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Trends in NLP for Personalized Learning: LDA and Sentiment Analysis Insights

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Title: Trends in NLP for Personalized Learning: LDA and Sentiment Analysis Insights
Language: English
Authors: Ji Hyun Yu (ORCID 0000-0001-9648-2582), Devraj Chauhan
Source: Education and Information Technologies. 2025 30(4):4307-4348.
Availability: 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/
Peer Reviewed: Y
Page Count: 42
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
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
DOI: 10.1007/s10639-024-12988-2
ISSN: 1360-2357
1573-7608
Abstract: 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.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1462558
Database: ERIC
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  Data: 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/
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  Data: 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.
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