Conference proceeding
Quality not Quantity! A Qualitative Evaluation and Proposal for Understanding the Depth of Audience "Knowledge" Post Data Extraction
2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI), pp 164-171
Aug 2020
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Knowledge is defined as...the result of machine extracted patterns; humans making sense of their environment; information generated and aggregated from software services or as the lowest form of human cognition. Different perspectives, different domains, but one concept. Information scientists are often concerned with retrieving knowledge from data sources and sharing that knowledge with concerned stakeholders; with such differing views on what qualifies as knowledge a cross-domain approach might prove beneficial. This work is a qualitative assessment of the layers of knowledge intended to bridge the gap between the analyst and their intended or unintended audiences. It examines the benefit of abstracting concepts used in the education discipline to justify including a post-evaluation stage to the Knowledge Discovered through Databases (KDD) framework. It also intends to promote awareness of the various human cognitive capacities and provide a useful approach for communicating and evaluating machine-extracted knowledge that supports higher order thinking.
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Details
- Title
- Quality not Quantity! A Qualitative Evaluation and Proposal for Understanding the Depth of Audience "Knowledge" Post Data Extraction
- Creators
- Kimberley Hemmings-Jarrett - Drexel University,College of Computing and Informatics,Philadelphia,PA,USA,19104Terryann Barnett - NYC,Department of Education,Bronx,NY,USA,10461Julian Jarrett - Lutron Electronics,Coopersburg,PA,USA,18036M. Brian Blake - dept. name of organization (of Aff.) name of organization (of Aff.),City,CountryDenise Agosto - Drexel University,College of Computing and Informatics,Philadelphia,PA,USA,19104
- Publication Details
- 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI), pp 164-171
- Conference
- 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI), 21st
- Publisher
- IEEE
- Number of pages
- 1
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000635425100023
- Scopus ID
- 2-s2.0-85092144858
- Other Identifier
- 991014976894904721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Computer Science, Interdisciplinary Applications
- Computer Science, Software Engineering
- Engineering, Electrical & Electronic