A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis
Article
| Article Title | A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis |
|---|---|
| ERA Journal ID | 210567 |
| Article Category | Article |
| Authors | Shaik, Thanveer (Author), Tao, Xiaohui (Author), Li, Yan (Author), Dann, Christopher (Author), McDonald, Jacquie (Author), Redmond, Petrea (Author) and Galligan, Linda (Author) |
| Journal Title | IEEE Access |
| Journal Citation | 10, pp. 56720-56739 |
| Number of Pages | 20 |
| Year | 2022 |
| Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
| Place of Publication | United States |
| ISSN | 2169-3536 |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2022.3177752 |
| Web Address (URL) | https://ieeexplore.ieee.org/document/9781308 |
| Abstract | Artificial Intelligence (AI) is a fast-growing area of study that stretching its presence to many business and research domains. Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of data processing and modelling. This review article presents an overview of AI’s impact on education outlining with current opportunities. In the education domain, student feedback data is crucial to uncover the merits and demerits of existing services provided to students. AI can assist in identifying the areas of improvement in educational infrastructure, learning management systems, teaching practices and study environment. NLP techniques play a vital role in analyzing student feedback in textual format. This research focuses on existing NLP methodologies and applications that could be adapted to educational domain applications like sentiment annotations, entity annotations, text summarization, and topic modelling. Trends and challenges in adopting NLP in education were reviewed and explored. Context-based challenges in NLP like sarcasm, domain-specific language, ambiguity, and aspect-based sentiment analysis are explained with existing methodologies to overcome them. Research community approaches to extract the semantic meaning of emoticons and special characters in feedback which conveys user opinion and challenges in adopting NLP in education are explored. |
| Keywords | Artificial intelligence; Education; Feature extraction; Natural language processing; Deep learning; Machine learning; Data models; Artificial Intelligence; natural language processing; education; deep learning |
| ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
| 460208. Natural language processing | |
| 390199. Curriculum and pedagogy not elsewhere classified | |
| Byline Affiliations | School of Mathematics, Physics and Computing |
| School of Education | |
| Academic Development | |
| Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q75v6/a-review-of-the-trends-and-challenges-in-adopting-natural-language-processing-methods-for-education-feedback-analysis
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| A_Review_of_the_Trends_and_Challenges_in_Adopting_Natural_Language_Processing_Methods_for_Education_Feedback_Analysis.pdf | ||
| License: CC BY 4.0 | ||
| File access level: Anyone | ||
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