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Advanced Hybrid Information Processing. 6th EAI International Conference, ADHIP 2022, Changsha, China, September 29-30, 2022, Proceedings, Part II

Research Article

Classified Evaluation Model of Online Teaching Quality in Colleges and Universities Based on Mobile Terminal

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-28867-8_24,
        author={Lei Han and Qiusheng Lin},
        title={Classified Evaluation Model of Online Teaching Quality in Colleges and Universities Based on Mobile Terminal},
        proceedings={Advanced Hybrid Information Processing. 6th EAI International Conference, ADHIP 2022, Changsha, China, September 29-30, 2022, Proceedings, Part II},
        proceedings_a={ADHIP PART 2},
        year={2023},
        month={3},
        keywords={Mobile terminal Teaching quality Classification evaluation Model construction BP neural network},
        doi={10.1007/978-3-031-28867-8_24}
    }
    
  • Lei Han
    Qiusheng Lin
    Year: 2023
    Classified Evaluation Model of Online Teaching Quality in Colleges and Universities Based on Mobile Terminal
    ADHIP PART 2
    Springer
    DOI: 10.1007/978-3-031-28867-8_24
Lei Han1,*, Qiusheng Lin2
  • 1: Department of Primary Education, Yuzhang Normal University
  • 2: Guangzhou Huali College
*Contact email: hanlei10023@163.com

Abstract

At the moment, only students’ academic achievements or questionnaire statistics are used to assess teaching quality. Its accuracy and efficiency are limited when applied to online evaluation of teaching quality. To address the aforementioned issues, this work investigates and develops an online teaching quality categorization evaluation model based on a mobile terminal. The crawler crawls the necessary data after collecting the evaluation of teaching excellence data on the mobile terminal. The online teaching quality categorization and assessment dimension is built based on data climbing, allowing for multi-dimensional teaching quality evaluation. On this basis, the teaching quality classification and evaluation index system is constructed. An adaptive variant genetic algorithm was used to improve the BP neural network and establish a classification and model for assessing teaching quality. The model test results show that the average evaluation accuracy of the model is 88.16%, and the model has good evaluation efficiency and stability.

Keywords
Mobile terminal Teaching quality Classification evaluation Model construction BP neural network
Published
2023-03-22
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-28867-8_24
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