A Prediction Strategy for Academic Records Based on Classification Algorithm in Online Learning Environment

With the development of the Internet and the big data technology, online education is growing vigorously. However, a large amount of data is generated in the online learning environment subsequently, in which the learning behavior data of the learner is very large and there is no good method to use these data. How to analyze and use these data and find the relationship between these data and learning effect has been a hot area of research in recent years. Online learning prediction strategy of academic records is proposed in the paper, which by calculating the correlation coefficient between data attributes and academic records firstly, and then using the correlation analysis to determine the impact of the study results, and finally using the classical classification algorithm of machine learning for classification prediction, explores the rules for online learning data based on the machine learning technology. In the end, the strategy has been proved to be effective in strengthening and improving the analysis and utilization of online learning data through experiments.