A Middle-School Camp Emphasizing Data Science and Computing for Social Good
| Autor: | Samuel A. Rebelsky, Sarah Dahlby Albright, Beatriz Herce-Hagiwara, Caelin Bryant, Nolan Schoenle, Shyamala Gumidyala, Seoyeon Lee, Annabella Koures, Jonathan Gilmour, Zhen Chen, Jonah Zimmerman, Anh Thu Pham, Yesheng Chen, Marli Remash, Halle Remash, James Msekela |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
media_common.quotation_subject 05 social sciences 050301 education 02 engineering and technology Social issues Pair programming Data science Code (semiotics) Field (computer science) Block (programming) 020204 information systems Perception ComputingMilieux_COMPUTERSANDEDUCATION 0202 electrical engineering electronic engineering information engineering Relevance (law) 0503 education Curriculum media_common |
| Zdroj: | SIGCSE |
| Popis: | The underrepresentation within computer science of women, domestic students of color, and students with lower socioeconomic-status remains a national issue. Recent studies demonstrate two critical factors: Persistent stereotypes about "who does computer science" can preclude interest in the field for members of these groups; many also perceive computing as "irrelevant" and "asocial". While these issues must be addressed at multiple ages and levels, many suggest that we should start early, before students have developed stereotypes. As a step in combating (mis-)perceptions of ability and relevance, we developed and conducted a spectrum of week-long summer "code camps" for regional middle-school students. These camps emphasize meaningful uses of computing, on building self-efficacy, and on broadening understanding of who does and can do computer science. In this paper, we focus on our "data science for social good" (ds4sg) camp, in which students explored computational approaches to data science through a lens of computing for social good, discovering how computing helps them not only better understand societal issues but also convince others to address problems. We discuss the rationale for the curriculum and its content, including our uses of pair programming, personal projects, and a growth model that brings students from block-based programming to professional Jupyter data notebooks. We consider the short-term effects the camps have on students' self-efficacy and perceptions of computer science. We conclude with recommendations and guidelines for those intending to offer similar camps. |
| Databáze: | OpenAIRE |
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