Building equitable computer science classrooms

Shah, Niral; Lewis, Colleen M.; Caires, Roxane; Khan, N. B.; Qureshi, Amirah; Ehsanipour, Danielle; Gupta, Nidhi

doi:10.1145/2445196.2445276

Cited by 56 publications

(20 citation statements)

References 10 publications

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“…Our finding that there were gross inequities in the Aaron-Jason and Peter-Jason dyads is surprising because the classroom practices were designed to support equity [34]. For example, the pair-programming structures were carefully designed to ensure that both students had equal time as driver and navigator, and that students reliably switched roles of driver and navigator.…”

Section: Discussionmentioning

confidence: 98%

“…The selfpaced curriculum was intended to provide students the opportunity to progress at their own pace, while offering daily synchronization points so that all students were exposed to the same material [34].…”

Section: Discussionmentioning

confidence: 99%

“…While in aggregate these results appear overwhelmingly positive, students and educators have noted instances where pair programming appears to limit one or both of the partners' opportunities to learn. We found that while the pair-programming structures were designed to promote equitable participation [34], in some cases gross inequity emerged within a partnership. In the examples presented here, we attribute the students' goals for completing work as quickly as possible (i.e., speed) as facilitating inequitable interactions.…”

Section: Introductionmentioning

confidence: 94%

“…[30]). Details regarding the goals, structure, and design of the class have been documented in a previous publication [34].…”

Section: Research Contextmentioning

confidence: 99%

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How Equity and Inequity Can Emerge in Pair Programming

Lewis

Shah

2015

Proceedings of the Eleventh Annual International Conference on International Computing Education Research

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Research suggests that pair programming increases student performance and decreases student attrition. However, less is known about the ways in which pair programming can unintentionally lead to inequitable relationships between students. Audio data were collected from pair programming interactions in a sixth-grade computer science enrichment program designed to promote equity. However, even in this context, there were surprising instances of inequity. We measured inequity by documenting the distribution of students' questions, commands, and total talk within four pairs. Analysis revealed that less equitable pairs sought to complete tasks quickly and this may have led to patterns of marginalization and domination. Notably, this focus on speed was not evident in the more equitable pairs. These findings are important for understanding mechanisms of inequity and designing equitable collaboration practices in computer science.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

“…[30]). Details regarding the goals, structure, and design of the class have been documented in a previous publication [34].…”

Section: Research Contextmentioning

confidence: 99%

See 2 more Smart Citations

How Equity and Inequity Can Emerge in Pair Programming

Lewis

Shah

2015

Proceedings of the Eleventh Annual International Conference on International Computing Education Research

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“…Importantly, prior literature has established that pedagogy is equally, if not more important, than content in efforts to stimulate student interest and broaden participation in computing (Shah et al. 2013).…”

Section: Context Of This Workmentioning

confidence: 99%

A Multiyear Investigation of Student Computational Thinking Concepts, Practices, and Perspectives in an After-School Computing Program

Mouza

Pan

Huí

et al. 2020

Journal of Educational Computing Research

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In this work, we examine whether repeated participation in an after-school computing program influenced student learning of computational thinking concepts, practices, and perspectives. We also examine gender differences in learning outcomes. The program was developed through a school–university partnership. Data were collected from 138 students over a 2.5-year period. Data sources included pre–post content assessments of computational concepts related to programming in addition to computational artifacts and interviews with a purposeful sample of 12 participants. Quantitative data were analyzed using statistical methods to identify gains in pre- and post-learning of computational thinking concepts and examine potential gender differences. Interview data were analyzed qualitatively. Results indicated that students made significant gains in their learning of computational thinking concepts and that gains persisted over time. Results also revealed differences in learning of computational thinking concepts among boys and girls both at the beginning and end of the program. Finally, results from student interviews provided insights into the development of computational thinking practices and perspectives over time. Results have implications for the design of after-school computing programs that help broaden participation in computing.

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