Features and Influencing Factors of Mobile Learning Behavior of Employees in Accounting Profession

Abstract

Data mining of the mobile learning behavior of learners can help researchers understand the underlying association rules of such behavior and the internal mechanism of the development of their thinking during the learning process, thereby giving the true and accurate evaluations on the thought and status of mobile learners. However, existing models established for mobile learning behavior recognition cannot give satisfactory enough explanations to the changes in the cognition level of learners, so this paper took employees in accounting profession as subjects to analyze the features and influencing factors of their mobile learning behavior. At first, this paper built a neural network model based on the attention mechanism and used it to classify and recognize the input data of mobile learning behavior via original data learning, and feature extraction and analysis. Then, this paper created evaluation scenarios for subjects participating in mobile learning, designed economic management tasks applicable for actual application scenarios, and proposed several evaluation indexes for assessing their learning performance. After that, the explicit learning behavior of the subjects was taken as the criterion for judging whether their learning performance has achieved the desired learning goals or not. At last, the effectiveness of our analysis model was verified by the experimental results.