaction between the source code and the machine. To address this, educators frequently use notional machines (NMs) - simplified models of program execution that support students with diverse analogies, metaphors, diagrams, and representations. However, there is comparatively little guidance for CS educators on considerations and best practices in creating NMs for learning. In this poster, we present key considerations and design guidance for NM creation to support future CS educators.

Desktop digital fabrication tools, and 3D printers in particular, have been used by hobbyists and non-professional makers to design and develop custom, personalized assistive devices for people with disabilities for nearly a decade.However, medical professionals have been cautious about the impact of these devices on end-users, as makers have limited medical and therapeutic expertise.In this poster, we report the perspective of five Occupational Therapists who have adopted 3D printing to develop assistive technologies in their professional practice.Our investigative work explores their perspectives on 3D-printed assistive technologies, the barriers they face from academic and professional institutions, and highlights ways to support the incorporation of 3D printing into Occupational Therapy infrastructure.

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.

The making phenomenon encompasses a broad range of recent developments in personal design and fabrication capabilities, such as 3D printing and related techniques. As part of this, research projects and do-it-yourself assistive technology efforts have employed making in relation to accessibility. In order to better understand the relationship between people with disabilities and making, we conducted a systematic literature review focused on design-related ACM conferences between January 2010 and December 2020, identifying 74 full research papers. This paper presents our findings based on analysis and categorization of the literature to help characterize and understand trends and coverage in research on making and accessibility.