Hybrid Teaching Feedback Mechanism and Efficiency Improvement Paths for Students’ Engagement in College English Learning in Guangdong Based on Learning Analytics

Abstract

Blended teaching mode has become the mainstream English teaching mode in Guangdong colleges and universities, how to make the communication between teachers and students more timely and in place is an important part of whether the blended teaching mode can give full play to its advantages. This paper generates a personalized knowledge point collection based on the knowledge point organizing strategy, then uses a neurocognitive diagnostic model to obtain students' English cognitive level, and establishes a feedback mechanism for blended teaching of English in colleges and universities based on students' level with the help of the DKVMN model. On this basis, in order to reveal the path of student engagement enhancement under the blended teaching feedback mechanism, the structural equation theory and scale testing method were chosen to construct the student engagement model. After the calculation of DKVMN model, we get that English topic 1914 has high correlation with knowledge points 10, 11, 13, 14 and 4, while English knowledge point 18 has almost no correlation, which accurately detects the English knowledge points that students have not mastered, and then provides students with targeted review methods, which verifies the effectiveness of the feedback mechanism. In addition, the path coefficient of self-efficacy and engagement is 0.206, and the other potential variables are similar, which comprehensively visualizes the quantitative relationship between the paths of students' engagement augmentation under the feedback mechanism.