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dc.contributor.author
Waters, Andrew E.
dc.contributor.author
Lan, Andrew S.
dc.contributor.author
Studer, Christoph
dc.date.accessioned
2020-12-14T13:34:17Z
dc.date.available
2020-12-08T11:13:27Z
dc.date.available
2020-12-14T13:34:17Z
dc.date.issued
2013
dc.identifier.isbn
978-1-4799-0356-6
en_US
dc.identifier.other
10.1109/ICASSP.2013.6639380
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/455264.1
dc.identifier.uri
http://hdl.handle.net/20.500.11850/455264
dc.identifier.doi
10.3929/ethz-b-000455264
dc.description.abstract
We develop a new model and algorithm for machine learning-based learning analytics, which estimate a learner's knowledge of the concepts underlying a domain. Our model represents the probability that a learner provides the correct response to a question in terms of three factors: their understanding of a set of underlying concepts, the concepts involved in each question, and each question's intrinsic difficulty. We estimate these factors given the graded responses to a set of questions. We develop a bi-convex algorithm to solve the resulting SPARse Factor Analysis (SPARFA) problem. We also incorporate user-defined tags on questions to facilitate the interpretability of the estimated factors. Experiments with synthetic and real-world data demonstrate the efficacy of our approach.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Bi-convex optimization
en_US
dc.subject
Content analytics
en_US
dc.subject
Learning analytics
en_US
dc.subject
Personalized learning
en_US
dc.subject
Factor analysis
en_US
dc.title
Sparse Probit Factor Analysis for Learning Analytics
en_US
dc.type
Conference Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2013-10-21
ethz.book.title
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
en_US
ethz.pages.start
8776
en_US
ethz.pages.end
8780
en_US
ethz.size
5 p.
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.event
38th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)
en_US
ethz.event.location
Vancouver, Canada
en_US
ethz.event.date
May 26-31, 2013
en_US
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02636 - Institut für Integrierte Systeme / Integrated Systems Laboratory::09695 - Studer, Christoph / Studer, Christoph
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02636 - Institut für Integrierte Systeme / Integrated Systems Laboratory::09695 - Studer, Christoph / Studer, Christoph
en_US
ethz.date.deposited
2020-12-08T11:13:38Z
ethz.source
FORM
ethz.eth
no
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-02-15T22:31:01Z
ethz.rosetta.lastUpdated
2021-02-15T22:31:01Z
ethz.rosetta.versionExported
true
ethz.rosetta.versionExported
true
ethz.COinS
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