  Open responses form a rich but underused source of information in educational
data mining and intelligent tutoring systems. One of the major obstacles is the
difficulty of clustering short texts automatically. In this paper, we
investigate the problem of clustering free-formed questionnaire answers. We
present comparative experiments on clustering ten sets of open responses from
course feedback queries in English and Finnish. We also evaluate how well the
main topics could be extracted from clusterings with the HITS algorithm. The
main result is that, for English data, affinity propagation performed well
despite frequent outliers and considerable overlapping between real clusters.
However, for Finnish data, the performance was poorer and none of the methods
clearly outperformed the others. Similarly, topic extraction was very
successful for the English data but only satisfactory for the Finnish data. The
most interesting discovery was that stemming could actually deteriorate the
clustering quality significantly.
