Learning About Learning at Scale: Methodological Challenges and Recommendations

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

doi:10.1145/3051457.3051461

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 language	Undefined/Unknown
Title of host publication	Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale
Number of pages	10
Place of Publication	New York, NY, USA
Publisher	ACM
Publication date	12 Apr 2017
Pages	131-140
ISBN (Print)	978-1-4503-4450-0
DOIs	https://doi.org/10.1145/3051457.3051461
Publication status	Published - 12 Apr 2017
Externally published	Yes

Series	L@S '17

Access to Document

10.1145/3051457.3051461

http://doi.acm.org/10.1145/3051457.3051461

Cite this

Learning About Learning at Scale: Methodological Challenges and Recommendations. / van der Sluis, Frans; van der Zee, Tim; Ginn, Jasper.
Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale. New York, NY, USA: ACM, 2017. p. 131-140 (L@S '17).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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