Abstract
Comprehensibility in modeling is the ability of stakeholders to understand relevant aspects of the modeling process. In this article, we provide a framework to help guide exploration of the space of comprehensibility challenges. We consider facets organized around key questions: Who is comprehending? Why are they trying to comprehend? Where in the process are they trying to comprehend? How can we help them comprehend? How do we measure their comprehension? With each facet we consider the broad range of options. We discuss why taking a broad view of comprehensibility in modeling is useful in identifying challenges and opportunities for solutions.
Keywords
Computer scienceProcess (computing)ComprehensionKey (lock)Space (punctuation)Facet (psychology)Measure (data warehouse)Data scienceProcess modelingManagement sciencePsychologyWork in processEngineering
MeSH Terms
ComprehensionHumansMachine LearningModelsTheoreticalUser-Computer Interface
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Publication Info
- Year
- 2016
- Type
- article
- Volume
- 4
- Issue
- 2
- Pages
- 75-88
- Citations
- 59
- Access
- Closed
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Cite This
Michael Gleicher
(2016).
A Framework for Considering Comprehensibility in Modeling.
Big Data
, 4
(2)
, 75-88.
https://doi.org/10.1089/big.2016.0007
Identifiers
- DOI
- 10.1089/big.2016.0007
- PMID
- 27441712
- PMCID
- PMC4932655