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Title: | Combining statistical and machine learning methods to explore German students’ attitudes towards ICT in PISA |
Authors: | Lezhnina, Olga; Kismihók, Gábor |
Publishers version: | https://doi.org/10.1080/1743727X.2021.1963226 |
URI: | https://oa.tib.eu/renate/handle/123456789/10110 http://dx.doi.org/10.34657/9148 |
Issue Date: | 2021 |
Published in: | International Journal of Research & Method in Education 45 (2022), Nr. 2 |
Journal: | International Journal of Research & Method in Education |
Volume: | 45 |
Issue: | 2 |
Page Start: | 180 |
Page End: | 199 |
Publisher: | London : Taylor & Francis |
Abstract: | In our age of big data and growing computational power, versatility in data analysis is important. This study presents a flexible way to combine statistics and machine learning for data analysis of a large-scale educational survey. The authors used statistical and machine learning methods to explore German students’ attitudes towards information and communication technology (ICT) in relation to mathematical and scientific literacy measured by the Programme for International Student Assessment (PISA) in 2015 and 2018. Implementations of the random forest (RF) algorithm were applied to impute missing data and to predict students’ proficiency levels in mathematics and science. Hierarchical linear models (HLM) were built to explore relationships between attitudes towards ICT and mathematical and scientific literacy with the focus on the nested structure of the data. ICT autonomy was an important variable in RF models, and associations between this attitude and literacy scores in HLM were significant and positive, while for other ICT attitudes the associations were negative (ICT in social interaction) or non-significant (ICT competence and ICT interest). The need for further research on ICT autonomy is discussed, and benefits of combining statistical and machine learning approaches are outlined. |
Keywords: | attitudes towards ICT; Combining statistics and machine learning; ICT autonomy; multilevel modeling; random forest |
Type: | article; Text |
Publishing status: | publishedVersion |
DDC: | 370 |
License: | CC BY-NC-ND 4.0 Unported |
Link to license: | https://creativecommons.org/licenses/by-nc-nd/4.0/ |
Appears in Collections: | Informationswissenschaften |
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Lezhnina, Olga and Gábor Kismihók, 2021. Combining statistical and machine learning methods to explore German students’ attitudes towards ICT in PISA. 2021. London : Taylor & Francis
Lezhnina, O. and Kismihók, G. (2021) “Combining statistical and machine learning methods to explore German students’ attitudes towards ICT in PISA.” London : Taylor & Francis. doi: https://doi.org/10.1080/1743727X.2021.1963226.
Lezhnina O, Kismihók G. Combining statistical and machine learning methods to explore German students’ attitudes towards ICT in PISA. Vol. 45. London : Taylor & Francis; 2021.
Lezhnina, O., & Kismihók, G. (2021). Combining statistical and machine learning methods to explore German students’ attitudes towards ICT in PISA (Version publishedVersion, Vol. 45). Version publishedVersion, Vol. 45. London : Taylor & Francis. https://doi.org/https://doi.org/10.1080/1743727X.2021.1963226
Lezhnina O, Kismihók G. Combining statistical and machine learning methods to explore German students’ attitudes towards ICT in PISA. 2021;45(2). doi:https://doi.org/10.1080/1743727X.2021.1963226
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