A Tutorial on Machine Learning in Educational Science

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.


Editor(s):
Li, Y
Chang, M
Kravcik, M
Popescu, E
Huang, R
Kinshuk, K
Chen, Ns
Published in:
State-Of-The-Art And Future Directions Of Smart Learning, 453-459
Presented at:
2nd International Conference on Smart Learning Environments (ICSLE), Univ Craiova, Sinaia, ROMANIA, SEP 23-25, 2015
Year:
2016
Publisher:
Singapore, Springer-Verlag Singapore Pte Ltd
ISSN:
2196-4963
ISBN:
978-981-287-868-7
978-981-287-866-3
Keywords:
Laboratories:




 Record created 2017-01-24, last modified 2018-09-13


Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)