学生开放式问卷答案聚类
Clustering students' open-ended questionnaire answers
摘要：
开放式回答在教育数据采掘和智能辅导系统中形成了丰富但未充分利用的信息源。其中一个主要的障碍是很难自动聚类短文本。在本文中，我们研究的问题聚类自由形式的问卷答案。我们提出了比较实验聚类10套开放的反应，从英语和芬兰语的课程反馈查询。我们还评估了如何从聚类中提取的主要主题与HITS算法。主要结果是，对于英语数据，尽管频繁的离群值和真实聚类之间的相当大的重叠，亲和传播表现良好。然而，对于芬兰的数据，性能较差，没有一种方法明显优于其他方法。同样，主题提取是非常成功的英语数据，但只有令人满意的芬兰数据。最有趣的发现是，词干处理实际上会显著降低聚类质量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
