Monitoring Method of Ideological and Political Class Learning Status Based on Mobile Learning Behavior Data

被引:0
|
作者
Wang, Yonghua [1 ]
机构
[1] Sanya Aviat & Tourism Coll, Sanya 572000, Peoples R China
来源
ADVANCED HYBRID INFORMATION PROCESSING, ADHIP 2022, PT I | 2023年 / 468卷
关键词
Mobile terminal; Learning behavior; Ideological and political lesson; State monitoring;
D O I
10.1007/978-3-031-28787-9_8
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In order to improve the quality of ideological and political education, a method for monitoring the learning status of ideological and political courses based on mobile learning behavior data is proposed. Combined with mobile technology to collect ideological and political learning behavior characteristic data. According to the feature recognition results of the data, an accurate stu classification algorithm is designed, and an evaluation system for the learning status of ideological and political courses is constructed. Six characteristic actions in human poses are selected to study learning state classification. Realize the monitoring of the students' learning status in political courses. Finally, it is proved by experiments that the monitoring method of learning state of ideological and political courses based on mobile learning behavior data has high practicability and meets the research requirements.
引用
收藏
页码:95 / 106
页数:12
相关论文
共 50 条
  • [31] Teaching Effect Evaluation System of Ideological and Political Teaching Based on Supervised Learning
    Tian, Yihao
    Journal of Interconnection Networks, 2022, 22
  • [32] A Mobile Teaching Method of Ideological and Political Education in Colleges and Universities Based on Android Platform
    Peng, Yu
    Zeng, Yan
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2022, 2022
  • [33] Evolutionary learning of a fuzzy behavior based controller for a nonholonomic mobile robot in a class of dynamic environments
    Nanayakkara, DPT
    Watanabe, K
    Kiguchi, K
    Izumi, K
    JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2001, 32 (03) : 255 - 277
  • [34] An authentic learning based evaluation method for mobile learning in Higher Education
    Chiu, Po-Sheng
    Pu, Ying-Hung
    Kao, Chih-Chien
    Wu, Ting-Ting
    Huang, Yueh-Min
    INNOVATIONS IN EDUCATION AND TEACHING INTERNATIONAL, 2018, 55 (03) : 336 - 347
  • [35] Evolutionary Learning of a Fuzzy Behavior Based Controller for a Nonholonomic Mobile Robot in a Class of Dynamic Environments
    D. P. Thrishantha Nanayakkara
    Keigo Watanabe
    Kazuo Kiguchi
    Kiyotaka Izumi
    Journal of Intelligent and Robotic Systems, 2001, 32 : 255 - 277
  • [36] A Mobile Steganography Method Based on Deep Learning
    Liao X.
    Li Y.
    Ouyang J.
    Zhou J.
    Dai X.
    Qin Z.
    Hunan Daxue Xuebao/Journal of Hunan University Natural Sciences, 2022, 49 (04): : 18 - 25
  • [37] Positive and Unlabeled Learning for User Behavior Analysis Based on Mobile Internet Traffic Data
    Yu, Ke
    Liu, Yue
    Qing, Linbo
    Wang, Binbin
    Cheng, Yongqiang
    IEEE ACCESS, 2018, 6 : 37568 - 37580
  • [38] PKULAE: A Learning Attitude Evaluation Method Based on Learning Behavior
    Li, Deqi
    Zhu, Zhengzhou
    Zhang, Youming
    Wu, Zhonghai
    INTELLIGENT TUTORING SYSTEMS (ITS 2019), 2019, 11528 : 156 - 162
  • [39] Data-driven-based Predictive Optimal for a class of Iterative Learning Control by Q-learning method
    Li, Jinze
    Tian, Senping
    Peng, Yunjian
    Gu, Panpan
    2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS, 2023, : 1214 - 1220
  • [40] Electric vehicle charging status monitoring and safety warning method based on deep learning
    Gao D.
    Wang Y.
    Zheng X.
    Yang Q.
    Dianji yu Kongzhi Xuebao/Electric Machines and Control, 2023, 27 (07): : 122 - 132