Teaching Performance Evaluation in Smart Campus

作     者：Xu, Xin Wang, Yunsheng Yu, Shujiang 

作者机构：Hebei Univ Technol Sch Econ & Management Tianjin 300130 Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2018年第6卷

页      面：77754-77766页

核心收录：

主　　题：Smart campus teaching performance evaluation educational big Data analytic hierarchy process grey correlation degree TOPSIS algorithm 

摘      要：With the rapid development of modern teaching technology, the construction of smart campus has become the focus of modern college education reform. The application of technologies, such as the Internet of Things and big data, plays an important role in improving the teaching environment of colleges and universities, improving the utilization of teaching resources, and the flexibility of education. As an important part of campus activities, teaching performance evaluation scientifically and effectively utilizes teaching information and teacher and student interaction information to evaluate teachers teaching performance, which helps to motivate teachers work enthusiasm, improve teaching quality, and enhance school core competitiveness. This paper analyzes the salient features of smart campus from the perspectives of technology, business, and construction mode, and proposes a smart campus architecture model. According to the research content of teaching performance evaluation, the framework model of smart campus education data collection and storage platform is established, which provides a reference model for the construction of smart campus in colleges and universities. The evaluation of teaching performance in smart campus first analyzes the shortcomings of traditional evaluation methods and proposes the necessity of combining teaching performance evaluation with modern technology. Second, six principal components were determined using the PCA algorithm. Then, use the AHP to calculate the weights of each layer of the indicator set, avoiding the decision errors caused by subjective factors. Finally, the gray correlation degree is used to improve the TOPSIS algorithm for multi-objective decision analysis. The evaluation results of the AHP-TOPSIS teaching performance model are consistent with the actual situation. The application of the smart campus education data platform combined with the AHP and the gray correlation improvement TOPSIS algorithm is more targeted t