Application of E-Learning Assessment Based on AHP-BP Algorithm in the Cloud Computing Teaching Platform

Chuanfu Hu

Abstract


With the increasing development of the Internet, students began to learn quickly through information technology and Internet technology. E-learning is not only an important part of China's higher education, but also is an important means to improve the quality of education, expand the scale of education, deepen educational reform, and realize the educational equity among different classes in society. However, the problem of E-learning is becoming increasingly obvious, which is how to ensure students' effective learning, an important topic in the field of online education. In order to solve this problem, we first build an E-learning platform based on cloud computing, and introduce the software interface and function. The platform is different from the traditional teaching platform, having a strong interaction. Secondly, the data mining technology is used to analyze and collect the record data in the process of E-learning, so as to establish the evaluation system of the E-learning comprehensive capability. Then, we propose an E-learning ability evaluation model based on an AHP-BP neural network algorithm. We use a BP neural network to predict the evaluation results of 1000 students, and compare them with the results obtained by the AHP method, so as to illustrate the effectiveness of the method proposed in this article. Finally, through experiments we can see that the prediction results of the BP neural network and the evaluation results obtained by the AHP method are similar. This proves the effectiveness of the AHP method on the evaluation of the E-learning comprehensive capability. At the same time, the BP neural network method can be used to deal with a large number of evaluation results, which can save time, without losing accuracy.

Keywords


Cloud computing; E-learning; AHP-BP algorithm; Assessment

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Copyright (c) 2017 Chuanfu Hu


International Journal of Emerging Technologies in Learning. ISSN: 1863-0383
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