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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Taiyu Lin Kinshuk |
| Copyright Year | 2004 |
| Description | Author affiliation: Adv. Learning Technol. Res. Centre, Massey Univ., New Zealand (Taiyu Lin; Kinshuk) |
| Abstract | In the search of creating a representation, such as a cognitive trait model, of cognitive traits, such as working memory capacity or inductive reasoning ability, of a learner, it is hard to find a consensus model of the cognitive trait among different perspectives of cognitive science. Dichotomic node network (DNN) is developed to provide a viable solution to this problem. DNN is a network representation of an entity of which the constituents are nodes that is consisted of a pair of dichotomic attributes. Through the contradiction detection mechanism and inclusion resolution mechanism, DNN is able to: (1) represents of an entity contains multiple portrayals/perspectives; (2) select appropriate portrayals for any particular entity is very difficult or impossible; (3) handle nonlinear aggregation of portrayals in which combinations does not render result linearly, and therefore very suitable for cognitive trait model, and is potential for other applications. |
| Sponsorship | IEEE Tech. Committee on Learning Technol. IEEE Comput. Soc. Univ. of Joensuu |
| Starting Page | 702 |
| Ending Page | 704 |
| File Size | 213238 |
| Page Count | 3 |
| File Format | |
| ISBN | 0769521819 |
| DOI | 10.1109/ICALT.2004.1357628 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-08-30 |
| Publisher Place | Finland |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Navigation Cognitive science Linearity |
| Content Type | Text |
| Resource Type | Article |
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