skip to main content
10.1145/3585967.3585984acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicwcsnConference Proceedingsconference-collections
research-article

Network Autonomous Learning Monitoring System Based on SVM Algorithm

Published: 19 April 2023 Publication History

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.

References

[1]
S.X. Zheng, “Course Selection Management System Based on JSP and SQL Server”, China Computer & Communication, no. 15, pp. 114-116, 2020.
[2]
Y.X. Yin, Y.L. He, Y. Li, “Development of University Grant Manangement System Based on B/S Framework and SQL Server Database”, Microcomputer Application, Vol. 36, no. 12, pp. 53-55, 2020.
[3]
Q. Xu, Y.D. Li, Y.Z. Wang, “Design of university information resource management system based on SQL Server”, Modern Electronics Technique,Vol. 43, no. 2, pp. 115-118, 2020.
[4]
D. Chad. “Practice Identification of SQL Injection Vulnerabilities”. US-CERT, vol. 28, no.1, pp. 1-15, 2016.
[5]
Rafael Caballero, J Luzon-Martin, A Tenorio-Fornes “Test-Case Generation for SQL Nested Queries with Existential Conditions”, Electronic Communications of the Easst, vol. 55, no. 4, pp.55-57, 2012.
[6]
F. Tian, “Research on comprehensive quality evaluation of college students based on support vector machine”, Microcomputer application, vol. 37, no. 5, pp. 65-68, 2021.
[7]
Y.J. Wang, H.Y. Lin, L.L. She, C.Y. Li 2022 International Conference on Education, Network and Information Technology (ICENIT), https://doi.10.1109/ICENIT57306.2022.00017.
[8]
X.J. Wei, “Research on student achievement evaluation method based on cluster analysis”, Think tank Era, no. 11, pp. 203-204, 2020.
[9]
W.H. Xi, “Research on dynamic early warning system of Ideological and political education based on Improved SVM algorithm”, Microcomputer application, vol. 36, no. 9, pp. 27-31, 2020.
[10]
L.H. Hao, “Design of network public opinion monitoring system based on SQL Server”, Electronic Design Engineering, vol. 28, no.7, pp. 59-63, 2020.
[11]
Y.B. Sun, Z.K. Pu, Y.H. Xu, B.Hu, “Comment emotion analysis model using Sd-Ls-Svm algorithm”, Software Guide,vol.20, no.4,pp.43-48, 2021.

Cited By

View all
  • (2024)Classification of Short Noisy TextProceedings of the International Conference on Computer Systems and Technologies 202410.1145/3674912.3674935(227-231)Online publication date: 14-Jun-2024

Index Terms

  1. Network Autonomous Learning Monitoring System Based on SVM Algorithm

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    icWCSN '23: Proceedings of the 2023 10th International Conference on Wireless Communication and Sensor Networks
    January 2023
    162 pages
    ISBN:9781450398466
    DOI:10.1145/3585967
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 19 April 2023

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. SQL Server
    2. SVM
    3. performance analysis
    4. system design

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    icWCSN 2023

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)8
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 16 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Classification of Short Noisy TextProceedings of the International Conference on Computer Systems and Technologies 202410.1145/3674912.3674935(227-231)Online publication date: 14-Jun-2024

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media