Details
| Titel in Übersetzung | Multi-level Annotation of Argumentative Learner Texts for Automatic Text Evaluation |
|---|---|
| Originalsprache | Deutsch |
| Seiten (von - bis) | 102–129 |
| Fachzeitschrift | Zeitschrift fur Angewandte Linguistik |
| Jahrgang | 2025 |
| Ausgabenummer | 82 |
| Publikationsstatus | Veröffentlicht - 13 März 2025 |
Abstract
This article presents a multi-level annotation approach for argumentative learner texts that was developed as part of an interdisciplinary DFG project. The project aims at the automated generation of individualized, development-promoting and learner-sensitive feedback on argumentative student texts and is situated in the field of AI-supported text production. To generate automated feedback, the first step was to manually annotate an extensive text corpus consisting of 1,320 argumentative texts written by fifth and ninth graders. This then formed the basis for the development of corresponding computational linguistic procedures. The article focuses on the special features as well as the challenges that arose in connection with the annotation of learner texts and the generation of learner-sensitive feedback. The article is structured as follows: First, the relevant computational linguistics and language didactics research findings and digital support systems for argumentative writing are outlined. In the main part, the procedure of multi-level annotation is explained in detail. Due to the methodological approach, above-average inter-annotator agreement was achieved resulting in the multi-level approach implemented being adaptable for further corpus-based studies. Finally, the results are interpreted and discussed.
Schlagwörter
- argumentative learner texts, automated learner-sensitive feedback, corpus-based analysis, multi-level annotation, Natural Language Processing
ASJC Scopus Sachgebiete
- Geisteswissenschaftliche Fächer (insg.)
- Sprache und Linguistik
- Sozialwissenschaften (insg.)
- Linguistik und Sprache
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in: Zeitschrift fur Angewandte Linguistik, Jahrgang 2025, Nr. 82, 13.03.2025, S. 102–129.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Mehrebenenannotation argumentativer Lerner∗innentexte für die automatische Textauswertung
AU - Kilsbach, Sebastian
AU - Rezat, Sara
AU - Michel, Nadine
AU - Karabey, Rabia
AU - Stahl, Maja
AU - Wachsmuth, Henning
N1 - Publisher Copyright: © 2025 Gesellschaft für Angewandte Linguistik, published by De Gruyter 2025.
PY - 2025/3/13
Y1 - 2025/3/13
N2 - This article presents a multi-level annotation approach for argumentative learner texts that was developed as part of an interdisciplinary DFG project. The project aims at the automated generation of individualized, development-promoting and learner-sensitive feedback on argumentative student texts and is situated in the field of AI-supported text production. To generate automated feedback, the first step was to manually annotate an extensive text corpus consisting of 1,320 argumentative texts written by fifth and ninth graders. This then formed the basis for the development of corresponding computational linguistic procedures. The article focuses on the special features as well as the challenges that arose in connection with the annotation of learner texts and the generation of learner-sensitive feedback. The article is structured as follows: First, the relevant computational linguistics and language didactics research findings and digital support systems for argumentative writing are outlined. In the main part, the procedure of multi-level annotation is explained in detail. Due to the methodological approach, above-average inter-annotator agreement was achieved resulting in the multi-level approach implemented being adaptable for further corpus-based studies. Finally, the results are interpreted and discussed.
AB - This article presents a multi-level annotation approach for argumentative learner texts that was developed as part of an interdisciplinary DFG project. The project aims at the automated generation of individualized, development-promoting and learner-sensitive feedback on argumentative student texts and is situated in the field of AI-supported text production. To generate automated feedback, the first step was to manually annotate an extensive text corpus consisting of 1,320 argumentative texts written by fifth and ninth graders. This then formed the basis for the development of corresponding computational linguistic procedures. The article focuses on the special features as well as the challenges that arose in connection with the annotation of learner texts and the generation of learner-sensitive feedback. The article is structured as follows: First, the relevant computational linguistics and language didactics research findings and digital support systems for argumentative writing are outlined. In the main part, the procedure of multi-level annotation is explained in detail. Due to the methodological approach, above-average inter-annotator agreement was achieved resulting in the multi-level approach implemented being adaptable for further corpus-based studies. Finally, the results are interpreted and discussed.
KW - argumentative learner texts
KW - automated learner-sensitive feedback
KW - corpus-based analysis
KW - multi-level annotation
KW - Natural Language Processing
UR - http://www.scopus.com/inward/record.url?scp=85219569540&partnerID=8YFLogxK
U2 - 10.1515/zfal-2025-2003
DO - 10.1515/zfal-2025-2003
M3 - Artikel
AN - SCOPUS:85219569540
VL - 2025
SP - 102
EP - 129
JO - Zeitschrift fur Angewandte Linguistik
JF - Zeitschrift fur Angewandte Linguistik
SN - 1433-9889
IS - 82
ER -