Bibliometric Cluster Analysis and Classification Algorithm for Questioning Effectiveness in Elementary School Classrooms

Authors

  • Rong Wang, Fadzilah Amzah

Keywords:

Bibliometric cluster, Classification, Elementary School, Centroid Clustering, Classrooms

Abstract

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).

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Published

26.03.2024

How to Cite

Rong Wang, Fadzilah Amzah. (2024). Bibliometric Cluster Analysis and Classification Algorithm for Questioning Effectiveness in Elementary School Classrooms. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 764–775. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5471

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Section

Research Article