Clustering students' open-ended questionnaire answers

Wilhelmiina Hämäläinen, Mike Joy, Florian Berger, Sami Huttunen

Tutkimustuotos: TyöpaperiWorking paperScientific

20 Lataukset (Pure)

Abstrakti

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
AlkuperäiskieliEnglanti
KustantajaarXiv.org
Sivumäärä13
TilaJulkaistu - 2018
OKM-julkaisutyyppiD4 Julkaistut kehitykset tai tutkimusraportit tai tutkimukset

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