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Authors: Wahlen, Andreas
Kuhn, Christiane
Zlatkin-Troitschanskaia, Olga
Gold, Christian
Zesch, Torsten
Horbach, Andrea
Title: Automated scoring of teachers’ pedagogical content knowledge : a comparison between human and machine scoring
Online publication date: 21-Oct-2020
Language: english
Abstract: To validly assess teachers’ pedagogical content knowledge (PCK), performance-based tasks with open-response formats are required. Automated scoring is considered an appropriate approach to reduce the resource-intensity of human scoring and to achieve more consistent scoring results than human raters. The focus is on the comparability of human and automated scoring of PCK for economics teachers. The answers of (prospective) teachers (N = 852) to six open-response tasks from a standardized and validated test were scored by two trained human raters and the engine “Educational SCoRIng Toolkit” (ESCRITO). The average agreement between human and computer ratings, κw = 0.66, suggests a convergent validity of the scoring results. The results of the single-sector variance analysis show a significant influence of the answers for each homogeneous subgroup (students = 460, trainees = 230, in-service teachers = 162) on the automated scoring. Findings are discussed in terms of implications for the use of automated scoring in educational assessment and its potentials and limitations.
DDC: 300 Sozialwissenschaften
300 Social sciences
330 Wirtschaft
330 Economics
Institution: Johannes Gutenberg-Universität Mainz
Department: FB 03 Rechts- und Wirtschaftswissenschaften
Place: Mainz
Version: Published version
Publication type: Zeitschriftenaufsatz
License: CC BY
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Journal: Frontiers in education
Pages or article number: Art. 149
Publisher: Frontiers Media
Publisher place: Lausanne
Issue date: 2020
ISSN: 2504-284X
Publisher URL:
Publisher DOI: 10.3389/feduc.2020.00149
Appears in collections:JGU-Publikationen

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