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Natural language processing approaches for student evaluation analysis
Citation
Nguyen, T and Khut, P and Seop Na, In and Yeom, S, Natural language processing approaches for student evaluation analysis, Proceedings of ICONI 2022, 11-13 December 2022, 2, Landing Convention Center, Jeju Shinhwa World, pp. 224-226. ISSN 2093-0542 (2022) [Refereed Conference Paper]
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Abstract
Student evaluation has been critical in all educational institutions and the popularity of student feedback has increased, especially during the COVID-19 pandemic when most colleges and universities switched from traditional face-to-face instruction to an online platform. Student feedback is valued information used to improve and develop learning materials for future generations. However, interpreting student evaluation is a complicated task due to the variety of comment formats and the large volume of data,
which often hides useful and valuable information. Therefore, the application of natural language processing (NLP) in analyzing student surveys has gained popularity among researchers, indicated by the growing number of studies related to the use of this technology in the education domain. One of the benefits of using NLP is the ability to process a large amount of text data in a short amount of time for effective results. This paper presents a review of some NLP technique applications on student evaluation
analysis, focusing on topic modeling and sentiment analysis. It also includes implementation strategies for topic modeling models and sentiment analysis models for processing student feedback. The methods of the research are literature review and technical experiments. This research will reduce the time taken to read student feedback from the University of Tasmania (UTAS) by effectively discovering topics of a large dataset as well as assigning a sentiment score for the feedbacks.
Item Details
| Item Type: | Refereed Conference Paper |
|---|---|
| Keywords: | Natural language procession, Topic modelling, Sentiment analysis |
| Research Division: | Information and Computing Sciences |
| Research Group: | Artificial intelligence |
| Research Field: | Natural language processing |
| Objective Division: | Education and Training |
| Objective Group: | Learner and learning |
| Objective Field: | Higher education |
| UTAS Author: | Nguyen, T (Miss Thi Kim Hue Nguyen) |
| UTAS Author: | Khut, P (Mr Pulsokunreangsy Khut) |
| UTAS Author: | Yeom, S (Dr Soonja Yeom) |
| ID Code: | 155321 |
| Year Published: | 2022 |
| Deposited By: | Information and Communication Technology |
| Deposited On: | 2023-02-11 |
| Last Modified: | 2023-02-13 |
| Downloads: | 0 |
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