Designing and Evaluating Learning Effectiveness of Blended Teaching and Learning of College English Based on BOPPPS Modeling

This paper applies the parallel K-means clustering algorithm of adaptive cuckoo search to mine the data related to BOPPPS blended English teaching and designs the evaluation index system of BOPPPS blended English teaching based on the teaching data. Then, it constructs the teaching effect evaluation model based on an improved BP neural network, completes the evaluation of the BOPPPS blended English teaching effect, optimizes the design of university English blended teaching based on the BOPPPS model and evaluates the learning effectiveness based on teaching experiments. The students in the experimental class were clustered into three categories before the experiment: “excellent students” (17.77%), “average students” (55.56%), and “failing students” (26.67%). 26.67%). Students in classes A and B increased by 1.22 and 0.16 points, respectively. In comparison, the progress of students in class A is more obvious than that of students in class B (P=0.026<0.05), indicating that there is a significant difference between the performance of class A and class B. The evidence suggests that BOPPPS blended English teaching can significantly enhance students’ performance, and the quality of instruction is superior.