Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/1051
Title: Local Ground: A Toolkit Supporting Metarepresentational Competence in Data Science
Authors: Van Wart, Sarah
Parikh, Tapan
Issue Date: Jun-2014
Publisher: Boulder, CO: International Society of the Learning Sciences
Citation: Van Wart, S. & Parikh, T. (2014). Local Ground: A Toolkit Supporting Metarepresentational Competence in Data Science. In Joseph L. Polman, Eleni A. Kyza, D. Kevin O'Neill, Iris Tabak, William R. Penuel, A. Susan Jurow, Kevin O'Connor, Tiffany Lee, and Laura D'Amico (Eds.). Learning and Becoming in Practice: The International Conference of the Learning Sciences (ICLS) 2014. Volume 3. Colorado, CO: International Society of the Learning Sciences, pp. 1589-1590.
Abstract: Local Ground is an online data collection, mapping and visualization platform that allows youth to learn and use data skills in support of local projects. Local Ground is unique in its ability to display and translate between a variety of representations of spatial phenomena, starting with drawings. In our studies, we have found that students have used Local Ground to combine and translate between different representations for sense-making and communication.
URI: https://doi.dx.org/10.22318/icls2014.1589
https://repository.isls.org//handle/1/1051
Appears in Collections:ICLS2014

Files in This Item:
File SizeFormat 
1589-1590.pdf155.28 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.