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Learning Animal Concepts with Semantic Hierarchy-Based Location-Aware Image Browsing and Ecology Task Generator

Ming-Chi Liu, , Kinshuk, Yueh-Min Huang

Abstract: This study firstly notices that lack of overall ecologic knowledge structure is one critical reason for learners' failure of keyword search. Therefore in order to identify their current interesting sight, the dynamic location-aware and semantic hierarchy (DLASH) is presented for learners to browse images. This hierarchy mainly considers that plant and animal species are discontinuously distributed around the planet, hence this hierarchy combines location information for constructing the semantic hierarchy through WordNet. After learners confirmed their intent information needs, this study also provides learners three kinds of image-based learning tasks to learn: similar-images comparison, concept map fill-out and placement map fill-out. These tasks are designed based on Ausubel's advance organizers and improved it by integrating three new properties: Displaying the nodes of the concepts by authentic images, automatically generating the knowledge structure by computer and interactively integrating new and old knowledge.
Keywords: Animals / Environmental factors / Radiofrequency identification / Mobile computing / Knowledge engineering / Pervasive computing / Information systems / Educational institutions / Education / Displays

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