Automatic Scoring of Student Feedback for Teaching Evaluation Based on Aspect-Level Sentiment Analysis

Autor/inn/en	Ren, Ping; Yang, Liu; Luo, Fang
Titel	Automatic Scoring of Student Feedback for Teaching Evaluation Based on Aspect-Level Sentiment Analysis
Quelle	In: Education and Information Technologies, 28 (2023) 1, S.797-814 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Luo, Fang)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-022-11151-z
Schlagwörter	Student Attitudes; Student Evaluation of Teacher Performance; Feedback (Response); Prediction; Scoring; Automation; Dictionaries + Suchen Sie Ihr Suchwort? Schülerverhalten; Vorhersage; Bewertung; Dictionary; Wörterbuch
Abstract	Student feedback is crucial for evaluating the performance of teachers and the quality of teaching. Free-form text comments obtained from open-ended questions are seldom analyzed comprehensively since it is difficult to interpret and score compared to standardized rating scales. To solve this problem, the present study employed aspect-level sentiment analysis using deep learning and dictionary-based approaches to automatically calculate the emotion orientation of text-based feedback. The results showed that the model using the topic dictionary as input and the attention mechanism had the strongest prediction effect in student review sentiment classification, with a precision rate of 80%, a recall rate of 79% and an F1 value of 79%. The findings identified issues that were not otherwise apparent from analyses of purely quantitative data, providing a deeper and more constructive understanding of curriculum and teaching performance. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01