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Title: Multilingual CS Education Pathways: Implications for Vertically-Scaled Assessment
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.  more » « less
Award ID(s):
1348866
PAR ID:
10353799
Author(s) / Creator(s):
;
Date Published:
Journal Name:
SIGCSE 2022: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education
Volume:
1
Page Range / eLocation ID:
64 to 70
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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