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International Journal of Big Data Intelligent Technology, 2021, 2(1); doi: 10.38007/IJBDIT.2021.020102.

Data Analysis Model for Training Self-Learning Ability of Applied Undergraduate Students in Large Data Environment


Xiufang Wu and Xiaoying Sun

Corresponding Author:
Xiufang Wu

Nanchang Institute of Science and Technology, Nanchang 330108, China


In the future society and development, the talents needed are not only those with strong theoretical knowledge, but also those with strong self-study ability and self-quality. Therefore, it is very important to strengthen students' self-learning ability. At present, the comprehensive quality of college students is not compatible with the training of applied talents, and there are many drawbacks. Under the background of large data, this paper designs and builds a data analysis model for the training of students with self-learning ability in Application-oriented Undergraduate Colleges and universities by using partition clustering algorithm, and selects an application-oriented undergraduate college in our city as the research object to verify. In the study, we call the original model used by the University as model A, and use partition clustering algorithm to improve the original model to form a new model called model B. We compare the differences of operation time and fitness of the two models under different data quantities. The experimental results show that the fitness of model B is 13% higher than model A when the data quantity value is 80 and 34% higher than model A when the data quantity value is 160.Therefore, the new model improved by partition clustering algorithm can improve the accuracy although it takes a long time to operate.


Self Study, Application Oriented Undergraduate Colleges, Big Data, Data Analysis Model

Cite This Paper

Xiufang Wu, Xiaoying Sun. Data Analysis Model for Training Self-Learning Ability of Applied Undergraduate Students in Large Data Environment. International Journal of Big Data Intelligent Technology (2021), Vol. 2, Issue 1: 10-17. https://doi.org/10.38007/IJBDIT.2021.020102.


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