Research on evaluation of MOOC distance learning effect based on a BP neural network
by Jiefeng Wang; Henry Loghej
International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL), Vol. 32, No. 3, 2022

Abstract: At present, the evaluation method of the massive open online course (MOOC) learning effect has the problems of large evaluation error and low evaluation efficiency. Therefore, this paper proposes an evaluation method based on a back propagation (BP) neural network. We select the evaluation index, and use the grey correlation analysis method to optimise the evaluation index, then use the entropy weight method to calculate the index weight. The BP neural network model is constructed, which is used as the evaluator of the MOOC distance learning effect. The sample data to be identified is input to minimise the accumulated evaluation residual, and the output is the evaluation result of the MOOC distance learning effect. After testing, the error rate of the design method is only 0.9259%, and the evaluation time is always less than 1.

Online publication date: Mon, 11-Jul-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com