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Abstract. We investigated teacher learning within a professional development (PD) workshop series on computational thinking (CT) for elementary-level mentor teachers. The purpose of the PD was to prepare mentor teachers to support preservice teachers in integrating CT into their classroom practice, toward the broader goal of advancing CT for all in the early grades. We examined the ways in which participants collaboratively built on existing professional knowledge as they engaged in professional learning activities designed to introduce CT and related pedagogies for elementary science education. Our data sources were field notes, artifacts, drawings, written reflections, and focus group interviews. We describe how participants developed new understandings of CT integration and made connections to existing professional knowledge of their students, their curriculum, and their school contexts. We discuss implications for teacher learning and PD design relevant to CT, and make recommendations for future research.
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Temporality in Data Science Education: Early Results from a Grounded Theory Study of an NSF-Funded CyberTraining Workshop
Interest in data science, especially within the context of grad- uate education, is exploding. In this study we present initial results from an ongoing qualitative study of an interdisciplinary cyberinfrastructure- focused NSF-funded graduate data science education workshop hosted at an iSchool in the US. The complexity of the workshop curriculum, the participants' and instructors' disparate disciplinary backgrounds, and the technical tools employed are particularly suited to qualitative meth- ods which can synthesize all of these aspects from rich observational, ethnographic, and trace data collected as part of the authors' role on the grant's qualitative evaluation team. The success of the workshop in equipping participants to do reproducible computational science was in part due to the successful acculturation process, whereby participants comprehended, altered, and enacted new norms amongst themselves. At the same time, we observed potential challenges for data science in- struction resulting from the rhetorical framing of the technologies as inescapably new. This language, which mirrors that of a successful grant proposal, tends to obscure the deeply embedded and contingent history of the command-line technologies required to preform computational sci- ence, many of which are decades old. We conclude by describing our on- going work, future theoretical sampling plans from this and future data, and the contributions that our ndings can provide to graduate data science curriculum development and pedagogy.
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- Award ID(s):
- 1730390
- PAR ID:
- 10203529
- Date Published:
- Journal Name:
- International Conference on Information
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-43687-2_43
