scholarly journals Course of Artificial Intelligence + Composition for Young Backbone Teachers

2020 ◽  
Vol 2 (1) ◽  
pp. 22-26
Author(s):  
Lian LIU
Keyword(s):  
Artificial Intelligence ◽  
Music Education ◽  
Markov Chain Model ◽  
Application Framework ◽  
Chain Model ◽  
Teachers And Students ◽  
Artificial Intelligence Technology ◽  
Music Model ◽  
Intelligent Technology ◽  
Practical Teaching

Based on the analysis of the needs in the education field, the basic application framework scenarios of the current artificial intelligence technology in the field of music education are constructed. The characteristics of various models (typed music model, Markov chain model, genetic algorithm model, neural network model, etc.) are used to improve the rule knowledge to form a practical and effective hybrid model. The innovation lies in combining non-note units (reproducing structures) in music composition to intelligently compose tracks by combining formulas and matrix data combinations. The project conducts in-depth cooperation on the training of composition teachers and students from the fields of curriculum teaching, practical teaching, and innovation competition, and strives to combine theoretical teaching and engineering cases, practical teaching and industrial products, subject competition and intelligent technology. Combination to achieve collaborative education of industry-academia cooperation.

scholarly journals Research on Campus Education Information System Based on Internet of Things and Artificial Intelligence Decision

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hetiao Hong
Keyword(s):  
Artificial Intelligence ◽  
Information System ◽  
Original System ◽  
Functional Testing ◽  
Excellent Performance ◽  
Teachers And Students ◽  
Interface Function ◽  
Artificial Intelligence Technology ◽  
Education Platform ◽  
Better Than

Because of the different reasons between regions, the distribution of educational resources is also different, the development of each school is unbalanced, and the degree of campus education informationization is different. The complex functional structure not only does not facilitate teachers and students but also leads to many problems: the prevention and prevention of campus life safety. It is difficult to keep and use multiple cards owned by one person. Software and education platform cannot be seamlessly connected, and there are various barriers between data and data and people and data. The lack of learning materials leads to the inequality of information. There are no good feedback and solution between teachers and students. It is difficult to manage accurately with a large number of people. This study will be based on the Internet and artificial intelligence technology, to explore how to study a large (or super large), concise and efficient, and excellent performance of campus education information system; this system can meet the teachers and students no matter what year, month, and day of a large number of visits. For some problems in the process of building the system, actively optimize and refine them. After functional testing and analysis of the system, the experimental results show that the interface function of the new system is stable, the usability test is better than the feedback experience of the original system, the response time is reduced by 21.6% on average, and the overall power consumption of the system is reduced by about 1.43% on average.

scholarly journals Connotation Analysis and Paradigm Shift of Teaching Design under Artificial Intelligence Technology

2021 ◽  
Vol 16 (05) ◽  
pp. 73
Author(s):  
Jian Huang ◽  
Gang Shen ◽  
Xiping Ren
Keyword(s):  
Artificial Intelligence ◽  
Paradigm Shift ◽  
Learning Ability ◽  
Teaching Activities ◽  
Learning Efficiency ◽  
Teaching Objectives ◽  
Teaching Design ◽  
Teachers And Students ◽  
Artificial Intelligence Technology ◽  
Teaching Efficiency

The influence of artificial intelligence technology on teaching design is explored to improve teaching efficiency. First, artificial intelligence is introduced and its impacts on teaching design are analyzed. Second, the connotation of the paradigm of teaching design and the paradigm shift for teaching design are explored using the paradigm shift analysis framework. Finally, the changes in teaching design under artificial intelligence are analyzed, and the impacts of artificial intelligence on teaching activities are investigated. The results show that the application of artificial intelligence technology has led to different levels of change in the six elements of teaching design, including teaching objectives, service objects (teachers and students), teaching content, teaching media, teaching environment, and teaching evaluation. The connotation and paradigm shift of the teaching design are introduced from the four elements based on the artificial intelligence technology. It is found that artificial intelligence technology can enhance the learning ability and cognitive ability of students to a certain extent while improving the teaching efficiency and learning efficiency. The investigation proves that the teaching design based on artificial intelligence technology can be applied to teaching activities, thereby improving the learning efficiency of students and the teaching efficiency of teachers.

scholarly journals Education Informatization 2.0 in China: Motivation, Framework, and Vision

2020 ◽  
pp. 209653112094492
Author(s):  
Shouxuan Yan ◽  
Yun Yang
Keyword(s):  
Artificial Intelligence ◽  
Action Plan ◽  
Educational Resources ◽  
National Network ◽  
Education Governance ◽  
Education Service ◽  
New Models ◽  
Teachers And Students ◽  
Artificial Intelligence Technology ◽  
New Education

Purpose: This article aims to shed light on a latest education informatization policy blueprint in China, titled Education Informatization 2.0 Action Plan, which was promulgated by the Ministry of Education in China on April 18, 2018. Design/Approach/Methods: The study is an analytical policy review based on the policy documents, theoretical discussion, and development of practice. Findings: This new Chinese education informatization policy was driven by three factors: the promotion of education informatization 1.0 in China, the requirement of education modernization toward 2035, and the response to “Wisdom Education.” The framework for action can be summarized as “One Goal, Three Tasks, and Eight Actions.” The main features involve innovation-driven development rather than technology-driven development, committing to the expansion of digital educational resources rather than the digital presentation of textbooks, and aiming at improving teachers and students’ information literacy rather than the applied skills of information technology. The future vision of the plan involves building new models on talent cultivation, education service, and education governance. The new models on talent cultivation involve establishing “Wisdom Teaching” mode, learning mode, and intelligent learning environment supported by artificial intelligence technology. The new education service models entail building the admission and sharing mechanism of quality educational resources based on National Network for Education and the public service platform and system for educational resources by means of the cloud computing and artificial intelligence. The new education governance models involve achieving precise, flat, and humanized education governance. Originality/Value: This article entails expounding the motivation, framework for action, main features, and vision of the education informatization 2.0 in China, which will be helpful for learning and understanding the current background, stage, and future path of China’s education informatization.

Measuring Music Education

2016 ◽  
Vol 64 (3) ◽  
pp. 274-293 ◽  
Author(s):  
Lauren Kapalka Richerme
Keyword(s):  
Music Education ◽  
Assessment Practices ◽  
Policy And Practice ◽  
Boundary Making ◽  
Teachers And Students ◽  
Karen Barad ◽  
Music Model ◽  
Active Nature ◽  
Music Education Policy ◽  
Contemporary Education

Despite substantial attention to measurement and assessment in contemporary education and music education policy and practice, the process of measurement has gone largely undiscussed in music education philosophy. Using the work of physicist and philosopher Karen Barad, in this philosophical inquiry, I investigated the nature of measurement in music education while concurrently exploring the assumptions underlying documents related to the proposed music Model Cornerstone Assessments. First, Barad’s concepts of reflection and diffraction reveal the false assumption that measurement captures rather than alters and produces musical experiences. Second, measurement apparatuses are explained as boundary-making practices. Third, the limits of measurement apparatuses are explored through Barad’s assertions about experimental inclusions and exclusions and Lyotard’s concept of the differend, and these limits are used to problematize the ambitious, value-laden discourse of documents related to the music Model Cornerstone Assessments. Finally, through Barad’s concept of intra-action, measurement is reinterpreted as a process through which “teacher” and “student” emerge. Music education policymakers, teachers, and students might adopt language emphasizing the intra-active nature of measurement and empower themselves to critique and reimagine existing measurement apparatuses and their measurement and assessment practices.

Intelligent Evaluation System of Animation Works Based on ELP

2013 ◽  
Vol 756-759 ◽  
pp. 2416-2421
Author(s):  
Jun Li Zhao
Keyword(s):  
Artificial Intelligence ◽  
Digital Media ◽  
Evaluation System ◽  
Comprehensive Evaluation ◽  
Qualitative Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Teachers And Students ◽  
Artificial Intelligence Technology ◽  
E Learning

In digital media area, exchange and evaluation of the animation works has become a prominent problem. In this paper, we develop an intelligent evaluation system of animation works based on E-learning portfolio, which can record students animation works and learning files in digitized form. It achieves a combination of quantitative evaluation and qualitative evaluation using fuzzy comprehensive evaluation and artificial intelligence technology. It can not only generate the scores, but also generate remarks automatically. This will facilitate teachers and students to exchange and evaluate animation works, and improve the quality of evaluation.

scholarly journals Using Artificial Intelligence for the Construction of University Physical Training and Teaching Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Liang Li ◽  
Lu Zhang ◽  
Su Zhang
Keyword(s):  
Artificial Intelligence ◽  
Educational System ◽  
Physical Training ◽  
Teaching Quality ◽  
Educational Systems ◽  
Teachers And Students ◽  
Artificial Intelligence Technology ◽  
Theoretical Application ◽  
And Training ◽  
Training Efficiency

The combination of education and artificial intelligence is the developmental direction of future educational systems. Through the participation of artificial intelligence, an educational system with sensibility and computer rationality can be created. Albeit the advantages and importance of this education system are beyond doubt, nevertheless, at present, the combination of artificial intelligence and education is still in its infancy. This is because the theoretical application, equipment research, and development are neither perfect nor up to the required standard. The research points out the reality that artificial intelligence technology has been widely used in sports and analyzes the specific application of artificial intelligence in sports. In this paper, the knowledge of artificial intelligence is combined with the physical training and teaching in colleges and universities, and an educational system is developed to guide teachers' and students’ training, which improves the teaching quality and training efficiency.

scholarly journals The Potential Energy of Artificial Intelligence Technology in University Education Reform from the Perspective of Communication Science

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yinghui Yang
Keyword(s):  
Artificial Intelligence ◽  
Online Education ◽  
Potential Energy ◽  
Education Reform ◽  
Science Communication ◽  
College Education ◽  
Machine Learning Algorithms ◽  
Teachers And Students ◽  
Artificial Intelligence Technology ◽  
Education Platform

In today’s rapid development of science and technology, science is everywhere in people’s lives, and science communication is everywhere. Science and communication are not only not far away but also very close. Since machine learning algorithms with deep learning as a theme have achieved great success in the fields of vision and speech recognition, as well as the large amount of data resources that cloud computing, big data, and other technologies can provide, the development speed of artificial intelligence has been greatly improved, and it has had a significant impact in various industries in the society, and the country has put forward the concept of intelligent education for this purpose. However, there have been few systematic discussions on the combination of artificial intelligence with education and teaching. Therefore, this article uses artificial intelligence technology to study the potential energy space of artificial intelligence technology in college education reform from the perspective of science communication, designs and implements an online education platform for colleges and universities, and conducts a trial of platform use in a domestic college and universities. Some teachers and students conduct a satisfaction survey after the platform is used, and the conclusions show that whether in the teacher group or the student group, most teachers and students are relatively satisfied with the online education platform designed in this article. The reform of college education includes many aspects. This article is a research study on the form of college education, changing from traditional offline education to online platform education. This research can provide a certain reference for the reform of college education.

scholarly journals Optimization model: the innovation and future of e-ecotourism for sustainability

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fauziah Eddyono ◽  
Dudung Darusman ◽  
Ujang Sumarwan ◽  
Fauziah Sunarminto
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Sustainable Tourism ◽  
System Modeling ◽  
Secondary Data ◽  
Simulation Models ◽  
Content Type ◽  
Causal Loop ◽  
Artificial Intelligence Technology ◽  
Intelligent Technology

PurposeThis study aims to find a dynamic model in an effort to optimize tourism performance in ecotourism destinations. The model structure is built based on competitive performance in geographic areas and the application of ecotourism elements that are integrated with big data innovation through artificial intelligence technology.Design/methodology/approachData analysis is performed through dynamic system modeling. Simulations are carried out in three models: First, existing simulation models. Second, Scenario 1 is carried out by utilizing a causal loop through innovation of big data-based artificial intelligence technology to ecotourism elements. Third, Scenario 2 is carried out by utilizing a causal loop through big data-based artificial intelligence technology on aspects of ecotourism elements and destination competitiveness.FindingsThis study provides empirical insight into the competitiveness performance of destinations and the performance of implementing ecotourism elements if integrated with big data innovations that will be able to massively demonstrate the growth of sustainable tourism performance.Research limitations/implicationsThis study does not use a primary database, but uses secondary data from official sources that can be accessed by the public.Practical implicationsThe paper includes implications for the development of intelligent technology based on big data and also requires policy innovation.Social implicationsSustainable tourism development.Originality/valueThis study finds the expansion of new theory competitiveness of ecotourism destinations.

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