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 |
| 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 |
| 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. |
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| ISSN: | 1360-2357 1573-7608 |
| DOI: | 10.1007/s10639-024-12988-2 |
