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Boosting Semantic Relations for Example Population in Concept Learning

Boosting Semantic Relations for Example Population in Concept Learning

Ming-Chi Liu, Kinshuk, Yueh-Min Huang, Dunwei Wen
Copyright: © 2012 |Pages: 17
ISBN13: 9781609608422|ISBN10: 1609608429|EISBN13: 9781609608439
DOI: 10.4018/978-1-60960-842-2.ch011
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MLA

Liu, Ming-Chi, et al. "Boosting Semantic Relations for Example Population in Concept Learning." Intelligent and Adaptive Learning Systems: Technology Enhanced Support for Learners and Teachers, edited by Sabine Graf, et al., IGI Global, 2012, pp. 165-181. https://doi.org/10.4018/978-1-60960-842-2.ch011

APA

Liu, M., Kinshuk, Huang, Y., & Wen, D. (2012). Boosting Semantic Relations for Example Population in Concept Learning. In S. Graf, F. Lin, Kinshuk, & R. McGreal (Eds.), Intelligent and Adaptive Learning Systems: Technology Enhanced Support for Learners and Teachers (pp. 165-181). IGI Global. https://doi.org/10.4018/978-1-60960-842-2.ch011

Chicago

Liu, Ming-Chi, et al. "Boosting Semantic Relations for Example Population in Concept Learning." In Intelligent and Adaptive Learning Systems: Technology Enhanced Support for Learners and Teachers, edited by Sabine Graf, et al., 165-181. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-60960-842-2.ch011

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Abstract

In this chapter, we discuss the advantages of expansion of learning objects through directed semantic relations (DSRs). We then propose an approach that can automatically construct the DSRs between learning objects and form a DSR graph. This approach addresses the problem to those learners with broad backgrounds. Also, we design two learning activities to show that how this DSR graph can be used to scaffold students’ semantic reasoning. The case study suggests relevant benefit of applying semantic technologies in educational context. This may implicate that this kind of relations can foster systematic thinking and complex domain knowledge inferences by complementing the raw chunks of data with different kinds of meaningful relations (e.g. feeding relation) and semantic associations.

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