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ERIC Number: EJ938543
Record Type: Journal
Publication Date: 2011-Oct
Pages: 6
Abstractor: As Provided
Reference Count: N/A
ISSN: ISSN-1041-6080
In the Context of Multiple Intelligences Theory, Intelligent Data Analysis of Learning Styles Was Based on Rough Set Theory
Narli, Serkan; Ozgen, Kemal; Alkan, Huseyin
Learning and Individual Differences, v21 n5 p613-618 Oct 2011
The present study aims to identify the relationship between individuals' multiple intelligence areas and their learning styles with mathematical clarity using the concept of rough sets which is used in areas such as artificial intelligence, data reduction, discovery of dependencies, prediction of data significance, and generating decision (control) algorithms based on data sets. Therefore, first multiple intelligence areas and learning styles of 243 mathematics prospective teachers studying at a state university were identified using the "Multiple Intelligence Inventory for Educators" developed by Armstrong and the "Learning Styles Scale" developed by Kolb. Second, the data was appropriated for rough set analysis and we identified potential learning styles that a student can have based on the learning style s/he already has. Certainty degrees of the learning style sets were [alpha][subscript R](D) [congruent to] 0.717, [alpha][subscript R](C) [congruent to] 0.618, [alpha][subscript R](AS) [congruent to] 0.699, [alpha][subscript R](AC) [congruent to] 0.461, and these sets were found to be rough sets. Finally, decision rules were identified for multiple intelligences and learning styles. (Contains 6 tables.)
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Assessments and Surveys: Learning Style Inventory