Show simple item record

dc.contributor.authorGiannakos, M.
dc.contributor.authorSampson, Demetrios
dc.contributor.authorDillinbourg, P.
dc.identifier.citationKidzinski and Giannakos, M. and Sampson, D. and Dillinbourg, P. 2016. A tutorial on machine learning in educational science, in Li, Y. et al (eds), State-of-the-Art and Future Directions of Smart Learning, pp. 453-459. Singapore: Springer.

Popularity of massive online open courses (MOOCs) allowed educational researchers to address problems which were not accessible few years ago. Although classical statistical techniques still apply, large datasets allow us to discover deeper patterns and to provide more accurate predictions of student’s behaviors and outcomes. The goal of this tutorial was to disseminate knowledge on elementary data analysis tools as well as facilitate simple practical data analysis activities with the purpose of stimulating reflection on the great potential of large datasets. In particular, during the tutorial we introduce elementary tools for using machine learning models in education. Although the methodology presented here applies in any programming environment, we choose R and CARET package due to simplicity and access to the most recent machine learning methods.

dc.titleA tutorial on machine learning in educational science
dc.typeBook Chapter
dcterms.source.titleState-of-the-Art and Future Directions of Smart Learning
curtin.departmentSchool of Education
curtin.accessStatusFulltext not available

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record