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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 |
