Does a Deep Learning Inventory Predict Knowledge Transfer? Linking Student Perceptions to Transfer Outcomes

Autor/inn/en	LoGiudice, Andrew B.; Norman, Geoffrey R.; Manzoor, Saba; Monteiro, Sandra
Titel	Does a Deep Learning Inventory Predict Knowledge Transfer? Linking Student Perceptions to Transfer Outcomes
Quelle	In: Advances in Health Sciences Education, 28 (2023) 1, S.47-63 (17 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (LoGiudice, Andrew B.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1382-4996
DOI	10.1007/s10459-022-10141-7
Schlagwörter	Learning Processes; Transfer of Training; Likert Scales; Generalization; Predictive Validity; Outcome Measures; Undergraduate Students; Health Sciences; Medical Education; Student Attitudes; Reliability; Questionnaires; Test Items; Outcomes of Education + Suchen Sie Ihr Suchwort? Learning process; Lernprozess; Training; Transfer; Ausbildung; Likert-Skala; Medizinische Ausbildung; Schülerverhalten; Reliabilität; Fragebogen; Test content; Testaufgabe; Lernleistung; Schulerfolg
Abstract	Students are often encouraged to learn 'deeply' by abstracting generalizable principles from course content rather than memorizing details. So widespread is this perspective that Likert-style inventories are now routinely administered to students to quantify how much a given course or curriculum evokes deep learning. The predictive validity of these inventories, however, has been criticized based on sparse empirical support and ambiguity in what specific outcome measures indicate whether deep learning has occurred. Here we further tested the predictive validity of a prevalent deep learning inventory, the Revised Two-Factor Study Process Questionnaire, by selectively analyzing outcome measures that reflect a major goal of medical education--i.e., knowledge transfer. Students from two undergraduate health sciences courses completed the deep learning inventory before their course's final exam. Shortly after, a random subset of students rated how much each final exam item aligned with three task demands associated with transfer: (1) application of general principles, (2) integration of multiple ideas or examples, and (3) contextual novelty. We then used these ratings from students to examine performance on a subset of exam items that were collectively perceived to demand transfer. Despite good reliability, the resulting transfer outcomes were not substantively predicted by the deep learning inventory. These findings challenge the validity of this tool and others like it. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01