Combining Statistical and Machine Learning Methods to Explore German Students' Attitudes towards ICT in PISA

Autor/inn/en	Lezhnina, Olga; Kismihók, Gábor
Titel	Combining Statistical and Machine Learning Methods to Explore German Students' Attitudes towards ICT in PISA
Quelle	In: International Journal of Research & Method in Education, 45 (2022) 2, S.180-199 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Lezhnina, Olga) ORCID (Kismihók, Gábor)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1743-727X
DOI	10.1080/1743727X.2021.1963226
Schlagwörter	Student Attitudes; Scientific Literacy; Numeracy; Foreign Countries; Achievement Tests; International Assessment; Secondary School Students; Information Technology; Prediction; Mathematics Achievement; Science Achievement; Personal Autonomy; Learning Analytics; Likert Scales; Student Characteristics; Programming Languages; Germany; Program for International Student Assessment + Suchen Sie Ihr Suchwort? Schülerverhalten; Rechenkompetenz; Ausland; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Sekundarschüler; Informationstechnologie; Vorhersage; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Individuelle Autonomie; Likert-Skala; Deutschland
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01