A Teaching Quality Evaluation Model for Preschool Teachers Based on Deep Learning

Ge, Dongjun; Wang, Xiaoyue; Liu, Jingting

doi:10.3991/ijet.v16i03.20471

Cited by 23 publications

(14 citation statements)

References 22 publications

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“…e authors of literature [15] combined speech and expression to achieve English teaching analysis using deep learning models. e authors of literature [16] proposed a teaching effectiveness evaluation method based on LSTM networks, which solved the problems such as the poor teaching effectiveness of traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Optimized LSTM Networks with Improved PSO for the Teaching Quality Evaluation Model of Physical Education

Yin

2022

International Transactions on Electrical Energy Systems

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Effective teaching behavior in physical education has an important impact on the quality of classroom teaching. To overcome the shortcomings in the existing university teaching quality assessment procedure, this paper designs an evaluation model for physical education departments based on long short-term memory networks (LSTM) with the improved particle swarm optimization (PSO). The model is constructed by analyzing the connotation and index system of teacher education teaching quality and constructing a fan-leaf structure model of teacher education teaching quality. Then, an improved PSO-LSTM model is proposed to train the teaching samples. The evaluation model applies the improved LSTM model and optimizes the network structure by dynamically adjusting the learning rate. The model is then optimized for the number of neurons and iterations of the network using the improved PSO. The results of the experiment indicate that the proposed model is effective in evaluating the quality of physical education. Moreover, the model analysis’ accuracy has greatly improved. This helps teachers have a comprehensive understanding of classroom dynamics and improve their professional competence and classroom teaching quality.

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Section: Introductionmentioning

confidence: 99%

Optimized LSTM Networks with Improved PSO for the Teaching Quality Evaluation Model of Physical Education

Yin

2022

International Transactions on Electrical Energy Systems

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“…DL can use standardization, normalization, and other operations for data preprocessing to make the data meet the training needs [23]. Normalized data are handled by scaling with the two maxima:…”

Section: Eorymentioning

confidence: 99%

Application of Deep Learning in College Physical Education Design under Flipped Classroom

Huang

2022

Computational Intelligence and Neuroscience

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With the development of information technology, teaching reform has also undergone major changes. e traditional college physical education teaching method cannot meet the needs of the majority of students, and the physical education teaching mode continues to be reformed. Microcourse is the most intuitive form of deep integration of information technology and physical education. From the perspective of the ipped classroom (FC), the physical education model has gradually changed from teacher centered to student centered. Deep learning (DL) emphasizes that learners have the ability to actively construct knowledge, e ectively transfer knowledge, and solve real problems. is design applies DL and convolutional neural network to the teaching design of physical gymnastics in colleges and universities.e application of the DL teaching model based on FC in the microcourse teaching of gymnastics in colleges and universities is studied and evaluated. e results show that the current utilization of microcourse teaching resources is too low. Interest-oriented teaching microcourses cannot improve students' interests. e proportion of students who are interested is relatively small, and more than 50% of students are not interested. Teachers generally believe that the current gymnastics microcourse needs further optimization and improvement. e poor quality of microvideos and the lack of supervision and reward mechanism in the course are the main reasons for the insu cient students' interest. e complexity of the videos and the liveliness of the discussions are the main problems of low resource utilization. e student's interest in learning is greatly improved after the application of the designed model, and the proportion increases to 82.4%. e e ect on ordinary college students is the most obvious, and the e ect of microvideo learning has been signi cantly promoted. Design mode has the most obvious improvement in improving learning e ciency and autonomous learning ability. e improvement of learning ability has increased from 18% to 72%, and the improvement of learning e ciency has increased from 39% to 82%. Meanwhile, students' interest in learning is stimulated, and the utilization of resources is improved.

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“…Educational evaluation should first set goals, and the establishment of established goals can effectively guide the evaluation of English teaching effectiveness, while the evaluation of English teaching effectiveness can promote the educational work toward the ideal goal of English teaching [13]. Ge et al proposed a generalized entropy fuzzy comprehensive evaluation algorithm based on feature weighting by introducing the entropy constraint term into the objective function of the bat fuzzy comprehensive evaluation algorithm and showed through experiments that the improved algorithm can, to a certain extent, solve the problem of low evaluation quality due to uniform contraction of the comprehensive evaluation algorithm in that the study compares the real situation in the process of English teaching with the ideal goal comparison, which is considered as the actual course of ELT assessment [14]. Rao et al introduced particle swarm algorithm in the basic bat algorithm and used acceleration factor to control the speed of individuals effectively, and the experimental results showed that the improved algorithm had better searchability and considered that ELT assessment is a process of acquiring and collecting information with clear objectives and systematically, and integrating and analyzing it, and finally assisting decision-makers of ELT management and assessment to make the process of scientific and rational choice among many possible assessment options [15].…”

Section: Related Workmentioning

confidence: 99%

A Study on the Quality Evaluation of English Teaching Based on the Fuzzy Comprehensive Evaluation of Bat Algorithm and Big Data Analysis

Ji¹,

Tsai

2021

Mathematical Problems in Engineering

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In this paper, the fuzzy comprehensive evaluation model based on the bat algorithm quantifies the qualitative evaluation effectively and provides a feasible and convenient English teaching quality evaluation system by combining quantitative evaluation with objective index data. Firstly, the English teaching quality evaluation model is constructed based on the fuzzy comprehensive evaluation analysis method and the weight values of each factor are calculated; secondly, the three types of data in the model are processed separately. This includes standardizing the data of objective indicators such as students’ course grades and weakening the influence of course difficulty on this indicator. The fuzzy comprehensive evaluation model based on the bat algorithm quantifies the qualitative evaluation to make the calculated comprehensive evaluation of English teaching quality more comprehensive and objective; then the comprehensive calculation of English teaching quality evaluation is completed, and the English teaching quality evaluation model is constructed by extracting keywords based on the qualitative evaluation; finally, a runnable English teaching quality evaluation system is designed and implemented. A fuzzy comprehensive evaluation algorithm based on improved bat algorithm optimization is proposed. The algorithm uses the improved fuzzy comprehensive evaluation algorithm to optimize the initial clustering centers and adopts a new objective function to guide the clustering process, thus improving the clustering quality of the fuzzy comprehensive evaluation algorithm. Comparative analysis through models shows that the improved algorithm improves the clustering accuracy to a certain extent when compared with the traditional fuzzy comprehensive evaluation clustering algorithm for analysis. The bat algorithm is one of the stochastic global optimization models. It can take advantage of the group, integrate global search and local search, and achieve rapid convergence. Therefore, it plays an important role in optimizing the evaluation of English teaching quality. This study enriches the theoretical study of English teaching quality evaluation to a certain extent and can play a role in strengthening and improving English teaching quality evaluation at the present stage.

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A Teaching Quality Evaluation Model for Preschool Teachers Based on Deep Learning

Cited by 23 publications

References 22 publications

Optimized LSTM Networks with Improved PSO for the Teaching Quality Evaluation Model of Physical Education

Optimized LSTM Networks with Improved PSO for the Teaching Quality Evaluation Model of Physical Education

Application of Deep Learning in College Physical Education Design under Flipped Classroom

A Study on the Quality Evaluation of English Teaching Based on the Fuzzy Comprehensive Evaluation of Bat Algorithm and Big Data Analysis

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