Network Autonomous Learning Monitoring System Based on SVM Algorithm

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Abstract

The network autonomous learning monitoring system is a subsystem of the learning quality monitoring system in the network education platform. Based on the training objectives of network education and the course learning objectives of learners, and on the basis of educational evaluation theory, it makes value judgments on learners' attitudes, knowledge and ability development level in the whole learning process. Through the planning, inspection, evaluation, feedback, control and adjustment of learners' learning activities, timely guide learners to correct their learning attitude, adjust their learning strategies, and effectively use learning resources and modern information technology means to carry out autonomous learning, so as to achieve the expected learning goals. The network self-learning monitoring system is based on the database created by SQL Server platform, supports C/S structure, has good scalability and usability, and is used to extract and analyze data. SVM algorithm is used to extract system features, which has the advantages of low system load, low response delay and good performance. An accurate network autonomous learning monitoring system model is constructed. After system test, the network autonomous learning monitoring system based on SVM algorithm has high data analysis ability, easy to understand, easy to maintain, reasonable structure and easy to use, which meets the needs of learners. Using SVM algorithm for feature extraction, the evaluation performance of the algorithm is improved by more than 3.2%. When learners learn in the system, the system load is small, the response delay is low, and the performance is good. It is an accurate network autonomous learning monitoring system.

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