文章基本信息

标题：Models of text mining to measure improvements to doctoral courses suggested by “STELLA” phd survey respondents
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作者：Pasquale Pavone ; Maria Francesca Romano
期刊名称：Statistica
印刷版ISSN：1973-2201
出版年度：2013
卷号：73
期号：4
页码：463-475
DOI：10.6092/issn.1973-2201/4500
语种：English
出版社：Dep. of Statistical Sciences "Paolo Fortunati", Università di Bologna
摘要：We present Text Mining models to thematically categorise and measure the suggestions of PhD holders on improving PhD programmes in the STELLA survey (Statistiche in TEma di Laureati e LAvoro). The coded responses questionnaire, designed to evaluate the employment opportunities of students and assess their learning experience, included open-ended questions on how to improve PhD programmes. The Corpus analysed was taken from the data of Italian PhD holders between 2005 and 2009 in eight universities (Bergamo, Brescia, Milano Statale, Milano Bicocca, Pisa, Scuola Superiore Sant’Anna, Palermo and Pavia). The usual methodological approach to text analysis allowed us to categorize open-ended proposals of PhD courses improvements in 8 Italian Universities.
关键词：textual analysis;automatic classification;multi-class categorisation;TF IDF;assessment of the learning experience