Research on Team Teaching Model Based on Deep Learning Theory

2021 ◽  
Author(s):  
Chuanxin Fang ◽  
Yanfeng Liu ◽  
Min Deng
Keyword(s):  
Deep Learning ◽  
Learning Theory ◽  
Team Teaching ◽  
Teaching Model ◽  
Model Based

scholarly journals College English Online Teaching Model Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hui Ding ◽  
Yajun Chen ◽  
Linling Wang
Keyword(s):  
Academic Performance ◽  
Deep Learning ◽  
Online Teaching ◽  
Clustering Algorithm ◽  
Teaching Model ◽  
Learning Method ◽  
College English ◽  
Model Based ◽  
Gradient Descent Algorithm ◽  
Human Brains

In today’s era, online teaching plays an important part in the college English teaching. Deep learning, famous for its ability of imitating the learning process of human brains and obtaining the internal essential features or rules of voice, videos, images, and other data, can be applied to assist and improve the college English online teaching which involves a wide use of those data. Based on the combination of the multilayer neural network model and the k-means clustering algorithm, this paper designs a kind of deep learning method that can be used to assist and improve the college English online teaching. Experiments were designed to test the reliability of this deep learning method. The results show that the optimization algorithm designed in this paper, which can adjust the learning rate, will improve the common probability gradient descent algorithm. Besides, it is proved that the deep learning’s efficiency of the CNN model is significantly higher than that of the MLP model. With the help of this deep learning method, it becomes feasible to apply the technologies related to the artificial intelligence to help teachers deeply analyze and diagnose students’ English learning behavior, replace the teachers in part to answer students’ questions in time, and automatically grade assignments in the process of the college English online teaching. Surveys and exams were then conducted to evaluate the effect of the application of the college English online teaching model based on deep learning on the students’ learning cognition and their academic performance. The results show that the college English online teaching model based on deep learning can stimulate students’ learning motivation and improve their academic performance.

scholarly journals Animation Production Teaching Model based on Design-Oriented Learning

2018 ◽  
Vol 13 (08) ◽  
pp. 172
Author(s):  
Ben Ma
Keyword(s):  
Learning Theory ◽  
Resource Sharing ◽  
Teaching Model ◽  
Teaching Process ◽  
Team Cooperation ◽  
Independent Innovation ◽  
Transmission Of Knowledge ◽  
Model Based ◽  
Teacher Student ◽  
Innovation Ability

The course of two-dimensional animation production focuses on practice. In teaching, more attention should be paid to cultivation of students’ innovation ability, team cooperation ability and similar prior education goals. With the promotion of paperless animation design courses, the animation production process should include the knowledge points in teaching. With this regard, taking the advantages of Flash software, an animation teaching model based on design-oriented learning was constructed in this study relying on design-oriented learning theory in animation production teaching, and taking project-oriented learning and empirical learning theory as guidelines. Meanwhile, comparison was made with the traditional teaching methods that only emphasize the presentation and transmission of knowledge. The research results show that using Flash software with design-oriented animation production teaching model makes it easier for students to accept knowledge when compared with the traditional PPT teaching model. It cannot only fully mobilize the learners’ enthusiasm, initiative and independent innovation, but also promote the students’ ability to study independently and constantly throughout their life. The Flash teaching platform adopted in the teaching process facilitates teacher-student interaction, team communication, and resource sharing, and is an effective assistant in the multimedia teaching process.

scholarly journals Deep learning–based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms

2021 ◽  
Author(s):  
Luuk J. Oostveen ◽  
Frederick J. A. Meijer ◽  
Frank de Lange ◽  
Ewoud J. Smit ◽  
Sjoert A. Pegge ◽  
...  
Keyword(s):  
Deep Learning ◽  
White Matter ◽  
Image Quality ◽  
Iterative Reconstruction ◽  
Quality Characteristics ◽  
Model Based ◽  
Hybrid Iterative Reconstruction ◽  
Contrast Ct ◽  
Lower Noise ◽  
Reconstruction Time

Abstract Objectives To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT). Methods Cerebral NCCT acquisitions of 50 consecutive patients were reconstructed using DLR, Hybrid-IR and MBIR with a clinical CT system. Image quality, in terms of six subjective characteristics (noise, sharpness, grey-white matter differentiation, artefacts, natural appearance and overall image quality), was scored by five observers. As objective metrics of image quality, the noise magnitude and signal-difference-to-noise ratio (SDNR) of the grey and white matter were calculated. Mean values for the image quality characteristics scored by the observers were estimated using a general linear model to account for multiple readers. The estimated means for the reconstruction methods were pairwise compared. Calculated measures were compared using paired t tests. Results For all image quality characteristics, DLR images were scored significantly higher than MBIR images. Compared to Hybrid-IR, perceived noise and grey-white matter differentiation were better with DLR, while no difference was detected for other image quality characteristics. Noise magnitude was lower for DLR compared to Hybrid-IR and MBIR (5.6, 6.4 and 6.2, respectively) and SDNR higher (2.4, 1.9 and 2.0, respectively). Reconstruction times were 27 s, 44 s and 176 s for Hybrid-IR, DLR and MBIR respectively. Conclusions With a slight increase in reconstruction time, DLR results in lower noise and improved tissue differentiation compared to Hybrid-IR. Image quality of MBIR is significantly lower compared to DLR with much longer reconstruction times. Key Points • Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction. • Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction. • Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.

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