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Cognitive Support System for Collaborative Learning and Blended Learning Based on Fuzzy Logic
Fang Liu, Li Zhao and Jiali Xie
At present, learners are faced with the problems of low learning efficiency and high time cost due to abundant network resources. A new type of cognitive support system for college students is designed. Firstly, the fuzzy K-Means algorithm is applied to obtain the screening principle, and then the neural network is integrated into the system to design a 6-layer cognitive support system. The optimal hidden layer node of the BP neural network algorithm is 5, and the error of the algorithm is the smallest atthis time, and the value is 0.4554. The clustering results of the fuzzy K-Means algorithm show that the first class has the least number, most of the data are gathered in one class, and there is one data outside the cluster; the largest cluster is the fourth class, which represents the most data of this type, and the data outside the cluster is the largest. There are 8 data. When the classification threshold is 0.6, the performance of the four hybrid collaborative learning models is the best, and the specificity, sensitivity, and accuracy of the new hybrid collaborative learning model are improved by about 23.5%, 26.5%, and 18.6%, respectively. The research results combine the learner’s characteristic information to ensure the practical value of the system to a large extent.
Keywords: Fuzzy logic, blended collaborative learning, cognitive support system, K-means, Least square method, BP neural network