Performance model’s development: a novel approach encompassing ontology-based data access and visual analytics

Marco Angelini (), Cinzia Daraio (), Maurizio Lenzerini (), Francesco Leotta () and Giuseppe Santucci ()
Additional contact information
Marco Angelini: Sapienza University of Rome
Cinzia Daraio: Sapienza University of Rome
Maurizio Lenzerini: Sapienza University of Rome
Francesco Leotta: Sapienza University of Rome
Giuseppe Santucci: Sapienza University of Rome

Scientometrics, 2020, vol. 125, issue 2, No 4, 865-892

Abstract: Abstract The quantitative evaluation of research is currently carried out by means of indicators calculated on data extracted and integrated by analysts who elaborate them by creating illustrative tables and plots of results. In this approach, the robustness of the metrics used and the possibility for users of the metrics to intervene in the evaluation process are completely neglected. We propose a new approach which is able to move forward, from indicators’ development to an interactive performance model’s development. It combines the advantages of the ontology-based data access paradigm with the flexibility and robustness of a visual analytics environment putting the consumer/stakeholder at the centre of the evaluation. A detailed description of such an approach is presented in the paper. The approach is illustrated and evaluated trough a comprehensive user’s study that proves the added capabilities and the benefits that a user of performance models can have by using this approach.

Keywords: Education and research; Performance assessment; Performance modelling; Ontology-based data access; Visual analytics (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-020-03689-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:125:y:2020:i:2:d:10.1007_s11192-020-03689-x

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-020-03689-x

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().