The application of artificial intelligence in education has been given great hope. Combined with the relevant Tensor Flow analysis and education informatization above, this study believes that Tensor Flow can be used in education, big data analysis, learning and recommendation systems, teaching games, and educational robots in the field of education, helping the design and implementation of intelligent teaching applications. .
1 Education Big Data Analysis
The major types of educational big data include text data, speech data, image data, video data, and behavioral data. A large number of complicated learning data needs effective methods for analysis to generate better educational value. This study summarizes the main applications of Tensor Flow in the field of education big data analysis, as shown in Table 2.
2 personalized learning recommendation system
In the field of education, personalized learning recommendations refer to learners' personalized and adaptable courses and resources based on their specific learning situations. It is an effective way to improve learning outcomes in the era of big data and information intelligence. In the study of recommendation systems, "Wide & Deep Learning" is an effective recommendation algorithm integrated in Tensor Flow by using deep neural networks (for induction) and wide linear models (for memory ) Jointly conducted training and achieved good recommendations.
This study designed a universal personalized learning recommendation system based on width and depth learning.
2 shows. The system is based on learner information, learning resource access behavior and other data, recommending relevant learning resources for learners. The recommendation part of the system includes two modules for candidate generation and sorting. First, the candidate generation module generates high-relevance candidate set data through machine learning or artificially defined rules; subsequently, the sorting module uses “width and depth learningâ€. The algorithm is recommended to sort the candidate set data and finally produce the recommendation result.
3 teaching games
The application of artificial intelligence in teaching games mainly involves two aspects: 1 The use of artificial intelligence to operate games, such as the system designed by the Deep Mind team has defeated the world's top players in multiple games, proving that artificial intelligence can master game skills through deep learning. And get the same manipulative power as humans, and even more than humans in some respects; 2 Integrate artificial intelligence elements into game design and development, such as game level design and non-player character (NPC) Games and more. At present, there are few teaching games that introduce artificial intelligence elements. This is a key issue that needs to be considered in the design of future instructional games.
question. The deep learning algorithm provided by Tensor Flow can be applied to teaching games, and the design of intelligent elements in games can be achieved through proper model selection and training. How to incorporate more and more innovative artificial intelligence elements in teaching games is a new topic faced by instructional game designers. This requires designers to truly understand the relationship between players and artificial intelligence, and fully consider the needs and labor of games. The intelligent application eventually designed excellent teaching game works.
4 Educational robots
Artificial intelligence is the key technology for the development of educational robots in the future, and robots are also one of the ultimate applications of artificial intelligence. The three parts of perception, cognition, and behavior control in educational robots are realized with the support of machine learning and deep learning. They can complete visual, auditory, emotional, reasoning, operation, and interaction. Tensor Flow provides intelligent platform support for building robots. Through powerful deep learning algorithms, it promotes the development of open educational robotic system platforms and robotic application software. This study analyzes the core functions of educational robots, behavior descriptions, and technical support for Tensor Flow, as shown in Table 3.
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