Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 180-199 |
| Seitenumfang | 20 |
| Fachzeitschrift | International Journal of Research and Method in Education |
| Jahrgang | 45 |
| Ausgabenummer | 2 |
| Frühes Online-Datum | 5 Aug. 2021 |
| Publikationsstatus | Veröffentlicht - Apr. 2022 |
| Extern publiziert | Ja |
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.
ASJC Scopus Sachgebiete
- Sozialwissenschaften (insg.)
- Ausbildung bzw. Denomination
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
in: International Journal of Research and Method in Education, Jahrgang 45, Nr. 2, 04.2022, S. 180-199.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Combining statistical and machine learning methods to explore German students’ attitudes towards ICT in PISA
AU - Lezhnina, Olga
AU - Kismihók, Gábor
N1 - Funding Information: We are grateful to Manuel Prinz for his insightful comments on the first draft, to Junaid Ghauri for thought-provoking discussions, and to anonymous reviewers for their criticism and constructive suggestions that helped us substantially improve the manuscript.
PY - 2022/4
Y1 - 2022/4
N2 - 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.
AB - 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.
KW - attitudes towards ICT
KW - Combining statistics and machine learning
KW - ICT autonomy
KW - multilevel modeling
KW - random forest
UR - http://www.scopus.com/inward/record.url?scp=85112595787&partnerID=8YFLogxK
U2 - 10.1080/1743727X.2021.1963226
DO - 10.1080/1743727X.2021.1963226
M3 - Article
AN - SCOPUS:85112595787
VL - 45
SP - 180
EP - 199
JO - International Journal of Research and Method in Education
JF - International Journal of Research and Method in Education
SN - 1743-727X
IS - 2
ER -