Evaluation of the sustainable development of college students' English autonomous learning ability in a mobile learning environment Online publication date: Thu, 08-Feb-2024
by Wei Liu
International Journal of Sustainable Development (IJSD), Vol. 27, No. 1/2, 2024
Abstract: Assessing the sustainable development of college students' autonomous learning ability can help to understand their comprehensive learning ability. However, existing evaluation methods have problems such as time-consuming data mining, low evaluation accuracy, and poor student performance. Therefore, an evaluation method for the sustainable development of college students' autonomous learning ability in English in a mobile learning environment is proposed. Firstly, construct an evaluation index system for the sustainable development of college students' autonomous English learning ability. Secondly, the evaluation index data is mined using a distributed mining framework. Finally, the sustainable development level of college students' autonomous English learning ability is determined by determining the calibrated clustering weight, grey clustering weight, and grey vector. The experimental results show that the maximum data mining time of this method is 0.9 seconds, the evaluation accuracy varies between 94% and 97%, and the average English learning achievement of students is 86.4.
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 Sustainable Development (IJSD):
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