Analyzing Teaching Effects of Blended Learning With LMS: An Empirical Investigation

The development of modern science and technology has had a significant impact on the traditional teaching methods of higher education institutions. The utilization of technology-mediated learning approaches has provided universities and other higher education institutions with an opportunity to promote blended learning. This research aims to empirically analyze the learning effects of blended learning using the machine learning algorithm CART (Classification and Regression Tree) and regression analysis models, with Learning Management Service (LMS, using Rain Classroom as an example in this paper). It explores whether different dimensional factors in blended learning on the Rain Classroom platform are associated rules for English proficiency, while specifically focusing on their correlation with scores in the English proficiency test (CET-4, College English Test Band 4). The study identifies which association rules within these factors contribute to the improvement of students’ scores. Additionally, it classifies students based on their scores and learning effects using a decision tree structure. Based on the classification results, a multiple linear regression model is constructed to quantitatively assess the learning effects of using Rain Classroom for English language learning among students with different English proficiency levels. These analytical results enable educators, during the later stages of using Rain Classroom for blended learning, to adjust teaching decisions based on individual situations of students in different categories. They can design teaching materials and activities tailored to each student, leveraging the tool capabilities of Rain Classroom as an LMS and the advantages of blended learning. This approach aims to create more profound and meaningful learning experiences for students, assisting them in achieving educational goals and unlocking their full potential in academics.