Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/99194
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dc.contributor.authorAtapattu Mudiyanselage, T.en
dc.contributor.authorFalkner, K.en
dc.contributor.authorFalkner, N.en
dc.date.issued2015en
dc.identifier.citationCommunications in Computer and Information Science, 2015 / Zvacek, S., Restivo, M., Uhomoibhi, J., Helfert, M. (ed./s), vol.510, pp.68-83en
dc.identifier.isbn9783319257679en
dc.identifier.issn1865-0929en
dc.identifier.urihttp://hdl.handle.net/2440/99194-
dc.descriptionRevised Selected Papers from 6th International Conference, CSEDU 2014 Barcelona, Spain, April 1–3, 2014en
dc.description.abstractConcept maps are effective tools that assist learners in organising and representing knowledge. Recent efforts in the area of concept mapping work toward semi- or fully automated approaches to extract concept maps from various text sources such as text books. The motivation for this research is twofold: novice learners require substantial assistance from experts in constructing their own maps, introducing additional hurdles, and alternatively, the workload required by academics in manually constructing expert maps is substantial and repetitive. A key limitation of an automated concept map generation is the lack of an evaluation framework to measure the quality of concept maps. The most common evaluation mechanism is measuring the overlap between machine-generated elements (e.g. concepts) with expert maps using relevancy measures such as precision and recall. However, in the educational context, the majority of knowledge presented is relevant to the learner, resulting in a large amount of information being retrieved for knowledge organisation. Therefore, this paper introduces a machine-based approach to evaluate the relative importance of knowledge by comparing with human judgment. We introduce three ranking models and conclude that the structural features are positively correlated with human experts (rs ~ 1) for courses with rich content and good structure (well-fitted).en
dc.description.statementofresponsibilityThushari Atapattu, Katrina Falkner, and Nickolas Falkneren
dc.language.isoenen
dc.publisherSpringer International Publishingen
dc.relation.ispartofseriesCommunications in Computer and Information Scienceen
dc.rights© 2015 Springer International Publishing Switzerlanden
dc.subjectConcept map mining; Evaluation methodology; Lecture notesen
dc.titleAn evaluation methodology for concept maps mined from lecture notes: an educational perspectiveen
dc.typeConference paperen
dc.identifier.rmid0030041271en
dc.contributor.conference6th International Conference on Computer Supported Education (CSEDU) (01 Apr 2014 - 03 Apr 2014 : Barcelona, Spain)en
dc.identifier.doi10.1007/978-3-319-25768-6_5en
dc.identifier.pubid227205-
pubs.library.collectionComputer Science publicationsen
pubs.library.teamDS07en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidAtapattu Mudiyanselage, T. [0000-0002-0632-4482]en
dc.identifier.orcidFalkner, K. [0000-0003-0309-4332]en
dc.identifier.orcidFalkner, N. [0000-0001-7892-6813]en
Appears in Collections:Computer Science publications

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