Models of text mining to measure improvements to doctoral courses suggested by “STELLA” phd survey respondents

Pasquale Pavone and Maria Francesca Romano ()
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Pasquale Pavone: Scuola Superiore Sant’Anna, Pisa - Italy
Maria Francesca Romano: Scuola Superiore Sant’Anna, Pisa - Italy

Statistica, 2013, vol. 73, issue 4, 463-475

Abstract: 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.

Keywords: textual analysis; automatic classification; multi-class categorisation; TF IDF; assessment of the learning experience (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:bot:rivsta:v:73:y:2013:i:4:p:463-475

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