College Students' Learning Decision-Making Based on Group Learning Behavior

Abstract

In learning groups, individuals have a few similarities in terms of the regularity of learning time, requirement for learning resources, and requirement for tutoring and accompanying. Analyzing the differences and connections of the learning behavior of different groups is helpful for generating more effective, targeted, and comprehensive learning decisions, however, existing studies are not extensive or deep enough in analyzing the learning behavior of different type learning groups. For this reason, this paper attempts to explore a learning decision-making model based on the influence of group learning behavior. At first, this paper made use of the advantages of Q-learning to improve the conventional behavior tree model, constructed a new model and used it to research the group learning behavior; then, this paper combined decision-making idea with the game model, and adopted a complex network structure to explore the evolution law of group learning decision-making based on multiple games. At last, this paper used experimental results to prove the effectiveness of the constructed model.

Student Online Learning Behavior Characteristics Based on Multidimensional Cognitive Model

“…However, existing research on analyzing the learning behavior of different types of learning groups is not extensive or in-depth enough. Therefore, Li et al [18] attempts to explore a learning decision-making model based on the impact of group learning behavior. First, the traditional behavior tree model is improved using the advantages of Q-learning to construct a new model, which is used to study group learning behavior.…”

The Learning Behavior Analysis of Online Vocational Education Students and Learning Resource Recommendation Based on Big Data

“…The analysis given by existing studies on the learning behaviors of different-type learning groups is generally not extensive or deep enough, therefore, Li et al [25] explored a learning decision model based on the influence of group learning behavior, they took the advantage of Q-learning to improve the conventional behavior tree model and constructed a novel model which was then applied to the research on group learning behavior in the study; the authors combined the decision-making idea with the game model, and adopted a complex network structure to figure out the evolution law of the decision-making of multi-game group learning. Zhang and Li [26] studied the influence of smart learning environment on students' learning behavior in higher vocational colleges and analyzed the related influencing factors, they also compared the specific data of these influencing factors and discussed the significance of smart learning environment to education and teaching.…”