Collaboratory at Columbia: An Aspen Grove of Data Science Education

Zaugg, Isabelle A.; Culligan, Patricia J.; Witten, Richard; Tian, Zheng

doi:10.1162/99608f92.53c4a1b4

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of our communal aspirations in the field of data ethics, this is important on two fronts. First, courses that bridge disciplines and do not require pre-existing technical proficiency have the potential to attract new cohorts of students to engage with computational fields (Zaugg et al , 2021). It is important to bring students with ethical insights from other disciplinary backgrounds into the field of computation, and transdisciplinary data ethics coursework facilitate that.…”

Section: Background and Related Workmentioning

confidence: 99%

Mapping data ethics curricula

Reeve

Zaugg

Tian

2022

JICES

Self Cite

View full text Add to dashboard Cite

Purpose As data-driven tools increasingly shape our life and tech ethics crises become strikingly frequent, data ethics coursework is urgently needed. The purpose of this study is to map the field of data ethics curricula, tracking relations between courses, instructors, texts and writers, and present a proof-of-concept interactive website for exploring these relations. This method is designed to be used in curricular research and development and provides multiple vantage points on this multidisciplinary field. Design/methodology/approach The authors use data science methods to foster insights about the field of data ethics education and literature. The authors present a semantic, linked open data graph in the Resource Description Framework, along with proof-of-concept analyses and an exploratory website. Its framework is open-source and language-agnostic, providing the seed for future contributions of code, syllabi and resources from the global data ethics community. Findings This method provides a convenient means of exploring an overview of the field of data ethics’ social and textual relations. For educators designing or refining a course, the authors provide a method for curricular introspection and discovery of transdisciplinary curricula. Research limitations/implications The syllabi the authors have collected are self-selected and represent only a subset of the field. Furthermore, this method exclusively represents a course’s assigned literature rather than a holistic view of what courses teach. The authors present a prototype rather than a finished product. Originality/value This curricular survey provides a new way of modeling a field of study, using existing ontologies to organize graph data into a comprehensible overview. This framework may be repurposed to map the institutional knowledge structures of other disciplines, as well.

show abstract

Section: Background and Related Workmentioning

confidence: 99%