Japanese Translation Quality Evaluation System Based on Deep Neural Network Algorithm

The current translation quality evaluation system relies on the combination of manual and text comparison for evaluation, which has the defects of low efficiency and large evaluation errors. In order to optimize the defects of the current quality evaluation system, a Japanese translation quality evaluation system based on deep neural network algorithm will be designed. In order to improve the processing efficiency of the system, the USB3.0 communication module of the hardware system will be optimized. Based on the hardware design, the reference translation map is used to extend the reference translation of Japanese translation. The evaluation indexes of over- and under-translation are set, and the evaluation of Japanese translation quality is realized after the parameters are determined by training the deep neural network using the sample set. The system functional test results show that the average data transmission processing time of the system is improved by about 31.27%, and the evaluation error interval is smaller and the evaluation is more reliable.

Based on Optimization Research on the Evaluation System of English Teaching Quality Based on GA-BPNN Algorithm

English teaching is an important part of basic teaching in our country, which has been deeply concerned by all aspects. Its teaching quality not only is related to the purpose of English teaching, but also has a far-reaching impact on students’ English learning. Therefore, the construction of English teaching quality evaluation system has become the focus of research. However, the traditional English teaching quality evaluation method has some problems; for example, the subjectivity of teaching evaluation is strong, the evaluation index is not comprehensive, and the evaluation results are distorted. Therefore, this paper studies the English teaching quality evaluation system based on optimized GA-BP neural network algorithm. On the basis of BP neural network algorithm evaluation simulation, GA algorithm is introduced for optimizing, and GA-BP neural network algorithm model is further optimized by GA adaptive degree variation and entropy method. The experimental results show that the optimized GA-BP neural network algorithm has faster convergence speed and smaller error. At the same time, the optimized GA-BP neural network algorithm evaluation model has better adaptability and stability, and its expected results are more in line with the ideal value. The results of English teaching quality evaluation are more scientific, showing higher value in the application of English teaching quality evaluation.

Validation and Development of a Moroccan School Quality Evaluation System

Evaluation is currently at the heart of the priorities of education systems. It is not limited to learning but affects several aspects: teachers, schools, training, management, education policies, and the system as a whole. There is a need in this area where research is extremely scarce in Morocco and especially in the teaching and education sector. The notion of transposing quality evaluation to the pedagogical side is very difficult and ambiguous. The evaluation of a school is a complex process, with varied practices and multiple actors. The first objective of this work is to present, within a rigorous methodological framework, the validation of pedagogical and administrative quality indicators in schools. This tool is a dashboard with precise indicators for the pedagogical audit of schools and educational institutions adapted to the Moroccan context. To select the best indicators, we used several techniques (structured interviews, focus groups, factor analysis, etc.) with the actors who carry out their activities. We identified three (03) fields and ten (10) criteria with indicators that form the basis of a quality assessment. The fields are management and strategic planning, administrative and sector management, and pedagogical organization.

Application of Optimized GA-BPNN Algorithm in English Teaching Quality Evaluation System

The assessment of teaching quality is a very complex and fuzzy nonlinear process, which involves many factors and variables, so the establishment of the mathematical model is complicated, and the traditional evaluation method of teaching quality is no longer fully competent. In order to evaluate teaching quality effectively and accurately, an optimized GA-BPNN algorithm based on genetic algorithm (GA) and backpropagation neural network (BPNN) is proposed. Firstly, an index system of teaching quality evaluation is established, and a questionnaire is designed according to the index system to collect data. Then, an English teaching quality evaluation system is established by optimizing model parameters. The simulation shows that the average evaluation accuracy of the GA-BPNN algorithm is 98.56%, which is 13.23% and 5.85% higher than those of the BPNN model and the optimized BPNN model, respectively. The comparison results show that the GA-BPNN algorithm in teaching quality evaluation can make reasonable and scientific results.

Online Classroom Teaching Quality Evaluation System Based on Facial Feature Recognition

With the rapid development of computer big data technology, online education in the form of online courses is increasingly becoming an important means of education. In order to objectively evaluate the teaching quality of online classroom, a teaching quality evaluation system based on facial feature recognition is proposed. The improved (MTCNN) multitask convolutional neural network is used to determine the face region, and then the eye and mouth regions are located according to the facial proportion relationship of the face. The light AlexNet classification based on Ghost module was used to detect the open and close state of eyes and mouth and combined with PERCLOS (percentage of eye closure) index values to achieve fatigue detection. Large range pose estimation from pitch, yaw, and roll angles can be achieved by easily locating facial feature angles. Finally, the fuzzy comprehensive evaluation method is used to evaluate students’ learning concentration. The simulation experiments are conducted, and the results show that the proposed system can objectively evaluate the teaching quality of online courses according to students' facial feature recognition.

Exploration on the Teaching Reform Measure for Machine Learning Course System of Artificial Intelligence Specialty

Due to the particularity of the artificial intelligence major and the machine learning courses learned, the traditional course teaching model is not suitable for artificial intelligence major machine learning courses. Based on this background, this article proposes a new system based on machine learning curriculum teaching reform. It mainly includes the reform of curriculum teaching mode, curriculum practice reform, and teaching process reform. In order to verify the effect of the proposed new model on the teaching quality of machine learning courses, this article also proposes an evaluation method based on intelligent technology. Firstly, the feasibility of evaluation based on intelligent technology is described. Secondly, it lists the application details of the existing teaching evaluation based on intelligent technology. Finally, a novel teaching quality evaluation system based on intelligent technology is proposed. The system collects student facial expression data and uses classification algorithms to make classification decisions on the data. The result of the decision can give feedback on the quality of classroom teaching. The comparison of experiments based on different intelligent technologies shows that the teaching quality evaluation system proposed in this article is feasible and effective.

Thoughts on the Research of the Multi-evaluation System of the Quality of “Ideological and Political Theories Teaching in All Courses” in Colleges and Universities

The teaching quality evaluation system of "Ideological and Political Theories Teaching in All Courses" is far from perfect, as the appraisal content is unitary, and the appraisal method become too rigid. The evaluation system of teaching quality is quite an important mechanism and "baton" to promote the teaching quality of " Ideological and Political Theories Teaching in All Courses ". It has important and realistic research significance to construct a diversified teaching quality evaluation system of " Ideological and Political Theories Teaching in All Courses ". This article focuses on the teaching quality of appraisal content, evaluation subject, appraisal method, and believes that the overall framework of the multi-evaluation system of " Ideological and Political Theories Teaching in All Courses " teaching quality includes multi-evaluation index content, evaluation subject and multi-evaluation methods.