An evaluation method of English online learning behaviour based on feature mining Online publication date: Wed, 01-Mar-2023
by Chao Han
International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL), Vol. 33, No. 2/3, 2023
Abstract: In order to overcome the problems of low precision of feature retrieval and poor clustering effect of traditional online learning behaviour evaluation methods, this paper designed an English online learning behaviour evaluation method based on feature mining. Firstly, the unbiased estimation theory is used to quantitatively sample English online learning behaviour. After data preprocessing, the clustering centre is divided. The data is allocated to different clusters through clustering processing, and the feature mining results are obtained through iteration. Then the evaluation index system is constructed, and the final evaluation results are obtained on the basis of the grey interval clustering treatment of the evaluation index. According to the test results, the retrieval accuracy of behaviour features of method of this paper is closer to one, and its clustering effect on different online learning behaviour features is good, which proves that it has achieved the design expectation.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
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:
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
Our Blog 
Follow us on Twitter 
Visit us on Facebook