Which First-Year Students Are Making Most Learning Gains in STEM Subjects?

Autor/inn/en	Rogaten, Jekaterina; Rienties, Bart Carlo
Titel	Which First-Year Students Are Making Most Learning Gains in STEM Subjects?
Quelle	In: Higher Education Pedagogies, 3 (2018) 1, S.161-172 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Rogaten, Jekaterina) ORCID (Rienties, Bart Carlo)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2375-2696
DOI	10.1080/23752696.2018.1484671
Schlagwörter	STEM Education; Data Collection; Data Analysis; Higher Education; Student Characteristics; Racial Differences; Ethnicity; Educational Attainment; Evaluation Methods; Foreign Countries; Undergraduate Students; Socioeconomic Influences; Open Universities; United Kingdom + Suchen Sie Ihr Suchwort? STEM; Data capture; Datensammlung; Auswertung; Hochschulbildung; Hochschulsystem; Hochschulwesen; Rassenunterschied; Ethnizität; Bildungsabschluss; Bildungsgut; Ausland; Sozioökonomischer Faktor; Offene Universität; Großbritannien
Abstract	With the introduction of the Teaching Excellence Framework a lot of attention is focussed on measuring learning gains. A vast body of research has found that individual student characteristics influence academic progression over time. This case-study aims to explore how advanced statistical techniques in combination with Big Data can be used to provide potentially new insights into how students are progressing over time, and in particular how students' socio-demographics (i.e. gender, ethnicity, Social Economic Status, prior educational qualifications) influence students' learning trajectories. Longitudinal academic performance data were sampled from 4222 first-year STEM students across nine modules and analysed using multi-level growth-curve modelling. There were significant differences between white and non-White students, and students with different prior educational qualifications. However, student-level characteristics accounted only for a small portion of variance. The majority of variance was explained by module-level characteristics and assessment level characteristics. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01