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The expansion of computer science (CS) into K-12 contexts has resulted in a diverse ecosystem of curricula designed for various grade levels, teaching a variety of concepts, and using a wide array of different programming languages and environments. Many students will learn more than one programming language over the course of their studies. There is a growing need for computer science assessment that can measure student learning over time, but the multilingual learning pathways create two challenges for assessment in computer science. First, there are not validated assessments for all of the programming languages used in CS classrooms. Second, it is difficult to measure growth in student understanding over time when students move between programming languages as they progress in their CS education. In this position paper, we argue that the field of computing education research needs to develop methods and tools to better measure students' learning over time and across the different programming languages they learn along the way. In presenting this position, we share data that shows students approach assessment problems differently depending on the programming language, even when the problems are conceptually isomorphic, and discuss some approaches for developing multilingual assessments of student learning over time.
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A Programming Language for Future Interests
Learning the system of estates in land and future interests can seem like learning a new language. Scholars and students must master unfamiliar phrases, razor-sharp rules, and arbitrarily complicated structures. Property law is this way not because future interests are a foreign language, but because they are a programming language. This Article presents Orlando, a programming language for expressing conveyances of future interests, and Littleton, a freely available online interpreter (at https://conveyanc.es) that can diagram the interests created by conveyances and model the consequences of future events. Doing so has three payoffs. First, formalizing future interests helps students and teachers of the subject by allowing them to visualize and experiment with conveyances. Second, the process of formalization is itself deeply illuminating about property doctrine and theory. And third, the computer-science subfield of programming language theory has untapped potential for legal scholarship: a programming-language approach takes advantage of the linguistic parallels between legal texts and computer programs.
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- Award ID(s):
- 2019313
- PAR ID:
- 10388237
- Editor(s):
- Emile Shehada
- Date Published:
- Journal Name:
- Yale journal of law technology
- Volume:
- 24
- Issue:
- 75
- ISSN:
- 2766-2403
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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