To Cluster the Learners'Online Dynamic Learning Performance Based on the Logistic Curve Growth Model
Based on the dynamic learning data in information-based teaching,the Logistic curve is used to fit the growth law of learners'comprehensive online learning performance with the learning time.By doing that,the infi-nite-dimensional function space is transformed into a finite-dimensional parameter space,and the clustering of the growth law of learners'comprehensive online learning performance is realized by using the classical K-Means clus-tering algorithm.The results show that learners can be divided into mainstream learners and non-mainstream learners.Among them,mainstream learners account for 96.92％of all learners,that is,the vast majority,and can be further subdivided into"high-efficient learners""moderate-efficient learners"and"low-efficient learn-ers",which account for about 16.41％,61.02％and 19.49％of all learners,respectively.Non-mainstream learners can be further divided into"long-term slow increase type"learners and"short-term rapid increase type"learners,and both account for 1.54％of all learners.In addition,there is a very significant difference in the av-erage final exam scores between different categories of learners,and the average final exam scores of the"high""medium"and"low"efficient mainstream learners gradually decrease,but they are all higher than those of non-mainstream learners.Among non-mainstream learners,the average final exam scores of learners with"short-term rapid increase type"is higher than those of"long-term slow increase type"learners.
comprehensive online learning performanceLogistic curvetwo-step series curve clustering methodelbow method
万方数据
维普
