Computer-Based Classification of Preservice Physics Teachers' Written Reflections

Autor/inn/en	Wulff, Peter; Buschhüter, David; Westphal, Andrea; Nowak, Anna; Becker, Lisa; Robalino, Hugo; Stede, Manfred; Borowski, Andreas
Titel	Computer-Based Classification of Preservice Physics Teachers' Written Reflections
Quelle	In: Journal of Science Education and Technology, 30 (2021) 1, S.1-15 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Wulff, Peter)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1059-0145
DOI	10.1007/s10956-020-09865-1
Schlagwörter	Preservice Teachers; Physics; Reflection; Preservice Teacher Education; Feedback (Response); Technology Uses in Education; Classification; Foreign Countries; Writing (Composition); Germany + Suchen Sie Ihr Suchwort? Physik; Lehramtsstudiengang; Lehrerausbildung; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Classification system; Klassifikation; Klassifikationssystem; Ausland; Schreibübung; Deutschland
Abstract	Reflecting in written form on one's teaching enactments has been considered a facilitator for teachers' professional growth in university-based preservice teacher education. Writing a structured reflection can be facilitated through external feedback. However, researchers noted that feedback in preservice teacher education often relies on holistic, rather than more content-based, analytic feedback because educators oftentimes lack resources (e.g., time) to provide more analytic feedback. To overcome this impediment to feedback for written reflection, advances in computer technology can be of use. Hence, this study sought to utilize techniques of natural language processing and machine learning to train a computer-based classifier that classifies preservice physics teachers' written reflections on their teaching enactments in a German university teacher education program. To do so, a reflection model was adapted to physics education. It was then tested to what extent the computer-based classifier could accurately classify the elements of the reflection model in segments of preservice physics teachers' written reflections. Multinomial logistic regression using word count as a predictor was found to yield acceptable average human-computer agreement (F1-score on held-out test dataset of 0.56) so that it might fuel further development towards an automated feedback tool that supplements existing holistic feedback for written reflections with data-based, analytic feedback. (As Provided).
Anmerkungen	Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01