How to assess students' cognitive structure is an important problem in the process of education and teaching.Traditionally,teachers evaluate a student based on their classroom performance and scores,which is lack of efficiency,objectivity,and it is hard to treat anyone equally.To solve this problem,DINA model,which is able to evaluate knowledge proficiency of students,has become a popular Cognitive Diagnosis model with a good interpretation.However,traditional DINA models are all based on small samples.When it comes to large-scale online learning scenario,the calculation will be significantly time-consuming.To address these issues,we first give proof of the convergence of DINA model,and then propose three acceleration methods.To be specific,the first one,called Incremental DINA(I-DINA),can partition the student data into blocks and iterate through the blocks.Then the second one,Maximum-Entropy DINA(ME-DINA),can choose and only access the most powerful students.At last,we combine the advantages of these two methods and propose the last model called Incremental Maximum Entropy DINA(IME-DINA).Extensive experiments on both a real-world dataset and simulation data demonstrate that our models can achieve dozens of acceleration without reducing the effectiveness of DINA Model.
Key words
educational data mining /
cognitive diagnosis /
DINA model /
convergence acceleration /
expectation maximization algorithm
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Funding
National High-tech R&D Program of China (863 Program) (No.2015AA015409); National Natural Science Foundation of China for Distinguished Young Schoolars (No.61325010); National Natural Science Foundation of China (No.61672483, No.U1605251); Youth Innovation Promotion Association CAS (会员编号2014299)
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