Research On The Analysis Model Of Teachers’ Teaching Ability Based On Multiple Evaluation

In order to improve the teaching level of university faculty,universities usually adopt some corresponding teaching evaluation strategies.With the continuous development of education informatization,the use of information technology to solve the limitation problem of unilateral evaluation of teachers and thus help teachers to improve their teaching ability has become a hot research topic in the field of teaching evaluation.The traditional unilateral evaluation of teachers’ teaching ability makes the evaluation data lack correlation with each other,and its evaluation results cannot accurately reflect the true situation of teachers’ teaching ability.Multiple evaluation is the evaluation of teachers from multiple perspectives,which includes the diversification of evaluation subjects,the multidimensionality of evaluation contents and the diversification of evaluation methods.Therefore,this paper analyzes the data of multiple evaluations and constructs a reasonable multiple evaluation model to assist teachers in objectively understanding their own teaching ability and eventually guide them to improve their teaching ability.This study is divided into three main parts as follows.(1)Construction of the multivariate evaluation model.By analyzing the existing framework of teachers’ teaching ability,we construct a multivariate evaluation index system and clarify the hierarchical structure of multivariate evaluation indexes.Combining the characteristics of multivariate evaluation,the construction of the multivariate evaluation model is completed from the personal information characteristics,macro-competency characteristics,micro-competency characteristics and relationship characteristics of teachers.(2)Dataization of the multivariate evaluation model.In this paper,the acquired student evaluation data,peer teacher evaluation data,expert evaluation data,alumni text evaluation data,and teachers’ teaching materials are analyzed in depth for teachers’ macro-competency and micro-competency characteristics.First,the multivariate structured data were processed using fuzzy hierarchical analysis and fuzzy comprehensive evaluation methods.Second,the multivariate unstructured data were processed using Word2 Vec word vector model and LDA model.Finally,we achieve the purpose of data processing of multivariate evaluation models,so as to realize the effective use of multivariate evaluation data.(3)Application strategy of multivariate evaluation model.In order to make the data-based results of the multivariate evaluation model beneficial to the improvement of teachers’ teaching ability,this paper designs a recommendation algorithm based on multivariate evaluation on the basis of teachers’ macro-competency and micro-competency characteristics.In practice,multivariate evaluation cannot produce preference behavior for all indicators in the evaluation system.Therefore,there is a data sparsity problem in common evaluation items.The problem can be effectively solved by introducing a common evaluation weight function based on the traditional similarity calculation method.In addition,multiple evaluations are also affected by time change,which in turn generates the problem of preference migration.To solve this problem,this paper introduces a temporal weighting function for the calculation of predicted values.Finally,the recommendation of teachers’ teaching ability improvement strategies under multivariate evaluation is carried out.The constructed multivariate evaluation model can realize the diversified evaluation of teachers’ teaching ability and help teachers obtain more objective and accurate evaluation of teaching ability.Therefore,the multivariate evaluation analysis model can not only help teachers improve their own teaching ability,but also help improve the efficiency of teaching quality management in universities.