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Project: Humanities, Arts, Science, and Technology Alliance and Collaboratory (HASTAC)
Abstract
This talk brings the interdisciplinary perspective of the social sciences, humanities, and digital humanities to data science and is a follow-up to our May 28 “Big (and Messy) Data” workshop as part of a two-year NSF EAGER grant on data and cross-disciplinary collaboration and mentoring. A key concern from this workshop that needs to be applied to our National Data Service is what my colleague and collaborator Richard Marciano has termed the “forensics” of understanding and interpreting big data. If we are going to provide a national data service for researchers, we must include in that service useful questions that any researcher, in any field, must pose in order to fully understand the biases, histories, and ambiguities of data, including the way that the inputs can distort the outputs an that all data requires interpretation and context.
Big Data Comes to School: Challenges the Learning Sciences
William Cope, University of Illinois Urbana-Champaign
Project: UIUC Department of Education
Abstract
This brief presentation will provide an overview of emerging “big data” challenges in education, including the range of data sources, data types and data collection technologies, and the problem for meta-analysis presented by variant data models. Successfully addressing these challenges could reap significant dividends for education, not only transforming the methodologies and processes of educational research, but also for the development of a new generation of student assessments. I will conclude by proposing in outline form a ‘National Data Dictionary’ that might support the federation of datasets that are at this point semantically incommensurable.