Notional Machines and Programming Language Semantics in Education

A formal semantics of a language serves many purposes. It can help debug the language’s design, be used to prove type soundness, and guide optimizers to confirm that their work is correctness-preserving. Formal semantics are evaluated by several criteria: full abstraction, adequacy, soundness and completeness, faithfulness to an underlying implementation, and so on. Unfortunately, we know relatively little about how non-experts, such as students, actually employ a semantics. Which models are they able to grasp? How useful are these as they explain or debug programs? How does their use of models evolve with the kinds of programs they write? And does studying these kinds of questions yield any new insights into forms of semantics? This Dagstuhl Seminar intended to bridge this gap. It brought together representatives of the two communities-who usually travel in non-intersecting circles-to enable mutual understanding and cross-pollination. The Programming Languages community uses mathematics and focuses on formal results; the Computing Education Research community uses social science methods and focuses on the impact on humans. Neither is superior: both are needed to arrive at a comprehensive solution to creating tools for learning