Rules of Program Behavior

Author:

Duran Rodrigo1,Sorva Juha2,Seppälä Otto2

Affiliation:

1. Federal Institute of Mato Grosso do Sul, Brazil and Aalto University, Finland

2. Department of Computer Science, Aalto University, Finland

Abstract

We propose a framework for identifying, organizing, and communicating learning objectives that involve program semantics. In this framework, detailed learning objectives are written down as rules of program behavior (RPBs). RPBs are teacher-facing statements that describe what needs to be learned about the behavior of a specific sort of programs. Different programming languages, student cohorts, and contexts call for different RPBs. Instructional designers may define progressions of RPB rulesets for different stages of a programming course or curriculum; we identify evaluation criteria for RPBs and discuss tradeoffs in RPB design. As a proof-of-concept example, we present a progression of rulesets designed for teaching beginners how expressions, variables, and functions work in Python. We submit that the RPB framework is valuable to practitioners and researchers as a tool for design and communication. Within computing education research, the framework can inform, among other things, the ongoing exploration of “notional machines” and the design of assessments and visualizations. The theoretical work that we report here lays a foundation for future empirical research that compares the effectiveness of RPB rulesets as well as different methods for teaching a particular ruleset.

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Association for Computing Machinery (ACM)

Subject

Education,General Computer Science

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Computational Thinking and Notional Machines: The Missing Link;ACM Transactions on Computing Education;2023-12-11

2. Domain-Specific Theories of Teaching Computing: Do they Inform Practice?;Proceedings of the 23rd Koli Calling International Conference on Computing Education Research;2023-11-13

3. Constructing feedback for computer science MCQ wrong answers using semantic profiling (Discussion Paper);Proceedings of the 23rd Koli Calling International Conference on Computing Education Research;2023-11-13

4. Evaluating the Utility of Notional Machine Representations to Help Novices Learn to Code Trace;Proceedings of the 2023 ACM Conference on International Computing Education Research V.1;2023-08-07

5. Expressions in Java: Essential, Prevalent, Neglected?;Proceedings of the 2022 ACM SIGPLAN International Symposium on SPLASH-E;2022-11-29

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