Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/75710
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dc.contributor.authorBystrova, T.en
dc.contributor.authorLarionova, V.en
dc.contributor.authorSinitsyn, E.en
dc.contributor.authorTolmachev, A.en
dc.date.accessioned2019-07-22T06:48:18Z-
dc.date.available2019-07-22T06:48:18Z-
dc.date.issued2018-
dc.identifier.citationLearning analytics in massive open online courses as a tool for predicting learner performance / T. Bystrova, V. Larionova, E. Sinitsyn et al. // Sotsiologicheskoe Obozrenie. — 2018. — Vol. 17. — Iss. 4. — P. 139-166.en
dc.identifier.issn1814-9545-
dc.identifier.otherhttps://vo.hse.ru/data/2018/12/15/1144780437/07 Bystrova.pdfpdf
dc.identifier.other1good_DOI
dc.identifier.othera92a2914-1508-4294-addb-cfe869d8b8f6pure_uuid
dc.identifier.otherhttp://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85057741896m
dc.identifier.urihttp://hdl.handle.net/10995/75710-
dc.description.abstractLearning analytics in MOOCs can be used to predict learner performance, which is critical as higher education is moving towards adaptive learning. Interdisciplinary methods used in the article allow for interpreting empirical qualitative data on performance in specific types of course assignments to predict learner performance and improve the quality of MOOCs. Learning analytics results make it possible to take the most from the data regarding the ways learners engage with information and their level of skills at entry. The article presents the results of applying the proposed learning analytics algorithm to analyze learner performance in specific MOOCs developed by Ural Federal University and offered through the National Open Education Platform. © 2018, National Research University Higher School of Economics.en
dc.description.sponsorshipThis study was support- ed by financial assis- tance provided under the Resolution of the Government of the Rus sian Federation No. 211, Contract No. 02. A03.21.0006. Translated from Russian by I. Zhuchkova.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherNational Research University Higher School of Economicsen
dc.publisherНациональный исследовательский университет "Высшая школа экономики"ru
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.sourceSotsiologicheskoe Obozrenieen
dc.subjectACADEMIC PERFORMANCE MONITORINGen
dc.subjectASSESSMENT TOOLSen
dc.subjectCHECKPOINT ASSIGNMENTSen
dc.subjectEMPIRICAL EVIDENCEen
dc.subjectLEARNING ANALYTICSen
dc.subjectMASSIVE OPEN ONLINE COURSESen
dc.subjectONLINE LEARNINGen
dc.titleLearning analytics in massive open online courses as a tool for predicting learner performanceen
dc.typeArticleen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeinfo:eu-repo/semantics/publishedVersionen
dc.identifier.rsi36566170-
dc.identifier.doi10.17323/1814-9545-2018-4-139-166-
dc.identifier.scopus85057741896-
local.affiliationUral Institute for the Humanities, Ural Federal University named after the first President of Russia B. N. Yeltsin, 19 Mira St, Ekaterinburg, 620002, Russian Federationen
local.affiliationGraduate School of Economics and Management, Ural Federal University named after the first President of Russia B. N. Yeltsin, 19 Mira St, Ekaterinburg, 620002, Russian Federationen
local.contributor.employeeБыстрова Татьяна Юрьевнаru
local.contributor.employeeЛарионова Виола Анатольевнаru
local.contributor.employeeСиницын Евгений Валентиновичru
local.contributor.employeeТолмачев Александр Владимировичru
local.description.firstpage139-
local.description.lastpage166-
local.issue4-
local.volume17-
dc.identifier.wos000456112500009-
local.identifier.pure8423285-
local.identifier.eid2-s2.0-85057741896-
local.identifier.wosWOS:000456112500009-
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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