Diagnosing cognitive engagement in TEFL

  • Cognitive engagement is a core dimension of good teaching. In this study, we conceptualize it with the ICAP framework, which distinguishes four levels of engagement identifiable through students' activities. We apply the framework to teaching English as a foreign language (TEFL) using a simulation in which pre- and in-service teachers learn to diagnose engagement levels when planning and implementing lessons. The study aimed to validate the simulation and replicate Roeben et al. (2025) to test the framework's cross-context applicability. Data from N = 118 pre-service teachers at a Bavarian university support the validity of the simulation. Applying signal detection theory further indicates that while ICAP is a useful analytical tool, it requires adaptation for subject-specific contexts like TEFL. To address these limits, we recommend incorporating the dual structure of foreign language learning and extending the framework with typical examples.

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Metadaten
Author:Meral Roeben, Christiane Lütge, Florian Schultz-Pernice, Johanna Vejvoda, Michael SailerORCiDGND, Frank Fischer, Nicole Heitzmann
URN:urn:nbn:de:bvb:384-opus4-1268162
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/126816
ISSN:0939-7299OPAC
Parent Title (German):Zeitschrift für Fremdsprachenforschung
Publisher:wbv Publikation
Place of publication:Bielefeld
Type:Article
Language:English
Year of first Publication:2025
Publishing Institution:Universität Augsburg
Release Date:2025/12/08
Volume:36
Issue:2
First Page:27
Last Page:48
DOI:https://doi.org/10.3278/zff2502w002
Institutes:Philosophisch-Sozialwissenschaftliche Fakultät
Philosophisch-Sozialwissenschaftliche Fakultät / Empirische Bildungsforschung
Philosophisch-Sozialwissenschaftliche Fakultät / Empirische Bildungsforschung / Lehrstuhl für Learning Analytics and Educational Data Mining
Dewey Decimal Classification:4 Sprache / 40 Sprache / 400 Sprache
Licence (German):CC-BY-SA 4.0: Creative Commons: Namensnennung - Weitergabe unter gleichen Bedingungen