Research On Evaluation Of Students Learning Online Based On Big Data Analysis

With the development of the Internet, education online has a large impact on human's life gradually. Online learning is characterized by its freedom from the traditional education with constraints of time and space so that students can study on the network as they wish. Online learning platform as a carrier of student self-learning provides a large amount of learning resources and learning tools. Therefore, in recent years, the study of learning platform has been on monitoring, analyzing and understanding students' learning process,improving learning efficiency and the way of providing students with a better learning environment. In the era of big data, method of big data analysis has been applied to each link of learning online by more and more researchers who devote themselves into the analysis of learning resources data, the solution to problems in the learning procedures and better service for the teachers and students with more comprehensive information. Learning evaluation, an important part of the learning process, also needs to use big data analysis method to make it result more scientific, more objective and more targeted.This paper proposes a modeling process framework for online learning assessment with the method of large data analysis, which is based on the research on current domestic and international learning and common data analysis methods and the actual learning evaluation objectives. Using the real data of students from Beijing University of Posts and Telecommunications College of Online Education, the model of predictive evaluation is used to evaluate students' learning outcomes. The evaluation enables the teacher to know whether the student can finish the course successfully after a course of teaching. With the help of the final evaluation, the student with trouble can be interfered according to the evaluation results from which the student's learning problem can be discovered and then offer the personalized 1 guide to the students. This study aims to pave the way for the personalized learning of teachers and the learning platform. The objective and scientific evaluation generated from the data analysis can provide information for teachers and students personalized guidance, also the foundation for personalized service on the adaptive learning platform that can reduce the burden on teachers.