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 studen...
| Main Authors: | , , , |
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| Format: | Book Chapter |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/50518 |
| _version_ | 1848758490662699008 |
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| author | Kidzinski Giannakos, M. Sampson, Demetrios Dillinbourg, P. |
| author_facet | Kidzinski Giannakos, M. Sampson, Demetrios Dillinbourg, P. |
| author_sort | Kidzinski |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T09:44:49Z |
| format | Book Chapter |
| id | curtin-20.500.11937-50518 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:44:49Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-505182019-09-10T06:25:52Z A tutorial on machine learning in educational science Kidzinski Giannakos, M. Sampson, Demetrios Dillinbourg, P. 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. 2016 Book Chapter http://hdl.handle.net/20.500.11937/50518 10.1007/978-981-287-868-7_54 restricted |
| spellingShingle | Kidzinski Giannakos, M. Sampson, Demetrios Dillinbourg, P. A tutorial on machine learning in educational science |
| title | A tutorial on machine learning in educational science |
| title_full | A tutorial on machine learning in educational science |
| title_fullStr | A tutorial on machine learning in educational science |
| title_full_unstemmed | A tutorial on machine learning in educational science |
| title_short | A tutorial on machine learning in educational science |
| title_sort | tutorial on machine learning in educational science |
| url | http://hdl.handle.net/20.500.11937/50518 |