Data science education can help broaden participation in computer science (CS) because it provides rich, authentic contexts for students to apply their computing knowledge. Data literacy, particularly among underrepresented students, is critical to everyone in this increasingly digital world. However, the integration of data science into K-12 schools is nascent, and the pedagogical training of CS teachers in data science remains limited. Our research-practice partnership modified an existing data science unit to include two pedagogical techniques known to support minoritized students: rich classroom discourse and personally-relevant problem-solving. This paper describes the iterative design process we used to revise and pilot this new data science unit.
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Discourse Practices in Computer Science Education
Rich classroom discussion, or discourse, has long been a recommended pedagogical practice in K-12 math and science education. Research shows that discourse is beneficial for all learners, but especially for English learners and minoritized students in STEM. Discourse helps develop students' agency, academic language, and conceptual understanding. Some K-12 computer science (CS) curricula incorporate student discourse, but we believe it is under-used. In this paper, we review how discourse helps students learn, discuss the use of discourse in CS and math education, share ideas for promoting discourse in CS classrooms, and call on curriculum developers, teacher professional learning providers, and researchers to support the increased use of discourse in K-12 CS education.
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- Award ID(s):
- 2122485
- PAR ID:
- 10558122
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400704239
- Page Range / eLocation ID:
- 632 to 638
- Subject(s) / Keyword(s):
- computer science education discourse academic language
- Format(s):
- Medium: X
- Location:
- Portland OR USA
- Sponsoring Org:
- National Science Foundation
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