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Your search keyword '"Daraio, Cinzia"' showing total 3 results
Start Over Author "Daraio, Cinzia" Topic development of data and information services
3 results on '"Daraio, Cinzia"'

1. Exploring the Potentialities of Automatic Extraction of University Webometric Information

Author

Bianchi Gianpiero, Bruni Renato, Daraio Cinzia, Laureti Palma Antonio, Perani Giulio, and Scalfati Francesco

Subjects
development of data and information services, webometrics indicators, higher education institutions, automatic extraction, machine learning, optimization, Information technology, T58.5-58.64, Electronic computers. Computer science, QA75.5-76.95
Abstract

The main objective of this work is to show the potentialities of recently developed approaches for automatic knowledge extraction directly from the universities’ websites. The information automatically extracted can be potentially updated with a frequency higher than once per year, and be safe from manipulations or misinterpretations. Moreover, this approach allows us flexibility in collecting indicators about the efficiency of universities’ websites and their effectiveness in disseminating key contents. These new indicators can complement traditional indicators of scientific research (e.g. number of articles and number of citations) and teaching (e.g. number of students and graduates) by introducing further dimensions to allow new insights for “profiling” the analyzed universities.

Published
2020
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2. A Tailor-made Data Quality Approach for Higher Educational Data

Author

Daraio Cinzia, Bruni Renato, Catalano Giuseppe, Daraio Alessandro, Matteucci Giorgio, Scannapieco Monica, Wagner-Schuster Daniel, and Lepori Benedetto

Subjects
knowledge organization, development of data and information services, cross-sectional and multiannual quality checks, higher education institutions, information quality, Information technology, T58.5-58.64, Electronic computers. Computer science, QA75.5-76.95
Abstract

This paper relates the definition of data quality procedures for knowledge organizations such as Higher Education Institutions. The main purpose is to present the flexible approach developed for monitoring the data quality of the European Tertiary Education Register (ETER) database, illustrating its functioning and highlighting the main challenges that still have to be faced in this domain.

Published
2020
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3. A Tailor-made Data Quality Approach for Higher Educational Data

Author

Daraio, Cinzia, Bruni, Renato, Catalano, Guiseppe, Daraio, Alessandro, Matteucci, Giorgio, Scannapieco, Monica, Wagner-Schuster, Daniel, and Lepori, Benedetto

Subjects
4. Education, ETER, European Tertiary Education Register, Knowledge organization, Development of data and information services, Crosssectional and multiannual quality checks, Higher education institutions, Information quality
Abstract

Purpose: This paper relates the definition of data quality procedures for knowledge organizations such as Higher Education Institutions. The main purpose is to present the flexible approach developed for monitoring the data quality of the European Tertiary Education Register (ETER) database, illustrating its functioning and highlighting the main challenges that still have to be faced in this domain. Design/methodology/approach: The proposed data quality methodology is based on two kinds of checks, one to assess the consistency of cross-sectional data and the other to evaluate the stability of multiannual data. This methodology has an operational and empirical orientation. This means that the proposed checks do not assume any theoretical distribution for the determination of the threshold parameters that identify potential outliers, inconsistencies, and errors in the data. Findings: We show that the proposed cross-sectional checks and multiannual checks are helpful to identify outliers, extreme observations and to detect ontological inconsistencies not described in the available meta-data. For this reason, they may be a useful complement to integrate the processing of the available information. Research limitations: The coverage of the study is limited to European Higher Education Institutions. The cross-sectional and multiannual checks are not yet completely integrated. Practical implications: The consideration of the quality of the available data and information is important to enhance data quality-aware empirical investigations, highlighting problems, and areas where to invest for improving the coverage and interoperability of data in future data collection initiatives. Originality/value: The data-driven quality checks proposed in this paper may be useful as a reference for building and monitoring the data quality of new databases or of existing databases available for other countries or systems characterized by high heterogeneity and complexity of the units of analysis without relying on pre-specified theoretical distributions.

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