Predicting the results of evaluation procedures of academics

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1 Computer Science and Engineering - DISI, Università di Bologna, Bologna, Italy
2 Department of Classical Philology and Italian Studies, University of Bologna, Bologna, Italy
3 STLab, Institute of Cognitive Science and Technologies, National Research Council, Roma, Italy
DOI
10.7287/peerj.preprints.27582v1
Published
Accepted
Subject Areas
Data Science, Digital Libraries
Keywords
Predictive Models, Scientometrics, Research Evaluation, Data Processing, ASN, Machine Learning, National Scientific Habilitation, Academic assessment, Science of Science, Informetrics
Copyright
© 2019 Poggi et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Poggi F, Ciancarini P, Gangemi A, Nuzzolese AG, Peroni S, Presutti V. 2019. Predicting the results of evaluation procedures of academics. PeerJ Preprints 7:e27582v1

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 59149 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.

Author Comment

This is a submission to PeerJ Computer Science for review.