Francesco Poggi 1 , Paolo Ciancarini 1,2 , Aldo Gangemi 3 , Andrea Giovanni Nuzzolese 4 , Silvio Peroni 3 , Valentina Presutti 4 Show Affiliations »
Abstract
BACKGROUND: The 2010 reform of the Italian university system introduced the National Scientific Habilitation (ASN) as a requirement for applying to permanent professor positions. Since the CVs of the 59,149 candidates and the results of their assessments have been made publicly available, the ASN constitutes an opportunity to perform analyses about a nation-wide evaluation process. OBJECTIVE: The main goals of this paper are: (i) predicting the ASN results using the information contained in the candidates' CVs; (ii) identifying a small set of quantitative indicators that can be used to perform accurate predictions. APPROACH: Semantic technologies are used to extract, systematize and enrich the information contained in the applicants' CVs, and machine learning methods are used to predict the ASN results and to identify a subset of relevant predictors. RESULTS: For predicting the success in the role of associate professor, our best models using all and the top 15 predictors make accurate predictions (F-measure values higher than 0.6) in 88% and 88.6% of the cases, respectively. Similar results have been achieved for the role of full professor. EVALUATION: The proposed approach outperforms the other models developed to predict the results of researchers' evaluation procedures. CONCLUSIONS: Such results allow the development of an automated system for supporting both candidates and committees in the future ASN sessions and other scholars' evaluation procedures. ©2019 Poggi et al.
BACKGROUND: The 2010 reform of the Italian university system introduced the National Scientific Habilitation (ASN) as a requirement for applying to permanent professor positions. Since the CVs of the 59,149 candidates and the results of their assessments have been made publicly available, the ASN constitutes an opportunity to perform analyses about a nation-wide evaluation process. OBJECTIVE: The main goals of this paper are: (i) predicting the ASN results using the information contained in the candidates' CVs; (ii) identifying a small set of quantitative indicators that can be used to perform accurate predictions. APPROACH: Semantic technologies are used to extract, systematize and enrich the information contained in the applicants' CVs, and machine learning methods are used to predict the ASN results and to identify a subset of relevant predictors. RESULTS: For predicting the success in the role of associate professor, our best models using all and the top 15 predictors make accurate predictions (F-measure values higher than 0.6) in 88% and 88.6% of the cases, respectively. Similar results have been achieved for the role of full professor. EVALUATION: The proposed approach outperforms the other models developed to predict the results of researchers' evaluation procedures. CONCLUSIONS: Such results allow the development of an automated system for supporting both candidates and committees in the future ASN sessions and other scholars' evaluation procedures. ©2019 Poggi et al.
Entities: Chemical
Keywords:
ASN; Academic assessment; Data Processing; Informetrics; Machine Learning; National Scientific Habilitation; Predictive Models; Research Evaluation; Science of Science; Scientometrics
Year: 2019
PMID： 33816852 PMCID： PMC7924640 DOI： 10.7717/peerj-cs.199
Source DB: PubMed Journal: PeerJ Comput Sci ISSN： 2376-5992