Edinburgh Research Explorer

Replicating 21 findings on student success in online learning

Research output: Contribution to journalArticle

  • Juan Miguel L. Andres
  • Ryan Baker
  • George Siemens
  • Dragan Gasevic
  • Catherine Spann

Related Edinburgh Organisations

Open Access permissions



  • Download as Adobe PDF

    Rights statement: This is the accepted version of the following article: Andres, JML, Baker, R, Siemens, G, Gasevic, D & Spann , C 2017, 'Replicating 21 findings on student success in online learning' Technology, Instruction, Cognition, and Learning. , which has been published in final form at http://www.oldcitypublishing.com/journals/ticl-home/ticl-issue-contents/ticl-volume-10-number-4-2017/ticl-10-4-p-313-333/.

    Accepted author manuscript, 704 KB, PDF-document

Original languageEnglish
Pages (from-to)313-333
JournalTechnology, Instruction, Cognition, and Learning
Issue number4
StatePublished - 2017


There has been a considerable amount of research over the last few years devoted towards studying what factors lead to student success in online courses, whether for-credit or open. However, there has been relatively limited work towards formally studying which findings replicate across courses. In this paper, we present an architecture to facilitate replication of this type of research, which can ingest data from an edX Massively Open Online Course (MOOC) and test whether a range of findings apply, in their original form or slightly modified using an automated search process. We identify 21 findings from previously published studies on completion in MOOCs, render them into production rules within our architecture, and test them in the case of a single MOOC, using a post-hoc method to control for multiple comparisons. We find that nine of these previously published results replicate successfully in the current data set and that contradictory results are found in two cases. This work represents a step towards automated replication of correlational research findings at large scale.

    Research areas

  • massive open online course, online learning, replication study, learning analytics, educational data mining

ID: 30122049