Identifying animals with dynamic location-aware and semantic hierarchy-based image browsing for different cognitive style learners

Dunwei Wen, Ming Chi Liu, Yueh Min Huang, Kinshuk, Pi Hsia Hung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Lack of overall ecological knowledge structure is a critical reason for learners' failure in keyword-based search. To address this issue, this paper firstly presents the dynamic location-aware and semantic hierarchy (DLASH) designed for the learners to browse images, which aims to identify learners' current interesting sights and provide adaptive assistance accordingly in ecological learning. The main idea is based on the observation that the species of plants and animals are discontinuously distributed around the planet, and hence their semantic hierarchy, besides its structural similarity with WordNet, is related to location information. This study then investigates how different cognitive styles of the learners influence the use of DLASH in their image browsing. The preliminary results show that the learners perform better when using DLASH based image browsing than using the Flickr one. In addition, cognitive styles have more effects on image browsing in the DLASH version than in the Flickr one.

Original languageEnglish
Title of host publicationProceedings - 10th IEEE International Conference on Advanced Learning Technologies, ICALT 2010
Pages355-359
Number of pages5
DOIs
Publication statusPublished - 2010 Nov 4
Event10th IEEE International Conference on Advanced Learning Technologies, ICALT 2010 - Sousse, Tunisia
Duration: 2010 Jul 52010 Jul 7

Publication series

NameProceedings - 10th IEEE International Conference on Advanced Learning Technologies, ICALT 2010

Other

Other10th IEEE International Conference on Advanced Learning Technologies, ICALT 2010
CountryTunisia
CitySousse
Period10-07-0510-07-07

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics

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