Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/90747
Title: Learning analytics in massive open online courses as a tool for predicting learner performance
Authors: Bystrova, T.
Larionova, V.
Sinitsyn, E.
Tolmachev, A.
Issue Date: 2018
Publisher: National Research University Higher School of Economics
Citation: Learning analytics in massive open online courses as a tool for predicting learner performance / T. Bystrova, V. Larionova, E. Sinitsyn, A. Tolmachev. — DOI 10.17323/1814-9545-2018-4-139-166 // Voprosy Obrazovaniya. — 2018. — Vol. 4. — Iss. 2018. — .
Abstract: Learning 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.
Keywords: ACADEMIC PERFORMANCE MONITORING
ASSESSMENT TOOLS
CHECKPOINT ASSIGNMENTS
EMPIRICAL EVIDENCE
LEARNING ANALYTICS
MASSIVE OPEN ONLINE COURSES
ONLINE LEARNING
URI: http://hdl.handle.net/10995/90747
https://elar.urfu.ru/handle/10995/90747
Access: info:eu-repo/semantics/openAccess
RSCI ID: 36566170
SCOPUS ID: 85057741896
WOS ID: 000456112500009
PURE ID: 8423285
ISSN: 1814-9545
DOI: 10.17323/1814-9545-2018-4-139-166
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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