A tutorial on machine learning in educational science
|dc.identifier.citation||Kidzinski 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.title||A tutorial on machine learning in educational science|
|dcterms.source.title||State-of-the-Art and Future Directions of Smart Learning|
|curtin.department||School of Education|
|curtin.accessStatus||Fulltext not available|
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