Improving Problem Detection in Peer Assessment through Pseudo-Labeling Using Semi-Supervised Learning [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (15th, Durham, United Kingdom, Jul 24-27, 2022).

Autor/inn/en	Liu, Chengyuan; Cui, Jialin; Shang, Ruixuan; Xiao, Yunkai; Jia, Qinjin; Gehringer, Edward
Titel	Improving Problem Detection in Peer Assessment through Pseudo-Labeling Using Semi-Supervised Learning [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (15th, Durham, United Kingdom, Jul 24-27, 2022).
Quelle	(2022), (7 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Peer Evaluation; Feedback (Response); Computer Mediated Communication; Teaching Methods; Artificial Intelligence; Learning Strategies; Identification; Error Patterns; Data Analysis; Form Classes (Languages); Natural Language Processing; Scores; Undergraduate Students; Accuracy; Evaluation Methods; Computer Software + Suchen Sie Ihr Suchwort? Computerkonferenz; Teaching method; Lehrmethode; Unterrichtsmethode; Künstliche Intelligenz; Learning methode; Learning techniques; Lernmethode; Lernstrategie; Identifikation; Identifizierung; Fehlertyp; Auswertung; Analytischer Sprachbau; Natürliche Sprache
Abstract	An online peer-assessment system typically allows students to give textual feedback to their peers, with the goal of helping the peers improve their work. The amount of help that students receive is highly dependent on the quality of the reviews. Previous studies have investigated using machine learning to detect characteristics of reviews (e.g., Do they mention a problem, make a suggestion, or tell the student where to make a change?). Machine-learning approaches to peer-assessment evaluation are heavily reliant on labeled data to learn how to identify review characteristics. However, attaining reliable labels for those characteristics is always time-consuming and labor-intensive. In this study, we propose to apply pseudo-labeling, a semi-supervised learning-based strategy, to improve the recognition of reviews that detect problems in the reviewed work. This is done by utilizing a small, reliably labeled dataset along with a large unlabeled dataset to train a text classifier. The ultimate goal of this research is to show that for peer assessment evaluation, we can utilize both unlabeled and labeled datasets to obtain a robust auto-labeling system and thereby save much effort in labeling the data. [For the full proceedings, see ED623995.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01