The mutual information model is introduced into the educational data association patterns mining. A new mining algorithm of the matrix-weighted positive and negative association patterns from educational data is presented based on mutual information model, and the related theorems and their proof are given. The algorithm overcomes the defects of the existing algorithms for weighted association patterns. It pays special attention to the various weights of the itemset in database, and also uses a new evaluation standard of positive and negative association patterns. Hence the positive and negative association patterns obtained from the educational data get closer to reality. Analysis on these patterns shows that, the potential educational and teaching rules, as well as educational development trend are discovered, providing a scientific basis for management, decision-making and teaching reform in education. Experiment results on real educational data demonstrate that the proposed algorithm is effective and reliable, with important potential value in the educational data processing and analyzing.