Learning Effect Evaluation of Online Course Based on Linear Regression Analysis

Online courses have been developed rapidly due to their advantages of being free from time and space constraints and sharing, and have been widely used in higher education. Improving the learning effect of online courses is also one of the difficulties of research in the era of big data. Taking the automobile theory online course on the Zhihuishu platform as an example, we analyze the influencing factors of online courses learning and establish a linear regression prediction model with correlation analysis and linear regression methods by selecting and extracting the data of students' main learning behaviors in the learning process. The results show that the number of student online course logins, the number of interactions. and test scores are the key indicators to predict their learning performance. On this basis, countermeasures and suggestions are put forward to improve the learning effect of online courses.