Digital Management of Teaching Cases in Colleges and Universities Based on Cluster Analysis

被引:0
作者
Wu R. [1 ]
Wang J. [1 ]
机构
[1] School of Literature, Cangzhou Normal University, Cangzhou
关键词
cluster analysis; teaching cases in colleges and universities; topic of digital management teaching case;
D O I
10.3991/ijet.v18i10.40241
中图分类号
学科分类号
摘要
The necessity of digital management of teaching cases is mainly reflected in the aspects of improving teaching quality, facilitating retrieval and storage, achieving cross-platform sharing, conducting real-time updating, saving resources, and carrying out data analysis and evaluation. However, there are still some defects in the existing management models or methods, which lead to difficulties in data storage and retrieval, and affect the utilization efficiency of teaching resources. To this end, this article takes the Chinese language and literature major as an example, and studies the digital management of teaching cases in colleges and universities based on cluster analysis. First of all, it quantifies the quality of digital management of teaching cases, enables resource demanders to accurately select digital resources of teaching cases that meet their teaching or learning needs through the evaluation index of service quality when faced with diversified digital resources of teaching cases in colleges and universities. The clustering algorithm is used to mine potential topics and patterns in teaching cases, which improves the classification efficiency of teaching cases and enables educators to have a deeper understanding of teaching content and educational needs. It uses “absolute index”, “incremental index” and “fluctuation index” to construct the similarity measurement distance function of the basic attributes of teaching cases in colleges and universities and uses the Ward method based on variance analysis to classify the characteristics of teaching cases in colleges and universities. Experimental results verify the effectiveness of the proposed method. © (2023), (Kassel University Press GmbH). All Rights Reserved.
引用
收藏
页码:264 / 279
页数:15
相关论文
共 23 条
[1]  
Lv Z., Wang J., The exploring on the university student honor management system based on enterprise refined management under the background of big data, Journal of Physics: Conference Series, 1453, 1, (2020)
[2]  
Ong M.I.U., Ameedeen M.A., A design theory for student self-service university management system, Journal of Physics: Conference Series, 1529, 5, (2020)
[3]  
Fei G., On the design of student employment module in higher education management system based on genetic algorithm, Application of Big Data, Blockchain, and Internet of Things for Education Informatization: Second EAI International Conference, pp. 232-242, (2023)
[4]  
Aljarallah N.A., Dutta A.K., Alsanea M., Sait A.R.W., Intelligent student mental health assessment model on learning management system, Computer Systems Science and Engineering, 44, 2, pp. 1853-1868, (2023)
[5]  
He S., Li Y., Design and application of college student management system based on big data technology, Wireless Communications and Mobile Computing, 2023, (2023)
[6]  
Kurniawan Y., Bhutkar G., Alfarrel J.F., Theo R.R., Denanto R.K., Nangka T.P.P., Jericho I., Unlocking student’s preference on two BINUS mobile learning management system, 2020 International Conference on Information Management and Technology (ICIMTech), pp. 972-976, (2020)
[7]  
Darko C., Jagger D., How can we manage the blackboard learning management system to enhance student’s learning, Proceedings of the 2020 4th International Conference on Deep Learning Technologies, pp. 49-54, (2020)
[8]  
Song Y.N., Luan Z.Q., Function design optimization of learning management system (LMS) based on student perspective-case study of canvas application university of Colorado Denver, Journal of Physics: Conference Series, 1621, 1, (2020)
[9]  
Mozahem N.A., Using learning management system activity data to predict student performance in face-to-face courses, International Journal of Mobile and Blended Learning (IJMBL), 12, 3, pp. 20-31, (2020)
[10]  
Omar N., Ariff M.A.M., Shah A.F.I.M., Mustaza M.S.A., Estimating synthetic load profile based on student behavior using fuzzy inference system for demand side management application, Turkish Journal of Electrical Engineering and Computer Sciences, 28, 6, pp. 3193-3207, (2020)