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

<p>This study presents a novel approach to the application of distributed logistic modeling for the enhancement of education and teaching in colleges and universities, particularly focusing on the pedagogical goals and curriculum content. Utilizing the abundance and accessibility of educational resources, this research harnesses data from diverse sources such as books, textbooks, catechisms, and blogs. Through the application of text mining and text recognition technologies, preliminary data are extracted for analysis. Subsequent data preprocessing steps involve the removal of extraneous information, null values, and outliers to refine the dataset. The research identifies key determinants influencing educational outcomes, establishing independent and dependent variables for these factors. The logistic and sigmoid functions are employed to compute the loss function, thereby addressing issues related to overfitting. Regularization transformations further optimize the results, culminating in the development of a robust distributed logistic model for education. An illustrative analysis demonstrates the model’s applicability and effectiveness in understanding and improving educational practices. Data presentation: P=0.026&lt;0.05, the independent variable of knowledge base level (<italic>x<sub>7</sub></italic>) has a significant effect on the effect of education and teaching and OR-Value=3.11, that is to say, if the knowledge base level rises by one unit, the dependent variable of the impact of education and teaching will be increased by 3.11 magnitude of change.</p>
</abstract>

This study presents a novel approach to the application of distributed logistic modeling for the enhancement of education and teaching in colleges and universities, particularly focusing on the pedagogical goals and curriculum content. Utilizing the abundance and accessibility of educational resources, this research harnesses data from diverse sources such as books, textbooks, catechisms, and blogs. Through the application of text mining and text recognition technologies, preliminary data are extracted for analysis. Subsequent data preprocessing steps involve the removal of extraneous information, null values, and outliers to refine the dataset. The research identifies key determinants influencing educational outcomes, establishing independent and dependent variables for these factors. The logistic and sigmoid functions are employed to compute the loss function, thereby addressing issues related to overfitting. Regularization transformations further optimize the results, culminating in the development of a robust distributed logistic model for education. An illustrative analysis demonstrates the model’s applicability and effectiveness in understanding and improving educational practices. Data presentation: P=0.026<0.05, the independent variable of knowledge base level (x7) has a significant effect on the effect of education and teaching and OR-Value=3.11, that is to say, if the knowledge base level rises by one unit, the dependent variable of the impact of education and teaching will be increased by 3.11 magnitude of change.

Optimization of Practical Path of Teaching Reform in Higher Education - Based on Distributed Logistic Model Application

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This study presents a novel approach to the application of distributed logistic modeling for the enhancement of education and teaching in colleges and...

Optimization of Practical Path of Teaching Reform in Higher Education - Based on Distributed Logistic Model Application

Published Online: Jun 07, 2024

Page range: -

Received: Mar 04, 2024

Accepted: May 08, 2024

DOI: https://doi.org/10.2478/amns-2024-1388

Keywords
Logistic model, Data preprocessing, Regularization transformation, Sigmoid function, Teaching and learning in higher education

© 2024 Xiaoni Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Optimization of Practical Path of Teaching Reform in Higher Education - Based on Distributed Logistic Model Application

Published Online: Jun 07, 2024

Page range: -

Received: Mar 04, 2024

Accepted: May 08, 2024

DOI: https://doi.org/10.2478/amns-2024-1388

KeywordsLogistic model, Data preprocessing, Regularization transformation, Sigmoid function, Teaching and learning in higher education

© 2024 Xiaoni Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Logistic model, Data preprocessing, Regularization transformation, Sigmoid function, Teaching and learning in higher education