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Title: Aesthetics of Authenticity for Teachers’ Data Set Preferences
Authors: Lee, Victor R.
Delaney, Victoria
Keywords: Learning Sciences
Issue Date: Jun-2021
Publisher: International Society of the Learning Sciences
Citation: Lee, V. R. & Delaney, V. (2021). Aesthetics of Authenticity for Teachers’ Data Set Preferences. In de Vries, E., Hod, Y., & Ahn, J. (Eds.), Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021. (pp. 259-266). Bochum, Germany: International Society of the Learning Sciences.
Abstract: This paper explores secondary school teachers’ aesthetic judgments of data sets for prospective use to teach data science. Twelve teachers were interviewed and asked to examine and select from side-by-side data sets the one that would provide a more authentic experience for students. Situating our work in the “Knowledge in Pieces” epistemology, we drew upon diSessa’s (1993) notion of an aesthetic – a knowledge system for appraisal and judgment. We examined what sensitivities were expressed and how judgments were made and supported. We observed that teachers drew upon at least three senses of authenticity to characterize and select data sets: authentic as messy, authentic as requiring more work, and authentic as involving computation. Identification of these ways of determining authenticity represents an initial step in better understanding how teachers appraise data that could be used in their teaching.
Appears in Collections:ISLS Annual Meeting 2021

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