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dc.contributor.authorAtapattu, T.en
dc.contributor.authorFalkner, K.en
dc.contributor.authorFalkner, N.en
dc.identifier.citationArtificial Intelligence in Education, 17th International Conference, AIED 2015, 2015 / Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M.F. (ed./s), vol.9112, pp.13-22en
dc.descriptionLecture Notes in Artificial Intelligence is a Subseries of Lecture Notes in Computer Science.en
dc.description.abstractQuestion answering (QA) is the automated process of answering general questions submitted by humans in natural language. QA has previously been explored within the educational context to facilitate learning, however the majority of works have focused on text-based answering. As an alternative, this paper proposes an approach to return answers as a concept map, which further encourages meaningful learning and knowledge organisation. Additionally, this paper investigates whether adapting the returned concept map to the specific question context provides further learning benefit. A randomised experiment was conducted with a sample of 59 Computer Science undergraduates, obtaining statistically significant results on learning gain when students are provided with the question-specific concept maps. Further, time spent on studying the concept maps were positively correlated with the learning gain.en
dc.description.statementofresponsibilityThushari Atapattu, Katrina Falkner, and Nickolas Falkneren
dc.relation.ispartofseriesLecture Notes in Artificial Intelligence; 9112en
dc.rights© Springer International Publishing Switzerland 2015en
dc.titleEducational question answering motivated by question-specific concept mapsen
dc.typeConference paperen
dc.contributor.conference17th International Conference on Artificial Intelligence in Education (AIED) (22 Jun 2015 - 26 Jun 2015 : Madrid, Spain)en
pubs.library.collectionComputer Science publicationsen
Appears in Collections:Computer Science publications

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