Debora de Chiusole
- Artificial Intelligence top 5%
- Information Systems top 10%
- Computer Science Applications top 5%
- Management Science and Operations Research top 10%
- Computational Theory and Mathematics top 10%
- Co-authors
- Luca StefanuttiPasquale AnselmiEgidio RobustoAndrea SpotoMatthias GondanNoemi MazzoniMatteo OrsoniSara Giovagnoli
- Topics
- Intelligent Tutoring Systems and Adaptive Learning (16 papers)Neural Networks and Applications (8 papers)Bayesian Modeling and Causal Inference (8 papers)
In The Last Decade
Debora de Chiusole
23 papers receiving 278 citations
Peers
Comparison fields: 5 of 35
- Artificial Intelligence 225
- Information Systems 60
- Computer Science Applications 56
- Management Science and Operations Research 54
- Computational Theory and Mathematics 42
Countries citing papers authored by Debora de Chiusole
This map shows the geographic impact of Debora de Chiusole's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Debora de Chiusole with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Debora de Chiusole more than expected).
Fields of papers citing papers by Debora de Chiusole
This network shows the impact of papers produced by Debora de Chiusole. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Debora de Chiusole. The network helps show where Debora de Chiusole may publish in the future.
Co-authorship network of co-authors of Debora de Chiusole
This figure shows the co-authorship network connecting the top 25 collaborators of Debora de Chiusole. A scholar is included among the top collaborators of Debora de Chiusole based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Debora de Chiusole. Debora de Chiusole is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 4 | |
| 5 | 1 | |
| 6 | 6 | |
| 7 | 7 | |
| 8 | 11 | |
| 9 | 21 | |
| 10 | 6 | |
| 11 | 11 | |
| 12 | 4 | |
| 13 | 36 | |
| 14 | 19 | |
| 15 | 16 | |
| 16 | 21 | |
| 17 | 4 | |
| 18 | 18 | |
| 19 | 10 | |
| 20 | Rating, ranking or both? A joint application of two probabilistic models for the measurement of values | 4 |
About Debora de Chiusole
Debora de Chiusole is a scholar working on Computer Science Applications, Artificial Intelligence and Management Science and Operations Research, having authored 25 papers that have together received 286 indexed citations. Recurring topics across this work include Intelligent Tutoring Systems and Adaptive Learning (16 papers), Neural Networks and Applications (8 papers) and Bayesian Modeling and Causal Inference (8 papers). The work is most often cited by research in Computer Science Applications (56 citations), Artificial Intelligence (225 citations) and Management Science and Operations Research (54 citations). Debora de Chiusole has collaborated with scholars based in Italy, Germany and Bulgaria. Frequent co-authors include Luca Stefanutti, Pasquale Anselmi, Egidio Robusto, Andrea Spoto, Matthias Gondan, Noemi Mazzoni, Matteo Orsoni, Sara Giovagnoli, Sara Garofalo and Mariagrazia Benassi. Their work appears in journals such as Psychological Methods, Psychometrika and Behavior Research Methods.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.