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<p>Drawing on the insights from the expert consultation form and the current state of parenting ability among teachers of Marxist Civics courses in higher education institutions, this paper initiates the selection of evaluation indicators for these teachers’ pedagogical skills. These indicators undergo reliability and validity testing before finalizing the evaluation system. Subsequently, the indices are quantified, and principal component analysis is employed to reduce the dimensions of these indices, resulting in an index feature matrix. The scores of these evaluation indices serve as inputs to a Bidirectional Recurrent Neural Network (BiRNN), with the comprehensive ability scores of Civics teachers as the outputs. This forms the basis for constructing a BiRNN-based evaluation model for assessing the educational capabilities of Marxist Civics course instructors in colleges and universities. The study then proceeds with an empirical analysis using this model. The results show that BiRNN (RMSE=0.0084, R=0.9948) can effectively improve the evaluation accuracy of the parenting ability of college Civics teachers, and B1 to B10 all show a significant correlation with the parenting ability (Y) of Civics teachers (Sig&lt;0.05). The Person correlation coefficients are 0.607, 0.691, 0.472, 0.785, 0.499, 0.758, 0.437, 0.539 and 0.519 respectively. This study reveals in depth the factors influencing the parenting ability of Marxist Civics teachers, which is of great significance in promoting the enhancement of the parenting ability of Marxist Civics teachers in colleges and universities.</p>
</abstract>

Drawing on the insights from the expert consultation form and the current state of parenting ability among teachers of Marxist Civics courses in higher education institutions, this paper initiates the selection of evaluation indicators for these teachers’ pedagogical skills. These indicators undergo reliability and validity testing before finalizing the evaluation system. Subsequently, the indices are quantified, and principal component analysis is employed to reduce the dimensions of these indices, resulting in an index feature matrix. The scores of these evaluation indices serve as inputs to a Bidirectional Recurrent Neural Network (BiRNN), with the comprehensive ability scores of Civics teachers as the outputs. This forms the basis for constructing a BiRNN-based evaluation model for assessing the educational capabilities of Marxist Civics course instructors in colleges and universities. The study then proceeds with an empirical analysis using this model. The results show that BiRNN (RMSE=0.0084, R=0.9948) can effectively improve the evaluation accuracy of the parenting ability of college Civics teachers, and B1 to B10 all show a significant correlation with the parenting ability (Y) of Civics teachers (Sig<0.05). The Person correlation coefficients are 0.607, 0.691, 0.472, 0.785, 0.499, 0.758, 0.437, 0.539 and 0.519 respectively. This study reveals in depth the factors influencing the parenting ability of Marxist Civics teachers, which is of great significance in promoting the enhancement of the parenting ability of Marxist Civics teachers in colleges and universities.

Research on the Path of Improving the Parenting Ability of Teachers of Marxist Civics Classes in Colleges and Universities Based on Bidirectional Recurrent Neural Networks

Continuing Education College of Yunnan University of Chinese Medicine

Applied Mathematics and Nonlinear Sciences

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

Drawing on the insights from the expert consultation form and the current state of parenting ability among teachers of Marxist Civics courses in higher...

Research on the Path of Improving the Parenting Ability of Teachers of Marxist Civics Classes in Colleges and Universities Based on Bidirectional Recurrent Neural Networks

Online veröffentlicht: 07. Juni 2024

Seitenbereich: -

Eingereicht: 04. März 2024

Akzeptiert: 06. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1431

Schlüsselwörter
BiRNN, Principal Component Analysis, Quantitative Processing, Teacher Nurturing Competencies

© 2024 Feng Lv, published by Sciendo

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

Research on the Path of Improving the Parenting Ability of Teachers of Marxist Civics Classes in Colleges and Universities Based on Bidirectional Recurrent Neural Networks

Online veröffentlicht: 07. Juni 2024

Seitenbereich: -

Eingereicht: 04. März 2024

Akzeptiert: 06. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1431

SchlüsselwörterBiRNN, Principal Component Analysis, Quantitative Processing, Teacher Nurturing Competencies

© 2024 Feng Lv, published by Sciendo

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

Schlüsselwörter
BiRNN, Principal Component Analysis, Quantitative Processing, Teacher Nurturing Competencies