Learning About Learning at Scale: Methodological Challenges and Recommendations

Frans van der Sluis, Tim van der Zee, Jasper Ginn

8 Citations (Scopus)

Abstract

Learning at scale opens up a new frontier to learn about learning. Massive Online Open Courses (MOOCs) and similar large-scale online learning platforms give an unprecedented view of learners' behavior whilst learning. In this paper, we argue that the abundance of data that results from such platforms not only brings novel opportunities to the study of learning, but also bears novel methodological challenges. We show that the resulting data comes with various challenges with respect to the granular, observational, and large nature of these data. Additionally, we discuss a series of potential solutions, such as sharing validated models and performing pre-registered confirmatory research. With these contributions, this paper aims to increase awareness and understanding of both the strengths and challenges of research on learning at scale.

Original languageUndefined/Unknown
Title of host publicationProceedings of the Fourth (2017) ACM Conference on Learning @ Scale
Number of pages10
Place of PublicationNew York, NY, USA
PublisherACM
Publication date12 Apr 2017
Pages131-140
ISBN (Print)978-1-4503-4450-0
DOIs
Publication statusPublished - 12 Apr 2017
Externally publishedYes
SeriesL@S '17

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