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conference paper

A Tutorial on Machine Learning in Educational Science

Kidzinski, Lukasz
•
Giannakos, Michail
•
Sampson, Demetrios G.
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Li, Y
•
Chang, M
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2016
State-Of-The-Art And Future Directions Of Smart Learning
2nd International Conference on Smart Learning Environments (ICSLE)

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.

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Type
conference paper
DOI
10.1007/978-981-287-868-7_54
Web of Science ID

WOS:000389703400054

Author(s)
Kidzinski, Lukasz
Giannakos, Michail
Sampson, Demetrios G.
Dillenbourg, Pierre  
Editors
Li, Y
•
Chang, M
•
Kravcik, M
•
Popescu, E
•
Huang, R
•
Kinshuk, K
•
Chen, Ns
Date Issued

2016

Publisher

Springer-Verlag Singapore Pte Ltd

Publisher place

Singapore

Published in
State-Of-The-Art And Future Directions Of Smart Learning
ISBN of the book

978-981-287-868-7

978-981-287-866-3

Total of pages

7

Series title/Series vol.

Lecture Notes in Educational Technology

Start page

453

End page

459

Subjects

MOOCs

•

Educational data mining

•

Learning analytics

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
CEDE  
Event nameEvent placeEvent date
2nd International Conference on Smart Learning Environments (ICSLE)

Univ Craiova, Sinaia, ROMANIA

SEP 23-25, 2015

Available on Infoscience
January 24, 2017
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/133317
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