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Natural language processing approaches for student evaluation analysis


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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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

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