Background/Context: Bi/multilingual students’ STEM learning is better supported when educators leverage their language and cultural practices as resources, but STEM subject divisions have been historically constructed based on oppressive, dominant values and exclude the ways of knowing of nondominant groups. Truly promoting equity requires expanding and transforming STEM disciplines. Purpose/Objective/Research Question/Focus of Study: This article contributes to efforts to illuminate emergent bi/multilingual students’ ways of knowing, languaging, and doing in STEM. We follow the development of syncretic literacies in relation to translanguaging practices, asking, How do knowledges and practices from different communities get combined and reorganized by students and teachers in service of new modeling practices? Setting and Participants: We focus on a seventh-grade science classroom, deliberately designed to support syncretic literacies and translanguaging practices, where computer science concepts were infused into the curriculum through modeling activities. The majority of the students in the bilingual program had arrived in the United States at most three years before enrolling, from the Caribbean and Central and South America. Research Design: We analyze one lesson that was part of a larger research–practice partnership focused on teaching computer science through leveraging translanguaging practices and syncretic literacies. The lesson was a modeling and computing activitymore »
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.
- Award ID(s):
- 1348866
- Publication Date:
- NSF-PAR ID:
- 10353799
- Journal Name:
- SIGCSE 2022: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education
- Volume:
- 1
- Page Range or eLocation-ID:
- 64 to 70
- Sponsoring Org:
- National Science Foundation
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