Predictive Modeling of Academic Performance of Online Learners Based on Data Mining

Abstract

This paper conducts a methodological research on the academic performance of online learners. This paper applies classification models commonly used in data mining algorithms, such as random forests based on decision trees, support vector machines, neural networks, K nearest neighbor algorithms, etc., combined with data mining tools S software and statistical analysis tools, and analyzes the University of Finance and Economics Tools scores of online learners in the class of 2019. It studies the important factors that affect the academic performance of college students' online learners, and uses these factors to predict students' academic performance. Based on the distance calculation method in mathematics, the article separately studied the application of Euclidean distance correlation analysis algorithm and correlation coefficient correlation algorithm in curriculum relevance, and compared several correlation algorithms. Experimental research results show that in the era of big data, learners will accumulate a large amount of structured and unstructured data during online learning. We can explore the influencing factors of online learners' academic performance through data mining technology, and we can also use machine learning to automatically learn from the data to the academic performance prediction model.