Development and Experimentation of an Adaptive e-Learning System
| Project/Area Number |
17500654
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Educational technology
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| Research Institution | Tokyo University of Technology |
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Principal Investigator |
INABA Taketoshi Tokyo University of Technology, メディア学部, professor (10386766)
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| Co-Investigator(Kenkyū-buntansha) |
MATSUNAGA Shinsuke Tokyo University of Technology, メディア学部, assistant professor (60318871)
佐藤 敬 東京工科大学, メディア学部, 教授 (20235360)
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| Project Period (FY) |
2005 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥3,410,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥210,000)
Fiscal Year 2007: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2006: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2005: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | e-learning / learner's individual traits / user model / AHS / cognitive style / Adaptive Hypermedia |
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| Research Abstract |
The majority of existing e-Learning systems are based on the traditional one-size-fits-all design approach which does not take into account the individual differences of each learner. Consequently, these systems are likely to deliver learning contents which do not fit well a learner's individual traits.
For overcoming this problem, adaptive hypermedia systems(AHS) can be an alternative to traditional systems ; adaptive hypermedia systems build a model of the goals, preferences and knowledge of the individual user and use these through the interaction to adapt the content, navigation support and layout to user needs.
Existing research has proposed enough evidence of the empirical effectiveness for learner using AHS. However, in the present state of the art on AHS, we do not have definitive guidelines when deciding what user features to take into account to build a user model.
In this study, we tried to build an adaptive hypermedia system in adoptingcognitive style to define a user as an individual. By cognitive style, we mean an individually preferred and habitual approach to organizing and representing information.
We have used Richard Riding's(1999) Cognitive Styles Label Wholist-Analytic to classify learners into two groups. After that, we made an attempt to match user styles and system versions, but also to mismatch them. We organized pre-test and Post-test design to compare the outcome of the two cases.
So far, we are not able to report significant differences against a mismatch adaptive condition. But in contrast, we have significant evidence for the positive effect of the Wholist design regardless of cognitive style. Thus, at least, we can confirm that cognitive style is an important user feature for future AHS design.
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Report
(4 results)
Research Products
(12 results)
