Developing a conception of the invisible and abstract internal processes that translate computer programs into observable outcomes is essential yet challenging for learners. Notional machines are simplified notions that educators adopt to make transparent or glass-box program dynamics to learners while teaching. In this thesis, I examined teaching and learning with notional machines during a 14-week online introductory electronic textiles unit in a charter high school. Two broad groups of research questions guided this dissertation—one, exploring teaching, and two, examining student learning with notional machines. Research questions on teaching included: (1) What notional machines did the teacher adopt? (2) What forms did the notional machines take in practice? Research questions on student learning included: (3) How did students interact with notional machines during the unit? (4) Did notional machines support students’ development of computing conceptual agency? If so, how? (5) How did students’ conceptions of computing systems shift after learning with notional machines? Multimodal data—online class recordings, student pre- and post-unit interviews, and student-generated artifacts—were qualitatively analyzed to answer the questions posed. Overall, observational data analysis provided one of the first frameworks to capture notional machines in practice. Notional machines belonged to one of the five themes depending on the electronic textiles concept being simplified and differed along the levels of granularity. Also, notional machines took two distinct representational forms—verbal explanations and participatory roleplays. Analysis of student interactions with notional machines highlighted the agentic roles learners took: questioning, adopting, explaining notions, and roleplaying program execution. Further, student pre- and post-unit interviews indicated that students’ conceptions of program dynamics shifted from being simplistic to more advanced in a set of everyday physical computing devices, showing promise for student sense-making of computing devices outside their immediate programming context. Overall, findings from this study point to future research directions to further explore teaching and learning with notional machines and their potential to expand computing learning beyond classroom contexts.