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.