A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

Shaik, Thanveer ORCID: https://orcid.org/0000-0002-9730-665X and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Li, Yan ORCID: https://orcid.org/0000-0002-4694-4926 and Dann, Christopher ORCID: https://orcid.org/0000-0001-7477-0305 and McDonald, Jacquie and Redmond, Petrea ORCID: https://orcid.org/0000-0001-9674-1206 and Galligan, Linda ORCID: https://orcid.org/0000-0001-8156-8690 (2022) A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis. IEEE Access, 10. pp. 56720-56739.

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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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Education (1 Jul 2019 -)
Date Deposited: 29 May 2022 23:18
Last Modified: 22 Jun 2022 02:56
Uncontrolled Keywords: Artificial intelligence, Education, Feature extraction, Natural language processing, Deep learning, Machine learning, Data models
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460208 Natural language processing
39 EDUCATION > 3901 Curriculum and pedagogy > 390199 Curriculum and pedagogy not elsewhere classified
Socio-Economic Objectives (2020): 16 EDUCATION AND TRAINING > 1601 Learner and learning > 160102 Higher education
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220401 Application software packages
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence
Identification Number or DOI: https://doi.org/10.1109/ACCESS.2022.3177752
URI: http://eprints.usq.edu.au/id/eprint/48640

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