EconPapers    
Economics at your fingertips  
 

How quickly do we learn conceptual models?

Palash Bera and Geert Poels

European Journal of Information Systems, 2019, vol. 28, issue 6, 663-680

Abstract: In organisations, conceptual models are used for understanding domain concepts. Learning the domain from models is crucial for the analysis and design of information systems that are intended to support the domain. Past research has proposed theories to structure conceptual models in order to improve learning. It has, however, never been investigated how quickly domain knowledge is acquired when using theory-guided conceptual models. Based on theoretical arguments, we hypothesise that theory-guided conceptual models expedite the initial stages of learning. Using the REA ontology pattern as an example of theoretical guidance, we show in a laboratory experiment how an eye-tracking procedure can be used to investigate the effect of using theory-guided models on the speed of learning. Whereas our experiment shows positive effects on both outcome and speed of learning in the initial stages of learning, the real contribution of our paper is methodological, i.e. an eye-tracking procedure to observe the process of learning from conceptual models.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/0960085X.2019.1673972 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjisxx:v:28:y:2019:i:6:p:663-680

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjis20

DOI: 10.1080/0960085X.2019.1673972

Access Statistics for this article

European Journal of Information Systems is currently edited by Par Agerfalk

More articles in European Journal of Information Systems from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:tjisxx:v:28:y:2019:i:6:p:663-680