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http://doi.org/10.25358/openscience-5243
Autoren: | Wahlen, Andreas Kuhn, Christiane Zlatkin-Troitschanskaia, Olga Gold, Christian Zesch, Torsten Horbach, Andrea |
Titel: | Automated scoring of teachers’ pedagogical content knowledge : a comparison between human and machine scoring |
Online-Publikationsdatum: | 21-Okt-2020 |
Erscheinungsdatum: | 2020 |
Sprache des Dokuments: | Englisch |
Zusammenfassung/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-Sachgruppe: | 300 Sozialwissenschaften 300 Social sciences 330 Wirtschaft 330 Economics |
Veröffentlichende Institution: | Johannes Gutenberg-Universität Mainz |
Organisationseinheit: | FB 03 Rechts- und Wirtschaftswissenschaften |
Veröffentlichungsort: | Mainz |
ROR: | https://ror.org/023b0x485 |
DOI: | http://doi.org/10.25358/openscience-5243 |
Version: | Published version |
Publikationstyp: | Zeitschriftenaufsatz |
Nutzungsrechte: | CC BY |
Informationen zu den Nutzungsrechten: | https://creativecommons.org/licenses/by/4.0/ |
Zeitschrift: | Frontiers in education 5 |
Seitenzahl oder Artikelnummer: | Art. 149 |
Verlag: | Frontiers Media |
Verlagsort: | Lausanne |
Erscheinungsdatum: | 2020 |
ISSN: | 2504-284X |
URL der Originalveröffentlichung: | https://doi.org/10.3389/feduc.2020.00149 |
DOI der Originalveröffentlichung: | 10.3389/feduc.2020.00149 |
Enthalten in den Sammlungen: | JGU-Publikationen |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | ||
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wahlen_andreas-automated_scor-20201021123117590.pdf | 886.22 kB | Adobe PDF | Öffnen/Anzeigen |