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.
Descriptors: Natural Language Processing, Individualized Instruction, Educational Technology, Emotional Intelligence, Writing Instruction, Interaction, Intelligent Tutoring Systems, MOOCs, Positive Attitudes, Technology Uses in Education, Barriers, Ethics, Artificial Intelligence, Instructional Innovation
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