Inclusive CT learning engages students in critiquing bias in real technological systems¶

Claim · All claims
Evidence · 1 study · theoretical r2 · q1
Currency · newest study 2021 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? The report argues that CT skills equip students to discuss and critique how computing systems impact social equality, using authentic examples such as gender bias in Gmail Smart Compose and error rates for marginalized groups in facial and speech recognition. → bias-critique

Evidence¶

bias-critique¶

Mills, K., Coenraad, M., Ruiz, P., Burke, Q., & Weisgrau J. (2021, December). Computational thinking for an inclusive world: A resource for educators to learn and lead. Digital Promise. https://doi.org/10.51388/20.500.12265/138

q1 · i? · theoretical · r2

In the inclusive pedagogies section the report proposes, as an illustrative example rather than a tested result, that "students might discuss the implications of gender biases in natural language processing systems such as Gmail Smart Compose" or higher error rates for marginalized groups in facial and speech recognition, and explore real-world data sets to identify patterns of inequity. It notes these discussions do not need to wait until high school.

"students might discuss the implications of gender biases in natural language processing systems such as Gmail Smart Compose"

Discussion¶

Related Claims¶

• Educators identify real-time translation, dialect handling, and speech recognition as potentially transformative for multilingual learners and students with disabilities, but current speech tools fail on accents and dialects — related
• Positioning personal experience as an epistemic resource supports expansion from inward to outward critical orientations — related
• Computing education has historically excluded marginalized student populations, and large inequities in access persist — a broader claim this one bears on
• Machine learning systems can perpetuate biases contained in their training data — related
• Algorithmic bias and data privacy are central ethical risks of AI in adult education — related
• Inclusion of students with disabilities often falls short because efforts start from a place of exclusion, making students guests who must earn access — related