<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Analyzing the research changes in curriculum and pedagogy is significant in exploring its developmental vein and future direction in China, and helps establish a modern academic framework. In this paper, the KNN algorithm is used to meticulously process the data related to the reform of curriculum and pedagogy, and the factor analysis model is used to explore the influencing factors behind these data and the strength of their effects. The clustering and integration method is further used to categorize and integrate these factors to explore the development trend of curriculum and pedagogy deeply. By analyzing the changes in research hotspots, research directions, and author influence between 2010 and 2022, it was found that between 2010 and 2016, the proportion of curriculum and pedagogy-related research reached a maximum of 0.785, while the proportion of research in the category of innovative teaching and learning was 0.065. Teaching and learning has always been the dominant research direction, and its importance has always been maintained at more than 0.5 during the 12 years. In contrast, the research on curriculum reached an importance level of about 0.35 in 2013. In-depth study of classroom teaching is an indispensable and critical way to maintain the innovative development and lasting vitality of the discipline of curriculum and pedagogy.</p>
</abstract>

Analyzing the research changes in curriculum and pedagogy is significant in exploring its developmental vein and future direction in China, and helps establish a modern academic framework. In this paper, the KNN algorithm is used to meticulously process the data related to the reform of curriculum and pedagogy, and the factor analysis model is used to explore the influencing factors behind these data and the strength of their effects. The clustering and integration method is further used to categorize and integrate these factors to explore the development trend of curriculum and pedagogy deeply. By analyzing the changes in research hotspots, research directions, and author influence between 2010 and 2022, it was found that between 2010 and 2016, the proportion of curriculum and pedagogy-related research reached a maximum of 0.785, while the proportion of research in the category of innovative teaching and learning was 0.065. Teaching and learning has always been the dominant research direction, and its importance has always been maintained at more than 0.5 during the 12 years. In contrast, the research on curriculum reached an importance level of about 0.35 in 2013. In-depth study of classroom teaching is an indispensable and critical way to maintain the innovative development and lasting vitality of the discipline of curriculum and pedagogy.

Research Changes in Curriculum and Pedagogy Based on Big Data Perspectives

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Research Changes in Curriculum and Pedagogy Based on Big Data Perspectives"}

Analyzing the research changes in curriculum and pedagogy is significant in exploring its developmental vein and future direction in China, and helps establish...

Research Changes in Curriculum and Pedagogy Based on Big Data Perspectives

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 04 sty 2024

Przyjęty: 09 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0389

Słowa kluczowe
KNN algorithm, Factor analysis, Cluster analysis, Research change

© 2024 Jingjing Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research Changes in Curriculum and Pedagogy Based on Big Data Perspectives

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 04 sty 2024

Przyjęty: 09 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0389

Słowa kluczoweKNN algorithm, Factor analysis, Cluster analysis, Research change

© 2024 Jingjing Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
KNN algorithm, Factor analysis, Cluster analysis, Research change