Open AccessCollege Students' Learning Decision-Making Based on Group Learning Behavior
Author(s) -
Lin Li,
Dongfang Chen,
Tao Li
Publication year - 2022
Publication title -
international journal of emerging technologies in learning/international journal: emerging technologies in learning
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.454
H-Index - 24
eISSN - 1868-8799
pISSN - 1863-0383
DOI - 10.3991/ijet.v17i08.30557
Subject(s) - artificial intelligence , computer science , decision tree , machine learning , group (periodic table) , cooperative learning , group learning , psychology , mathematics education , teaching method , chemistry , organic chemistry
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.