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Learning Analytics in Massive Open Online Courses as a Tool for Predicting Learner Performance

Author

Listed:
  • Tatiana Bystrova
  • Viola Larionova
  • Evgueny Sinitsyn
  • Alexander Tolmachev

Abstract

Tatiana Bystrova - Doctor of Sciences in Philosophy, Professor at Ural Institute for the Humanities, Ural Federal University named after the first President of Russia B. N. Yeltsin. E-mail: tatiana.bystrova@urfu.ruViola Larionova - Candidate of Sciences in Mathematical Physics, Associate Professor, Deputy Provost, Head of an academic department, Graduate School of Economics and Management, Ural Federal University named after the first President of Russia B. N. Yeltsin. E-mail: v.a.larionova@ urfu.ruEvgueny Sinitsyn - Doctor of Sciences in Mathematical Physics, Professor, Graduate School of Economics and Management, Ural Federal University named after the first President of Russia B. N. Yeltsin. E-mail: e.v.sinitcyn@urfu.ru.Alexander Tolmachev - Senior Lecturer, Graduate School of Economics and Management, Ural Federal University named after the first President of Russia B. N. Yeltsin. E-mail: avtolmachev@urfu.ruLearning 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.

Suggested Citation

  • Tatiana Bystrova & Viola Larionova & Evgueny Sinitsyn & Alexander Tolmachev, 2018. "Learning Analytics in Massive Open Online Courses as a Tool for Predicting Learner Performance," Voprosy obrazovaniya / Educational Studies Moscow, National Research University Higher School of Economics, issue 4, pages 139-166.
    • Handle: RePEc:nos:voprob:2018:i:4:p:139-166
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