NLP Revolution in Education Feedback Analysis: Trends and Challenges

Year : 2023 | Volume :01 | Issue : 01 | Page : 21-25
By

Maina Changeriwal,

Vidhik Sharma,

Suhana Arif,

Lalit Kumar,

Madhur Bharadwaj,

  1. Assistant Professor, Department of Information Technology, Poornima College of Engineering, Jaipur, Rajasthan, India
  2. Student, Department of Information Technology, Poornima College of Engineering, Jaipur, Rajasthan, India
  3. Student, Department of Information Technology, Poornima College of Engineering, Jaipur, Rajasthan, India
  4. Student, Department of Information Technology, Poornima College of Engineering, Jaipur, Rajasthan, India
  5. Student, Department of Information Technology, Poornima College of Engineering, Jaipur, Rajasthan, India

Abstract

Artificial Intelligence (AI) is a rapidly growing area of study in many domains like research and business. Various subsets of AI, such as Machine Learning, Deep Learning, and Natural Language Processing (NLP), are employed to address diverse aspects of data processing and modeling. This review is about the impact of AI on the education system and students feedback for the analysis is required for the enhancement of the technology used in the educational domain. To get this feedback NLP is used to get information about the educational domain. Indeed, understanding domain-specific languages like sarcasm, emoticons, and other forms of nuanced expressions through Natural Language Processing (NLP) has posed significant challenges. These complexities arise due to the non-literal nature of these language elements, making them difficult for traditional NLP techniques to accurately interpret. However, researchers have recognized the importance of capturing these subtleties in educational contexts and have explored various methodologies to overcome these obstacles.

Keywords: Artificial Intelligence, NLP, Deep Learning, Education, Medical Education

[This article belongs to International Journal of Wireless Security and Networks (ijwsn)]

How to cite this article:
Maina Changeriwal, Vidhik Sharma, Suhana Arif, Lalit Kumar, Madhur Bharadwaj. NLP Revolution in Education Feedback Analysis: Trends and Challenges. International Journal of Wireless Security and Networks. 2023; 01(01):21-25.
How to cite this URL:
Maina Changeriwal, Vidhik Sharma, Suhana Arif, Lalit Kumar, Madhur Bharadwaj. NLP Revolution in Education Feedback Analysis: Trends and Challenges. International Journal of Wireless Security and Networks. 2023; 01(01):21-25. Available from: https://journals.stmjournals.com/ijwsn/article=2023/view=116612

References

1. Sonbol R, Rebdawi G, Ghneim N. The use of nlp-based text representation techniques to support requirement engineering tasks: A systematic mapping review. IEEE Access. 2022 Jun 13.
2. Shaik T, Tao X, Li Y, Dann C, McDonald J, Redmond P, Galligan L. A review of the trends and challenges in adopting natural language processing methods for education feedback analysis. IEEE Access. 2022 May 25;10:56720-39.
3. Ofer D, Brandes N, Linial M. The language of proteins: NLP, machine learning & protein sequences. Computational and Structural Biotechnology Journal. 2021 Jan 1;19:1750-8.
4. Ghoumid K, Ar-Reyouchi D, Rattal S, Yahiaoui R, Elmazria O. An accelerated end-to-end probing protocol for narrowband IoT medical devices. IEEE Access. 2021 Feb 22;9:34131-41.
5. Juhn Y, Liu H. Artificial intelligence approaches using natural language processing to advance EHR-based clinical research. Journal of Allergy and Clinical Immunology. 2020 Feb 1;145(2):463-9.
6. Wu S, Roberts K, Datta S, Du J, Ji Z, Si Y, Soni S, Wang Q, Wei Q, Xiang Y, Zhao B. Deep learning in clinical natural language processing: a methodical review. Journal of the American Medical Informatics Association. 2020 Mar;27(3):457-70.
7. Sun H, Liu Z, Wang G, Lian W, Ma J. Intelligent analysis of medical big data based on deep learning. IEEE Access. 2019 Sep 23;7:142022-37.
8. Maadi M, Akbarzadeh Khorshidi H, Aickelin U. A review on human–ai interaction in machine learning and insights for medical applications. International journal of environmental research and public health. 2021 Feb;18(4):2121.
9. Carrasquilla J, Melko RG. Machine learning phases of matter. Nature Physics. 2017 May;13(5):431-4.
10. Barber EL, Garg R, Persenaire C, Simon M. Natural language processing with machine learning to predict outcomes after ovarian cancer surgery. Gynecologic oncology. 2021 Jan 1;160(1):182-6.
11. Le Glaz A, Haralambous Y, Kim-Dufor DH, Lenca P, Billot R, Ryan TC, Marsh J, Devylder J, Walter M, Berrouiguet S, Lemey C. Machine learning and natural language processing in mental health: systematic review. Journal of Medical Internet Research. 2021 May 4;23(5):e15708.
12. Névéol A, Dalianis H, Velupillai S, Savova G, Zweigenbaum P. Clinical natural language processing in languages other than english: opportunities and challenges. Journal of biomedical semantics. 2018 Dec;9(1):1-3.


Regular Issue Subscription Review Article
Volume 01
Issue 01
Received 13/07/2023
Accepted 18/07/2023
Published 24/08/2023