Construction of a learning behaviour tracking analysis model for a MOOC online education platform
by Peng Zhang; Wei Wang; Chui-Zhen Zeng
International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL), Vol. 30, No. 2, 2020

Abstract: Aiming at the problems of low extraction accuracy, high missed detection rate and low learning efficiency of existing learning behaviour tracking analysis models, a learning behaviour tracking analysis model of MOOC online education platform based on XAPI and Bayesian fuzzy rough set is established. Firstly, the learning behaviour is stratified, then the learning behaviour of online education platform and its correlation with learning effect are analysed, and the learning behaviour indicators are determined. Finally, the learning behaviour tracking analysis model based on Bayesian fuzzy rough set is established. The experimental results show that the accuracy of learning behaviour extraction of the model is always above 93%, and the accuracy is high; the rate of missing detection is between 1% and 4%, and the rate of missing detection is low; the maximum improvement of learning efficiency is 14.8%, and the students' learning efficiency is high, which verifies the validity of the model.

Online publication date: Thu, 02-Apr-2020

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