AI Article Synopsis
- This paper addresses the bias of human influence on the weight of evaluation indices in assessing teachers' comprehensive literacy, particularly in environmental protection education.
- It introduces a new evaluation model utilizing a hierarchical Bayesian beta-return model for more rational weight distribution of evaluation indicators.
- Simulation results demonstrate that this model significantly enhances the accuracy of evaluating the comprehensive quality of environmental protection professional teachers.
Article Abstract
In order to improve the shortcomings that the weight of each index is easily affected by human influence in the evaluation of traditional teachers' comprehensive literacy, on the basis of the analysis of index coefficients, feature reorganization, and feature analysis, this paper constructs the comprehensive literacy of environmental protection professional teachers' education in the big data environment. The evaluation model is proposed, and a weight distribution scheme for teachers' comprehensive literacy evaluation indicators is proposed based on the hierarchical Bayesian beta-return model, so as to improve the rationality of the indicator distribution and make the prediction results more accurate. The simulation results further verify the superiority of the proposed model in improving the comprehensive quality evaluation level of environmental protection teachers.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581667 | PMC |
| http://dx.doi.org/10.1155/2022/6848352 | DOI Listing |
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