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The evaluation of instructors by their students has been practiced at mostuniversities for many decades, and there has always been a great interest in avariety of aspects of the evaluations. Are students matured and knowledgeableenough to provide useful and dependable feedback for the improvement of theirinstructors' teaching skills/abilities? Does the level of difficulty of thecourse have a strong relationship with the rating the student give aninstructor? In this paper, we attempt to answer questions such as these usingsome state of the art statistical data mining techniques such support vectormachines, classification and regression trees, boosting, random forest, factoranalysis, kMeans clustering. hierarchical clustering. We explore variousaspects of the data from both the supervised and unsupervised learningperspective. The data set analyzed in this paper was collected from auniversity in Turkey. The application of our techniques to this data revealssome very interesting patterns in the evaluations, like the strong associationbetween the student's seriousness and dedication (measured by attendance) andthe kind of scores they tend to assign to their instructors.

Pattern Discovery in Students' Evaluations of Professors: A Statistical Data Mining Approach