基于人工智能的高校英语阅读学习管理系统设计

IF 1.5 Q2 COMPUTER SCIENCE, THEORY & METHODS
Fengxia Zhang, Minghong She
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引用次数: 4

摘要

大学英语阅读学习是大学英语学习的一种有效手段。然而,目前高校的英语阅读学习平台大多只是将不同的英语书籍以电子形式放在平台上供学生阅读,这就造成了阅读的盲目性。本文基于人工智能算法,根据高校英语阅读与学习管理的需求,构建模型功能模块,并基于人工智能算法实现系统功能。此外,根据上述个性化学习模型的设计原则和个性化网络学习的特点,本文设计了一个基于有意义学习理论的个性化学习系统。此外,本文还对模型的性能进行了验证和分析。研究结果表明,本文提出的模型具有一定的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of English reading and learning management system in college education based on artificial intelligence
English reading learning in college education is an efficient means of English learning. However, most of the current English reading learning platforms in colleges and universities only put different English books on the platform in electronic form for students to read, which leads to blindness of reading. Based on artificial intelligence algorithms, this paper builds model function modules according to the needs of English reading and learning management in college education and implements system functions based on artificial intelligence algorithms. Moreover, according to the above design principles of personalized learning model and the characteristics of personalized network learning, this paper designs a personalized learning system based on meaningful learning theory. In addition, this article verifies and analyzes the model performance. The research results show that the model proposed in this paper has a certain effect.
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来源期刊
CiteScore
2.80
自引率
23.10%
发文量
31
期刊介绍: The International Journal of Fuzzy Logic and Intelligent Systems (pISSN 1598-2645, eISSN 2093-744X) is published quarterly by the Korean Institute of Intelligent Systems. The official title of the journal is International Journal of Fuzzy Logic and Intelligent Systems and the abbreviated title is Int. J. Fuzzy Log. Intell. Syst. Some, or all, of the articles in the journal are indexed in SCOPUS, Korea Citation Index (KCI), DOI/CrossrRef, DBLP, and Google Scholar. The journal was launched in 2001 and dedicated to the dissemination of well-defined theoretical and empirical studies results that have a potential impact on the realization of intelligent systems based on fuzzy logic and intelligent systems theory. Specific topics include, but are not limited to: a) computational intelligence techniques including fuzzy logic systems, neural networks and evolutionary computation; b) intelligent control, instrumentation and robotics; c) adaptive signal and multimedia processing; d) intelligent information processing including pattern recognition and information processing; e) machine learning and smart systems including data mining and intelligent service practices; f) fuzzy theory and its applications.
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