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ERIC Number: EJ1462558
Record Type: Journal
Publication Date: 2025-Mar
Pages: 42
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1360-2357
EISSN: EISSN-1573-7608
Available Date: 2024-08-28
Trends in NLP for Personalized Learning: LDA and Sentiment Analysis Insights
Ji Hyun Yu1; Devraj Chauhan1
Education and Information Technologies, v30 n4 p4307-4348 2025
This paper presents a comprehensive analysis of the major themes in Natural Language Processing (NLP) applications for personalized learning, derived from a Latent Dirichlet Allocation (LDA) examination of top educational technology journals from 2014 to 2023. Our methodology involved collecting a corpus of relevant journal articles, applying LDA for thematic extraction, and conducting sentiment analysis on the identified themes. Four predominant themes have been identified: Emotionally Intelligent NLP for Enhanced Writing Education, Interactive Conversational Tutors, Semantic and Sentiment Analysis in Video-based Learning, and Algorithmic Personalization in Massive Open Online Courses (MOOCs). The study highlights the growing importance of emotional intelligence in NLP, the development of AI-powered conversational tutors, and the strategic use of NLP to extract insights from multimedia content. Moreover, the study reveals a uniformly positive sentiment towards NLP's potential in education, despite the challenges and a need for ethical considerations. No significant sentiment variances were found across the four themes, indicating a consensus on NLP's value in diverse educational applications. This research supports the sentiment of ongoing innovation within NLP to enhance personalized learning experiences and suggests a promising future for its empirical validation and application in educational settings.
Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link-springer-com.bibliotheek.ehb.be/
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: 1University of North Texas, Department of Learning Technologies, College of Information, Denton, USA