A Machine Learning Approach to Investigating the Effects of Mathematics Dispositions on Mathematical Literacy

Autor/inn/en	Gabriel, Florence; Signolet, Jason; Westwell, Martin
Titel	A Machine Learning Approach to Investigating the Effects of Mathematics Dispositions on Mathematical Literacy
Quelle	In: International Journal of Research & Method in Education, 41 (2018) 3, S.306-327 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1743-727X
DOI	10.1080/1743727X.2017.1301916
Schlagwörter	Artificial Intelligence; Mathematics Instruction; Numeracy; Models; Achievement Tests; Foreign Countries; International Assessment; Secondary School Students; Mathematics Anxiety; Data; Decision Making; Regression (Statistics); Teaching Methods; Self Efficacy; Self Concept; Predictor Variables; Correlation; Psychological Characteristics; Demography; Statistical Analysis; Australia; Program for International Student Assessment + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Mathematics lessons; Mathematikunterricht; Rechenkompetenz; Analogiemodell; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Ausland; Sekundarschüler; Daten; Decision-making; Entscheidungsfindung; Regression; Regressionsanalyse; Teaching method; Lehrmethode; Unterrichtsmethode; Self-efficacy; Selbstwirksamkeit; Selbstkonzept; Prädiktor; Korrelation; Demografie; Statistische Analyse; Australien
Abstract	Mathematics competency is fast becoming an essential requirement in ever greater parts of day-to-day work and life. Thus, creating strategies for improving mathematics learning in students is a major goal of education research. However, doing so requires an ability to look at many aspects of mathematics learning, such as demographics and psychological dispositions, in an integrated way as part of the same system. Large-scale assessments such as the Programme for International Student Assessment (PISA) provide an accessible and large volume of coherent data, and this gives researchers the opportunity to employ data-driven approaches to gain an overview of the system. For these reasons, we have used machine learning to explore the relationships between psychological dispositions and mathematical literacy in Australian 15-year-olds using the PISA 2012 data set. Our results from this strongly data-driven approach re-affirm the primacy of mathematics self-efficacy and highlight novel complex interactions between mathematics self-efficacy, mathematics anxiety and socio-economic status. In this paper, we demonstrate how education researchers can usefully employ data-driven modelling techniques to find complex non-linear relationships and novel interactions in a multidimensional data set. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01