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Autor/inn/en | Ren, Ping; Yang, Liu; Luo, Fang |
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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
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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 |
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 |