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Machine-processable Representation of Training Outcomes

P Iskandar, Yulita Hanum, Gilbert, Lester and Wills, Gary (2011) Machine-processable Representation of Training Outcomes IEEE Learning Technology Newsletter, 13, (1)

Record type: Article


Modelling a domain, a process, or data is a common way of understanding it. The purpose of modelling is simplification, so that the domain is easier to understand. Often, models are mathematical because they are predictable and repeatable. There are many teaching and learning theories such as behaviourism, cognitivisim, constructivism, and cybernetics. Modelling and validating these theories is problematic because of their inherent aspect of ambiguity and lack of repeatability. This paper constructed a model of a major aspect of teaching and learning that is machine-processable. This provides repeatable, realistic, less ambiguous, and deterministic results for testing and validating. A machine-processable representation may be expect to be able to validate such models to better understand teaching and learning situations.

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Published date: January 2011
Organisations: Electronic & Software Systems


Local EPrints ID: 271994
PURE UUID: 73525f1f-bd6b-456f-bc1a-d3a1d488117e
ORCID for Gary Wills: ORCID iD

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Date deposited: 09 Feb 2011 03:38
Last modified: 18 Jul 2017 06:36

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Author: Yulita Hanum P Iskandar
Author: Lester Gilbert
Author: Gary Wills ORCID iD

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