Research and Application of Computer Network Course Teaching Based on Deep Learning
DOI:
https://doi.org/10.53469/jtpes.2024.04(12).03Keywords:
Deep learning, Advanced courses, Computer network, Professional comprehensive practice, Artificial intelligenceAbstract
With the introduction of new engineering disciplines, the classroom teaching methods for computer related majors have shifted from traditional theoretical teaching to advanced classroom teaching that cultivates students' thinking development. The focus is on cultivating students' higher-order thinking abilities, with the core teaching philosophy of improving hands-on skills. This includes exercises in critical and creative thinking, which can enhance students' cognitive and non cognitive abilities. Effectively combining deep learning with advanced classrooms and applying it to comprehensive practical courses in new engineering disciplines such as computer network courses can enhance students' ability to solve complex problems hands-on. For example, incorporating new technologies related to artificial intelligence (AI) into the experimental content of network security classrooms, and using various virtualization technologies to build network simulation platforms to verify the effectiveness of technology applications. These methods can fully stimulate students' interest in learning, allowing them to complete practical course content in an immersive learning manner. Through graded and progressive experimental stages, students can gradually improve their hands-on abilities. Through effective course practice, it has been proven that this method is not only beneficial for teachers to have more detailed control over the teaching process, but also to improve students' classroom participation and exercise their hands-on abilities, fully improving the teaching quality of comprehensive practical training courses.
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