An Improved BP Neural Network-based Quality Evaluation Model for Chinese International Education Teaching Courses

Huang, Binxian; Wang, Ke

doi:10.1145/3523089.3523109

Cited by 3 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This way of learning language knowledge, without access to professional knowledge, is not only easy to make foreign students feel “tired” in learning, but also not conducive to the stimulation of foreign students' enthusiasm for learning. So after realizing basic Chinese communication, we found a problem limited by professional Chinese knowledge, foreign students have some difficulties in professional learning, and domestic vocational colleges also lack relevant professional Chinese teaching experience, so the connection between basic Chinese and professional Chinese is not smooth [ 3 ]. Specifically, it includes four aspects: First, there is a bias in the cognition of the teaching of international Chinese courses.…”

Section: Introductionmentioning

confidence: 99%

Optimized multiple-attribute group decision-making through employing probabilistic hesitant fuzzy TODIM and EDAS technique and application to teaching quality evaluation of international Chinese course in higher vocational colleges

Xia

2024

Heliyon

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Optimized multiple-attribute group decision-making through employing probabilistic hesitant fuzzy TODIM and EDAS technique and application to teaching quality evaluation of international Chinese course in higher vocational colleges

Xia

2024

Heliyon

View full text Add to dashboard Cite

A BP Neural Network-Based Method for Evaluating the Quality of Creative Education in Minority Regions

Yang

Huang²,

Li³

et al. 2023

J CIRCUIT SYST COMP

View full text Add to dashboard Cite

Ethnic minority resources are very rich and contain rich historical resources and culture. Under the impact of modern information technology, the development of minority resources and the inheritance of ethnic culture are facing many challenges, and the current school education is lagging behind in exploring the ecological resources of minority groups, which makes the integration of creative education and minority groups encounter a bottleneck. In response to this situation, we make full use of the platform of creative education to actively explore the traditional skills contained in the lives of ethnic minorities. In the evaluation of creative education, we should not only focus on the evaluation of students’ works, but also on the improvement of students’ knowledge of various creative tools, their ability to use comprehensive subject knowledge, hands-on ability, solution ability and creativity ability during the whole learning process. Based on this, this paper proposes a back propagation neural network (BPNN)-based quality evaluation method for creative education to evaluate the quality of creative education from multiple dimensions. Experiments and comparisons show that the BPNN-based evaluation method proposed in this paper can better evaluate the whole process of creative education and help the further development of creative education in minority regions.

show abstract

A Vision sensing-based automatic evaluation method for teaching effect based on deep residual network

Sun

2023

MBE

View full text Add to dashboard Cite

<abstract><p>The automatic evaluation of the teaching effect has been a technical problem for many years. Because only video frames are available for it, and the information extraction from such dynamic scenes still remains challenging. In recent years, the progress of deep learning has boosted the application of computer vision in many areas, which can provide much insight into the above issue. As a consequence, this paper proposes a vision sensing-based automatic evaluation method for teaching effects based on deep residual network (DRN). The DRN is utilized to construct a backbone network for sensing from visual features such as attending status, taking notes, playing phones, looking outside, etc. The extracted visual features are further selected as the basis for the evaluation of the teaching effect. We have also collected some realistic course images to establish a real-world dataset for the performance assessment of the proposal. The proposed method is implemented on collected datasets via computer programming-based simulation experiments, so as to obtain accuracy assessment results as measurement. The obtained results show that the proposal can well perceive typical visual features from video frames of courses and realize automatic evaluation of the teaching effect.</p></abstract>

show abstract

An Improved BP Neural Network-based Quality Evaluation Model for Chinese International Education Teaching Courses

Cited by 3 publications

References 4 publications

Optimized multiple-attribute group decision-making through employing probabilistic hesitant fuzzy TODIM and EDAS technique and application to teaching quality evaluation of international Chinese course in higher vocational colleges

Optimized multiple-attribute group decision-making through employing probabilistic hesitant fuzzy TODIM and EDAS technique and application to teaching quality evaluation of international Chinese course in higher vocational colleges

A BP Neural Network-Based Method for Evaluating the Quality of Creative Education in Minority Regions

A Vision sensing-based automatic evaluation method for teaching effect based on deep residual network

Contact Info

Product

Resources

About