An evaluation of pedagogically informed parameterised questions for self assessment
An evaluation of pedagogically informed parameterised questions for self assessment
Self-assessment is a crucial component of learning. Learners can learn by asking themselves questions and attempting to answer them. However, creating effective questions is time-consuming because it may require considerable resources and the skill of critical thinking. Questions need careful construction to accurately represent the intended learning outcome and the subject matter involved. There are very few systems currently available which generate questions automatically, and these are confined to specific domains. This paper presents a system for automatically generating questions from a competency framework, based on a sound pedagogical and technological approach. This makes it possible to guide learners in developing questions for themselves, and to provide authoring templates which speed the creation of new questions for self-assessment. This novel design and implementation involves an ontological database that represents the intended learning outcome to be assessed across a number of dimensions, including level of cognitive ability and subject matter. The system generates a list of all the questions that are possible from a given learning outcome, which may then be used to test for understanding, and so could determine the degree to which learners actually acquire the desired knowledge. The way in which the system has been designed and evaluated is discussed, along with its educational benefits.
competency, self-assessment, ontology, ims qti
235-248
Sitthisak, Onjira
7a4ff5e1-d550-48cf-8d77-a294cbd9f039
Gilbert, Lester
a593729a-9941-4b0a-bb10-1be61673b741
Davis, Hugh
1608a3c8-0920-4a0c-82b3-ee29a52e7c1b
2008
Sitthisak, Onjira
7a4ff5e1-d550-48cf-8d77-a294cbd9f039
Gilbert, Lester
a593729a-9941-4b0a-bb10-1be61673b741
Davis, Hugh
1608a3c8-0920-4a0c-82b3-ee29a52e7c1b
Sitthisak, Onjira, Gilbert, Lester and Davis, Hugh
(2008)
An evaluation of pedagogically informed parameterised questions for self assessment.
[in special issue: Reframing E?assessment: Adopting New Media and Adapting Old Frameworks]
Learning, Media and Technology, 33 (3), .
(doi:10.1080/17439880802324210).
Abstract
Self-assessment is a crucial component of learning. Learners can learn by asking themselves questions and attempting to answer them. However, creating effective questions is time-consuming because it may require considerable resources and the skill of critical thinking. Questions need careful construction to accurately represent the intended learning outcome and the subject matter involved. There are very few systems currently available which generate questions automatically, and these are confined to specific domains. This paper presents a system for automatically generating questions from a competency framework, based on a sound pedagogical and technological approach. This makes it possible to guide learners in developing questions for themselves, and to provide authoring templates which speed the creation of new questions for self-assessment. This novel design and implementation involves an ontological database that represents the intended learning outcome to be assessed across a number of dimensions, including level of cognitive ability and subject matter. The system generates a list of all the questions that are possible from a given learning outcome, which may then be used to test for understanding, and so could determine the degree to which learners actually acquire the desired knowledge. The way in which the system has been designed and evaluated is discussed, along with its educational benefits.
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An_evaluation_of_pedagogically_informed_parameterised_questions_for_self_assessment.pdf
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e-pub ahead of print date: 17 September 2008
Published date: 2008
Keywords:
competency, self-assessment, ontology, ims qti
Organisations:
Web & Internet Science, Electronic & Software Systems
Identifiers
Local EPrints ID: 266117
URI: http://eprints.soton.ac.uk/id/eprint/266117
ISSN: 1743-9884
PURE UUID: d13d6fc6-2018-440e-bbfc-25cd9b178c44
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Date deposited: 12 Jul 2008 20:32
Last modified: 15 Mar 2024 02:36
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Contributors
Author:
Onjira Sitthisak
Author:
Lester Gilbert
Author:
Hugh Davis
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