Evaluation of Learning Effect Based on Online Data

With the development of Internet technology, online learning has gradually been widely used, which has further enriched the teaching methods. However, the indicators provided by online learning platforms to characterize students’ learning behaviors are still relatively small, it is hard to effectively evaluate students’ learning effects and provide personalized education for students. While students produce a large number of online data records during the learning process, if these data can be used to evaluate the learning effect of students and find indicators that are significantly related to the learning effect, it can help teachers know the learning status of students and provide them with more targeted guidance. The framework of this article includes: firstly collect online data of students’ learning, and determine major types of factors that affect the learning effect. Next, the original data is processed, and new features are constructed from the original data according to the factors that affect the learning effect. Then use several different methods to select the features that have the most significant impact on the learning effect, and use several regression methods to predict students’ academic performance. Finally, this article selects important features that are more relevant to learning effects, studies the relationship between these features.