eTutor: online learning for personalized education

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dc.citation.epage5549en_US
dc.citation.spage5545en_US
dc.contributor.authorTekin, Cemen_US
dc.contributor.authorBraun, J.en_US
dc.contributor.authorSchaar, Mihaela van deren_US
dc.coverage.spatialBrisbane, QLD, Australia
dc.date.accessioned2016-02-08T12:08:33Z
dc.date.available2016-02-08T12:08:33Z
dc.date.issued2015-04en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 19-24 April 2015
dc.descriptionConference name: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
dc.description.abstractGiven recent advances in information technology and artificial intelligence, web-based education systems have became complementary and, in some cases, viable alternatives to traditional classroom teaching. The popularity of these systems stems from their ability to make education available to a large demographics (see MOOCs). However, existing systems do not take advantage of the personalization which becomes possible when web-based education is offered: they continue to be one-size-fits-all. In this paper, we aim to provide a first systematic method for designing a personalized web-based education system. Personalizing education is challenging: (i) students need to be provided personalized teaching and training depending on their contexts (e.g. classes already taken, methods of learning preferred, etc.), (ii) for each specific context, the best teaching and training method (e.g type and order of teaching materials to be shown) must be learned, (iii) teaching and training should be adapted online, based on the scores/feedback (e.g. tests, quizzes, final exam, likes/dislikes etc.) of the students. Our personalized online system, e-Tutor, is able to address these challenges by learning how to adapt the teaching methodology (in this case what sequence of teaching material to present to a student) to maximize her performance in the final exam, while minimizing the time spent by the students to learn the course (and possibly dropouts). We illustrate the efficiency of the proposed method on a real-world eTutor platform which is used for remedial training for a Digital Signal Processing (DSP) course. © 2015 IEEE.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T12:08:33Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2015en
dc.identifier.doi10.1109/ICASSP.2015.7179032en_US
dc.identifier.urihttp://hdl.handle.net/11693/28019
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttps://doi.org/10.1109/ICASSP.2015.7179032en_US
dc.source.titleIEEE International Conference on Acoustics, Speech and Signal Processing. Proceedingsen_US
dc.subjecteLearningen_US
dc.subjectIntelligent tutoring systemsen_US
dc.subjectOnline learningen_US
dc.subjectPersonalized educationen_US
dc.titleeTutor: online learning for personalized educationen_US
dc.typeConference Paperen_US

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