Research And Implementation Of A Collaborative Learning Grouping System That Balances Fairness And Difference

Collaborative learning is a learning mode of learning in groups or teams,and is an important way to cultivate learners' ability to communicate and understand.Among them,the grouping problem is one of the key problems of collaborative learning,and has become a challenging problem in the field of education science research.In recent years,the problem of grouping has received great attention from scholars at home and abroad,and a large number of research results have been obtained.However,the existing collaborative learning grouping research mainly considers the similarity or difference between learners,and does not consider the fairness between the groups in a balanced manner,which affects the education equity to a certain extent.In response to this problem,this paper studies the fair grouping problem of collaborative learning,and proposes a collaborative learning grouping algorithm.The main work is as follows:(1)This paper proposes a cooperative learning grouping algorithm based on fairness and difference in consideration of fairness and difference.This algorithm divides the students with the largest individual differences into a group as much as possible.The degree of difference should be as close as possible,so that the grouping results can be maximized heterogeneity within the group and maximum homogeneity between the groups,while also meeting the needs of balanced fairness and difference.Further,in order to verify the effectiveness of the collaborative learning grouping algorithm based on fairness and difference proposed in this paper,the analysis of variance was used to conduct experiments on real data sets.The experimental results show that the proposed collaboration based on fairness and difference The learning grouping algorithm has a significant effect on improving students' academic performance.At the same time,it can effectively improve the differences within the group,ensure the fairness between the groups,and achieve the grouping effect of"heterogeneous within the group,homogeneous between the groups".(2)This paper proposes an index for evaluating the degree of difference within the group,and the minimum degree of difference between the elements contained in the group is positively correlated with this value,that is,the greater the degree of difference within the group,the greater the value.Therefore,use This value can accurately reflect whether the grouping algorithm improves the degree of difference between elements in all groups.Further,in the simulation experiment,the comparison of the minimum difference index within the group with the random grouping method,the K-Means clustering grouping method and the collaborative learning grouping algorithm proposed in this paper verifies the collaborative learning grouping algorithm proposed in this paper can effectively improve the degree of difference within the group.(3)Based on the proposed collaborative learning grouping algorithm,this paper designs and implements a collaborative learning grouping prototype system that balances differences and fairness.The system uses the B/C architecture,which is mainly divided into teacher module and student module.Among them,the grouping algorithm in the teacher module implements two methods of free grouping and differential grouping,which meets the needs of automatic grouping.Finally,this paper performs a functional test on the grouping module to verify the stability and integrity of the proposed grouping algorithm and ensure the normal and stable operation of the system.