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| Main Author: | |
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| Format: | Recurso educativo Open Access |
| Language: | en |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://eric.ed.gov/?id=EJ1470559 |
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| _version_ | 1867180932182573057 |
|---|---|
| author | Mengping Cen |
| author_facet | Mengping Cen Mengping Cen |
| collection | Education Resources Information Center |
| contents | Applying Computational Methods to Analyze Trends and Themes in Library and Information Science Education Mengping Cen Library Education Information Science Education Educational Trends Trend Analysis Multivariate Analysis The rapid advancement of Library and Information Science requires thoroughly examining emerging trends and evolving research priorities. This study seeks to uncover key themes within the field by applying Clustering Analysis and Keyword Frequency techniques. The objective is to analyze interdisciplinary connections and shifts in focus areas such as digital libraries, metadata, and information retrieval. Data were collected from 450 librarians through a structured questionnaire and analyzed using SPSS. The findings reveal distinct clusters centered around digital information management, metadata, and open access, while the rising prominence of concepts like data science indicates changing research interests. These insights highlight potential biases in the Library and Information Science literature and suggest opportunities for future research, particularly in areas such as ethical considerations, user-centered design, and scholarly communication. The study's contributions lie in offering a data-driven approach to trend analysis within Library and Information Science, providing practical implications for guiding future research directions and interdisciplinary collaboration. Its novelty stems from integrating computational methods to explore the complex, evolving landscape of Library and Information Science and offer a roadmap for future scholarly exploration. |
| format | Recurso educativo Open Access |
| id | eric_EJ1470559 |
| institution | ERIC Institute of Education Sciences |
| language | en |
| publishDate | 2025 |
| record_format | eric |
| spellingShingle | Applying Computational Methods to Analyze Trends and Themes in Library and Information Science Education Mengping Cen Library Education Information Science Education Educational Trends Trend Analysis Multivariate Analysis Applying Computational Methods to Analyze Trends and Themes in Library and Information Science Education Mengping Cen Library Education Information Science Education Educational Trends Trend Analysis Multivariate Analysis The rapid advancement of Library and Information Science requires thoroughly examining emerging trends and evolving research priorities. This study seeks to uncover key themes within the field by applying Clustering Analysis and Keyword Frequency techniques. The objective is to analyze interdisciplinary connections and shifts in focus areas such as digital libraries, metadata, and information retrieval. Data were collected from 450 librarians through a structured questionnaire and analyzed using SPSS. The findings reveal distinct clusters centered around digital information management, metadata, and open access, while the rising prominence of concepts like data science indicates changing research interests. These insights highlight potential biases in the Library and Information Science literature and suggest opportunities for future research, particularly in areas such as ethical considerations, user-centered design, and scholarly communication. The study's contributions lie in offering a data-driven approach to trend analysis within Library and Information Science, providing practical implications for guiding future research directions and interdisciplinary collaboration. Its novelty stems from integrating computational methods to explore the complex, evolving landscape of Library and Information Science and offer a roadmap for future scholarly exploration. |
| title | Applying Computational Methods to Analyze Trends and Themes in Library and Information Science Education |
| topic | Library Education Information Science Education Educational Trends Trend Analysis Multivariate Analysis |
| url | https://eric.ed.gov/?id=EJ1470559 |