Assessing Students' Approaches to Modelling in Undergraduate Biology

Autor/inn/en	Bennett, Steve; Gotwals, Amelia Wenk; Long, Tammy M.
Titel	Assessing Students' Approaches to Modelling in Undergraduate Biology
Quelle	In: International Journal of Science Education, 42 (2020) 10, S.1697-1714 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Bennett, Steve) ORCID (Gotwals, Amelia Wenk) ORCID (Long, Tammy M.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0950-0693
DOI	10.1080/09500693.2020.1777343
Schlagwörter	Undergraduate Students; Biology; Metacognition; Thinking Skills; Protocol Analysis; Academic Achievement; Teaching Methods; Models; Predictor Variables; Logical Thinking; Cues + Suchen Sie Ihr Suchwort? Biologie; Meta cognitive ability; Meta-cognition; Metakognitive Fähigkeit; Metakognition; Denkfähigkeit; Schulleistung; Teaching method; Lehrmethode; Unterrichtsmethode; Analogiemodell; Prädiktor; Stichwort
Abstract	In this study, we propose an 'Approach to Modeling' (AtM) framework for examining how undergraduates approach tasks that require modelling scientific phenomena. Our framework is adapted from Approach to Learning (AtL) theories and consists of three observable behavioural constructs: metacognition, generative thinking, and causal reasoning. Twenty students participated in two think-aloud interviews, one year apart, in which they engaged in a total of five modelling tasks. We used AtM to analyse and score students' modelling behaviours. We concluded that: (1) students' AtM is associated with, but cannot be predicted by their previous academic achievement, and (2) students' AtM is not an inherent characteristic of the individual, but differs as modelling tasks change. We discuss the influence of prompt construction on AtM; specifically the presence of cueing words, the type and amount of background information provided, and familiarity with the contexts and concepts addressed in the prompts. In addition, we discuss connections between AtM and broader notions of science learning. We conclude with recommendations for constructing modelling prompts that may encourage diverse learners to engage more deeply in modelling tasks. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01