Clustering students' open-ended questionnaire answers
This work addresses a bottleneck in educational data mining for researchers and practitioners, but it is incremental as it applies existing clustering methods to new data without major methodological innovations.
The paper tackled the problem of automatically clustering short, open-ended questionnaire answers in educational data mining, finding that affinity propagation performed well for English data despite outliers and overlapping clusters, but methods struggled with Finnish data and stemming often reduced clustering quality.
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.