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Leveraging Data Science and Social-Impact Analysis to Broaden Participation in Introductory Computer Science Courses

dc.contributor.authorFisler, Kati
dc.contributor.editorKienle, Andrea
dc.contributor.editorHarrer, Andreas
dc.contributor.editorHaake, Joerg M.
dc.contributor.editorLingnau, Andreas
dc.date.accessioned2021-08-25T08:21:49Z
dc.date.available2021-08-25T08:21:49Z
dc.date.issued2021
dc.description.abstractRecent trends – such as rising demand for computing courses, the emergence of Data Science as a critical skill, attention to the lack of diversity in computing workforces, and growing concerns about the social impacts of algorithmic decision-making systems – call on educators to revisit how we teach introductory computing and informatics courses. The speaker is three years into an experiment with redesigning the introductory computing course to combine data science, basic data structures, and social impacts into an introductory course meant for students across the university. The course has proven successful, attracting a diverse student population across each of gender, race, and academic interests. The talk will describe the course design, its research-based foundations, and lessons learned about addressing these trends through revitalized introductory courses.en
dc.identifier.isbn978-3-88579-710-4
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/37014
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofDELFI 2021
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-316
dc.subjectData Science
dc.subjectIntroductory Computing
dc.subjectSocial Impact
dc.titleLeveraging Data Science and Social-Impact Analysis to Broaden Participation in Introductory Computer Science Coursesen
dc.typeText/Conference Abstract
gi.citation.endPage22
gi.citation.publisherPlaceBonn
gi.citation.startPage21
gi.conference.date13.-15. September 2021
gi.conference.locationOnline
gi.conference.sessiontitleKeynotes

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