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Application Research Of An Improved K-means Clustering Algorithm For Cooperative Learning In Modern Education

Posted on:2019-12-07Degree:MasterType:Thesis
Country:ChinaCandidate:Y J WangFull Text:PDF
GTID:2428330566967385Subject:Software engineering
Abstract/Summary:PDF Full Text Request
Modern education encourages students to autonomous learning,cooperative learning and inquiry learning.At present,many domestic and foreign scholars have done extensive research on cooperative learning.and have an immense harvest.At the same time,it is proved that cooperative learing is an effeetive learning paradigm and strategy.Therefore,how to segment students in correct groups effeetivcly according to the characteristics of the students becomes the chief issue.Thc research and development of data mining technology provided an effective approach for student grouping.SOM and K-means have their superior tcaturrs for cluster analysis,their combination into a two-stage method is generally much more powerful than the two methods used individually,and a stronger self-adaptability.In this paper,an improved K-means algorithm is proposed,the SOM is combined with K-means into a two stage clustering method,self-organizing neural network is used to obtain the initial cluster center for the input of K-means.Applied several standard datasets.the improved algorithm has been proved that it performs better than Kohonen's SOM,K-means and fuzzy means clustcring.This thesis is coneerned with applying the two stage clustering algorithm to resolve student segmentation problem.In order to improve the cffeetiveness of cooperative learning,an improved K-means clustering algorithm is applied in grouping the cooperative learners based on their individual characteristics,The optimal cluster number of the improved algorithm is determined by the sum of squares of deviations within the group.In the case of same cluster numbers,the standard deviation of distance within cluster is compared with the original approach.The algorithm is applied to the data analysis of Siyuan college students,the impact of clustering on student grouping has been confirmed by experimental evidence,and the algorithm can produce more scientific and reasonable results.All in all,it provides a firm anchor for cooperative learning theory applied to teaching area.
Keywords/Search Tags:K-means, Self-Organizing Maps, Cooperative Learning, Data Mining
PDF Full Text Request
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