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dc.contributor.authorWilliams, Joseph Jay
dc.contributor.authorKim, Juho
dc.contributor.authorRafferty, Anna
dc.contributor.authorMaldonado, Samuel
dc.contributor.authorGajos, Krzysztof Z
dc.contributor.authorLasecki, Walter
dc.contributor.authorHeffernan, Neil
dc.date.accessioned2016-11-17T18:40:31Z
dc.date.issued2016
dc.identifier.citationWilliams, Joseph Jay, Juho Kim, Anna Rafferty, Samuel Maldonado, Krzysztof Z. Gajos, Walter S. Lasecki, and Neil Heffernan. 2016. AXIS: Generating Explanations at Scale with Learnersourcing and Machine Learning. In Proceedings of the Third ACM Conference on Learning @ Scale (L@S '160, Edinburgh, Scotland, April 25-26, 2016: 379-388.en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:29405811
dc.description.abstractWhile explanations may help people learn by providing information about why an answer is correct, many problems on online platforms lack high-quality explanations. This paper presents AXIS (Adaptive eXplanation Improvement System), a system for obtaining explanations. AXIS asks learners to generate, revise, and evaluate explanations as they solve a problem, and then uses machine learning to dynamically determine which explanation to present to a future learner, based on previous learners’ collective input. Results from a case study deployment and a randomized experiment demonstrate that AXIS elicits and identifies explanations that learners find helpful. Providing explanations from AXIS also objectively enhanced learning, when compared to the default practice where learners solved problems and received answers without explanations. The rated quality and learning benefit of AXIS explanations did not differ from explanations generated by an experienced instructor.en_US
dc.description.sponsorshipOther Research Uniten_US
dc.language.isoen_USen_US
dc.relation.hasversionhttp://www.eecs.harvard.edu/~kgajos/papers/2016/williams16axis.pdfen_US
dash.licenseOAP
dc.subjectexplanationen_US
dc.subjectlearning at scaleen_US
dc.subjectcrowdsourcingen_US
dc.subjectlearnersourcingen_US
dc.subjectmachine learningen_US
dc.subjectadaptive learningen_US
dc.titleAXIS: Generating Explanations at Scale with Learnersourcing and Machine Learningen_US
dc.typeConference Paperen_US
dc.description.versionAccepted Manuscripten_US
dc.relation.journalProceedings of the Third (2016) ACM Conference on Learning @ Scaleen_US
dash.depositing.authorGajos, Krzysztof Z
dc.date.available2016-11-17T18:40:31Z
dash.contributor.affiliatedWilliams, Joseph
dash.contributor.affiliatedGajos, Krzysztof


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