  The evaluation of instructors by their students has been practiced at most
universities for many decades, and there has always been a great interest in a
variety of aspects of the evaluations. Are students matured and knowledgeable
enough to provide useful and dependable feedback for the improvement of their
instructors' teaching skills/abilities? Does the level of difficulty of the
course have a strong relationship with the rating the student give an
instructor? In this paper, we attempt to answer questions such as these using
some state of the art statistical data mining techniques such support vector
machines, classification and regression trees, boosting, random forest, factor
analysis, kMeans clustering. hierarchical clustering. We explore various
aspects of the data from both the supervised and unsupervised learning
perspective. The data set analyzed in this paper was collected from a
university in Turkey. The application of our techniques to this data reveals
some very interesting patterns in the evaluations, like the strong association
between the student's seriousness and dedication (measured by attendance) and
the kind of scores they tend to assign to their instructors.
