Computer Assisted Instruction System under Artificial Intelligence Technology

Zhang, Jingjing

doi:10.3991/ijet.v16i05.20307

Cited by 14 publications

(9 citation statements)

References 11 publications

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“…"5G Internet+" has had a significant impact on academics' interest in "teacher learning communities," and scholars have linked them to the growth of teacher learning and investigated new paths for teacher learning [23]. When it comes to the concept of "community," literature suggests that it is a group of people who work together to build a better world for everyone [24].…”

Section: Introductionmentioning

confidence: 99%

Remote Music Teaching Classroom Based on Machine Learning and 5G Network Station

Seng

2022

Wireless Communications and Mobile Computing

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Most recently, the technological impacts are increasing day by day. Artificial intelligence, machine learning, deep learning, and big data are used to acquire enough application feasibility to reduce work pressure for humans. The way information society is being developed in this world will result in some changes in the development of the education system. The impact will result in some changes in the development of the education system. This research focuses on improving remote music teaching following the presence of a 5G network. Data perception handling is considered the central issue in remote communication, which creates some inabilities to achieve better communication between the systems. To avoid this kind of risk management, most schools and colleges have planned to create a separate network working under wireless connections while delivering the course content to a minimum number of students. Another effective integration of this evolution is efficiently building the remote music education system. Here the compatibility should match both the cases, be they students or the teachers. In this case, network speed is considered one of the most significant impacts on students listening to online classes. Therefore, a 5G network might be the best path to take by increasing the network speed and giving a strong impetus. The proposed system utilizes convolutional neural network (CNN) algorithm to train the intelligent system to provide remote music education through wireless 5G networks. The parameters used for analyzing the proposed system with the existing system are compline, uncertainty, unconfirmed, and out-of-place. The proposed algorithm has achieved an overall accuracy of 99.13% than the existing system.

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Section: Introductionmentioning

confidence: 99%

Remote Music Teaching Classroom Based on Machine Learning and 5G Network Station

Seng

2022

Wireless Communications and Mobile Computing

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“…Experiments show that the model is suitable for conventional major/minor triad classification and large vocabulary chord recognition and is superior to other most advanced chord recognition systems [10]. In order to promote the development of an intelligent teaching system, Zhang combined AI technology with a computer-aided teaching system to produce an intelligent computer teaching system and studied the design of the system [11]. The above research results show that in the current environment, the combination of online music teaching and AI technology is the general trend.…”

Section: Introductionmentioning

confidence: 99%

The Use of Wireless Network Combined with Artificial Intelligence Technology in the Reform of Music Online Teaching System

Chen

Meng

2022

Wireless Communications and Mobile Computing

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The purpose is to respond to the call of the government on the artificial intelligence (AI) policy of music online education. First, wireless network technology and AI technology are described in detail; the application principle of AI technology in traditional music teaching mode and online teaching mode is analyzed; then, the combination model of AI technology and music online education system in 5th-generation (5G) wireless network environment is proposed. These models include music online composition teaching model, music online teaching scoring model, and music online lyric writing teaching model. Finally, the models are verified in the application of AI technology. The results show that AI technology has good application performance in the models above. Moreover, the direction of music online teaching reform and innovation along the combination of 5G network environment and AI is correct under the background of the new era. This exploration expands the application scenario of AI technology and provides ideas for the reform and innovation of music online education in the context of the new era.

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“…Further, with the integration of Artificial Intelligent (AI) in education, Institute for Computer-Assisted Information (ICAI) analyses the characteristics and processes of students’ learning and thinking. ICAI aims to promote the intelligent development of the cognitive learning mode and teaching process ( Zhang, 2021 ). Figure 2 is the basic structure of ICAI.…”

Section: Methodsmentioning

confidence: 99%

“…It can process information by adjusting the weight between neurons. In particular, CNN, a feedforward Neural Network (NN) model with several convolution layers and pooling layers, can well to image processing (Zhang et 2021). Meanwhile, CNN a deep structure and can complete convolution operations or the "filter operation" in image processing (Dhillon and Verma, 2020).…”

Section: Convolutional Neural Network Theory and Its Application In I...mentioning

confidence: 99%

Entrepreneurship education-infiltrated computer-aided instruction system for college Music Majors using convolutional neural network

Chen

2022

Front. Psychol.

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The purpose is to improve the teaching and learning efficiency of college Innovation and Entrepreneurship Education (IEE). Firstly, from the perspective of aesthetic education, this work designs the teacher and student sides of the Computer-aided Instruction (CAI) system. Secondly, the CAI model is implemented based on the weight sharing and local perception of the Convolutional Neural Network (CNN). Finally, the performance of the CNN-based CAI model is tested. Meanwhile, it analyses students’ IEE experience under the proposed CAI model through a case study of Music Majors from Xi’an Conservatory of Music. The experimental data show that the CNN-based CAI model can respond quickly and stably when users access different functional modules, such as webpage browsing. The proposed CAI model increases students’ entrepreneurial interest, skills, and knowledge by 55.62, 57.32, and 72.12%, respectively. Students’ entrepreneurial practice ability has been improved by over 50.00%; such an increase in entrepreneurial practice ability has also shown individual differences. Thus, the proposed Music Majors-oriented IEE-infiltrated CAI model based on CNN improves students’ entrepreneurial practice ability and reflects the positive experience of Music Majors on IEE. The finding provides references for the step-by-step identification of the CNN-based CAI model and has certain guiding significance for analyzing the effect of college IEE.

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Computer Assisted Instruction System under Artificial Intelligence Technology

Cited by 14 publications

References 11 publications

Remote Music Teaching Classroom Based on Machine Learning and 5G Network Station

Remote Music Teaching Classroom Based on Machine Learning and 5G Network Station

The Use of Wireless Network Combined with Artificial Intelligence Technology in the Reform of Music Online Teaching System

Entrepreneurship education-infiltrated computer-aided instruction system for college Music Majors using convolutional neural network

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