Evaluation of the sustainable development of college students' English autonomous learning ability in a mobile learning environment

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.