Bibliometric Cluster Analysis and Classification Algorithm for Questioning Effectiveness in Elementary School Classrooms
Keywords:
Bibliometric cluster, Classification, Elementary School, Centroid Clustering, ClassroomsAbstract
Bibliometric cluster analysis and classification algorithms offer a systematic approach to evaluating the effectiveness of questioning techniques in elementary school classrooms. By analyzing a vast array of academic literature and educational resources, bibliometric methods identify key themes, trends, and patterns related to questioning effectiveness. This analysis enables the classification of questioning techniques based on their impact on student engagement, comprehension, and critical thinking skills. By leveraging advanced algorithms, educators can gain valuable insights into the most effective questioning strategies for enhancing learning outcomes in elementary classrooms. This paper presented a novel approach to assessing the effectiveness of questioning techniques in elementary school classrooms through Bibliometric Cluster Analysis and a Classification Algorithm, integrating Centroid Clustering Deep Learning (CCDL). By analyzing a diverse range of scholarly literature and educational resources, the bibliometric analysis identifies key themes, trends, and patterns related to questioning effectiveness. Subsequently, CCDL is employed to cluster and classify questioning techniques based on their impact on student engagement, comprehension, and critical thinking skills. Through this integrated approach, educators gain valuable insights into the most effective questioning strategies for enhancing learning outcomes in elementary classrooms. the algorithm identifies three primary clusters of questioning techniques: low-engagement (average student participation rate below 30%), moderate-engagement (30-60% participation rate), and high-engagement (above 60% participation rate).
Downloads
References
Pua, S., Ahmad, N. A., & Khambari, M. N. M. (2021). Identification and Analysis of Core Topics in Educational Artificial Intelligence Research: A Bibliometric Analysis. Cypriot Journal of Educational Sciences, 16(3), 995-1009.
Chen, X., Zou, D., & Xie, H. (2022). A decade of learning analytics: Structural topic modeling based bibliometric analysis. Education and Information Technologies, 27(8), 10517-10561.
Hwang, G. J., & Tu, Y. F. (2021). Roles and research trends of artificial intelligence in mathematics education: A bibliometric mapping analysis and systematic review. Mathematics, 9(6), 584.
Baek, C., & Doleck, T. (2022). Educational data mining: A bibliometric analysis of an emerging field. IEEE Access, 10, 31289-31296.
Behl, A., Jayawardena, N., Pereira, V., Islam, N., Del Giudice, M., & Choudrie, J. (2022). Gamification and e-learning for young learners: A systematic literature review, bibliometric analysis, and future research agenda. Technological Forecasting and Social Change, 176, 121445.
Maphosa, V., & Maphosa, M. (2023). Artificial intelligence in higher education: a bibliometric analysis and topic modeling approach. Applied Artificial Intelligence, 37(1), 2261730.
Liu, T. (2022). Knowledge tracing: A bibliometric analysis. Computers and Education: Artificial Intelligence, 100090.
Kaban, A. (2023). Artificial intelligence in education: A science mapping approach. International Journal of Education in Mathematics, Science and Technology, 11(4), 844-861.
Cai, J., Backer, F. D., Vandermeersche, G., & Lombaerts, K. (2023). Comparing Chinese and Western classroom learning environment research: a bibliometric analysis and visualization. Frontiers in Psychology, 14, 1213397.
Cuéllar-Rojas, O. A., Hincapié-Montoya, M., Contero, M., & Güemes-Castorena, D. (2022, December). Bibliometric analysis and systematic literature review of the intelligent tutoring systems. In Frontiers in Education (Vol. 7, p. 1047853). Frontiers.
Liang, J. C., Hwang, G. J., Chen, M. R. A., & Darmawansah, D. (2023). Roles and research foci of artificial intelligence in language education: an integrated bibliographic analysis and systematic review approach. Interactive Learning Environments, 31(7), 4270-4296.
Guo, S., Zheng, Y., & Zhai, X. (2024). Artificial intelligence in education research during 2013–2023: A review based on bibliometric analysis. Education and Information Technologies, 1-23.
Dewi, P. S., Widodo, A., Rochintaniawati, D., & Prima, E. C. (2021). Web-Based Inquiry in Science Learning: Bibliometric Analysis. Indonesian Journal of Science and Mathematics Education, 4(2), 191-203.
Lukito, A., & Wijayanti, P. (2023). Mathematical Understanding: Systematic Mapping Study Based on Bibliometric Analysis. Journal of Higher Education Theory and Practice, 23(20), 228-240.
Jing, Y., Zhao, L., Zhu, K., Wang, H., Wang, C., & Xia, Q. (2023). Research landscape of adaptive learning in education: A bibliometric study on research publications from 2000 to 2022. Sustainability, 15(4), 3115.
Cuéllar-Rojas, O. A., Hincapié, M., Contero, M., & Güemes-Castorena, D. (2021). Intelligent tutoring system: A bibliometric analysis and systematic literature review.
Lin, Y., & Yu, Z. (2023). A bibliometric analysis of artificial intelligence chatbots in educational contexts. Interactive Technology and Smart Education.
Sarin, G., Kumar, P., & Mukund, M. (2023). Text classification using deep learning techniques: a bibliometric analysis and future research directions. Benchmarking: An International Journal.
Trinidad, M., Ruiz, M., & Calderon, A. (2021). A bibliometric analysis of gamification research. IEEE Access, 9, 46505-46544.
Hsieh, C. C., Gunawan, I., & Li, H. C. (2023). A Bibliometric Review of Instructional Leadership Research: Science Mapping the Literature from 1974 to 2020. Revista de Educación, 1.
Chen, H. E., Sun, D., Hsu, T. C., Yang, Y., & Sun, J. (2023). Visualising trends in computational thinking research from 2012 to 2021: A bibliometric analysis. Thinking Skills and Creativity, 47, 101224.
Kousis, A., & Tjortjis, C. (2021). Data mining algorithms for smart cities: A bibliometric analysis. Algorithms, 14(8), 242.
Bahroun, Z., Anane, C., Ahmed, V., & Zacca, A. (2023). Transforming education: A comprehensive review of generative artificial intelligence in educational settings through bibliometric and content analysis. Sustainability, 15(17), 12983.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
