z-logo
open-access-imgOpen Access
Classroom Teaching Performance Evaluation Model Guided by Big Data and Mobile Computing
Author(s) -
Li Meng,
Jinlong Zhu,
Liying Wang
Publication year - 2022
Publication title -
wireless communications and mobile computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.42
H-Index - 64
eISSN - 1530-8677
pISSN - 1530-8669
DOI - 10.1155/2022/2084423
Subject(s) - benchmarking , computer science , big data , field (mathematics) , process (computing) , performance management , data science , knowledge management , engineering management , data mining , management , mathematics , pure mathematics , engineering , economics , operating system
Performance management has evolved rapidly in recent years and has become increasingly dominant in enterprise applications, whereas its application in the field of education has progressed slowly. Because performance management focuses on improving business performance and empowering employees, implementing it in schools helps students develop practical skills. This research focuses on evaluating classroom teaching performance using a big data and mobile computing-driven model. In addition, in the era of educational big data, this paper investigates the general process by which teachers acquire, analyze, and use educational data to improve teaching performance. The data mining method and mobile data capture are organically integrated into the benchmarking analysis to evaluate the classroom teaching performance of local universities, enriching the teaching management theories and methods of local universities. The findings show that benchmarking analysis can produce more meaningful results and provide new data for improving teaching management quality.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom