Research on Discrete Dynamic Modeling of Learner Behavior Analysis in English Teaching

Abstract

The current English teaching mode focuses on the traditional offline teaching and online teaching. In order to solve the problems that some students are inefficient and cannot teach students according to their aptitude in the teaching process, this paper uses the big data analysis strategy based on a neural network algorithm. This paper studies the discrete dynamic modeling method of learner behavior analysis in English teaching. Firstly, it summarizes the current situation of English teaching and the research status of the hybrid application of discrete dynamic modeling technology. Secondly, combined with English teaching content and teaching objectives, through the analysis of various data of students’ learning behavior, this paper evaluates students’ English teaching quality from five aspects that affect the students’ English teaching quality and puts forward a personalized English teaching quality evaluation model based on discrete dynamic modeling technology and learners’ behavior analysis. Finally, through the practical teaching application in a university, the feasibility of the discrete dynamic English teaching model is verified. The results show that compared with the current innovative English teaching methods based on a dynamic iterative decision algorithm, the personalized discrete dynamic English teaching model based on learner behavior analysis significantly improves the quality of English teaching and students’ academic performance.

Research on Knowledge Graph Multiple Environment-Aware Recommendation Algorithm for Teaching Course Recommendation

“…The core task of intelligent MOOC education is to apply artificial intelligence technology to build an intelligent guided learning service for MOOC platforms, which can comprehensively integrate learner and course information, deeply excavate the deep interaction between learners and courses, and ultimately realize personalized recommendations planned for diverse groups of learners [10][11][12][13]. However, at the beginning of the establishment of MOOC platforms, many educational factors and teaching functions were not fully considered, such as the characterization of learner profiles, the maintenance of the knowledge system, and the construction of the course recommendation engine [14][15][16]. Due to the lack of educator supervision and the lack of personalized services, although MOOC attracts a large number of learners to register, the course completion rate is chronically low [17][18].…”