An algorithm of Association Rules for exploring defects of college teaching

The teaching appraisal must be done every semester in most of the universities. There are large amounts of data accumulated. In order to find out the relevance between individual background of a teacher and his teaching performance, this thesis introduces an algorithm based on multidimensional Association Rules Mining, which has explored teaching defects when there are the individual differences and use the verdict to guide teachers´ work.