Debora de Chiusole

454 total citations
25 papers, 286 citations indexed

About

Debora de Chiusole is a scholar working on Artificial Intelligence, Information Systems and Management Science and Operations Research. According to data from OpenAlex, Debora de Chiusole has authored 25 papers receiving a total of 286 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Artificial Intelligence, 5 papers in Information Systems and 5 papers in Management Science and Operations Research. Recurrent topics in Debora de Chiusole's work include Intelligent Tutoring Systems and Adaptive Learning (16 papers), Neural Networks and Applications (8 papers) and Bayesian Modeling and Causal Inference (8 papers). Debora de Chiusole is often cited by papers focused on Intelligent Tutoring Systems and Adaptive Learning (16 papers), Neural Networks and Applications (8 papers) and Bayesian Modeling and Causal Inference (8 papers). Debora de Chiusole collaborates with scholars based in Italy, Germany and Bulgaria. Debora de Chiusole's co-authors include Luca Stefanutti, Pasquale Anselmi, Egidio Robusto, Andrea Spoto, Matthias Gondan, Noemi Mazzoni, Matteo Orsoni, Sara Giovagnoli, Sara Garofalo and Mariagrazia Benassi and has published in prestigious journals such as Psychological Methods, Psychometrika and Behavior Research Methods.

In The Last Decade

Debora de Chiusole

23 papers receiving 278 citations

Peers

Debora de Chiusole

CSA

MSOR

CTM

Masaki Uto Japan

Anthony F. Botelho United States

Shaghayegh Sahebi United States

Hao Cen United States

Atro Voutilainen Finland

Shuanghong Shen China

Fernando Schapachnik Argentina

Karl Schultz United States

Jinze Wu China

Enric Mor Spain

Masaki Uto Japan

Debora de Chiusole

183 ×0.8

78 ×1.3

59 ×1.1

CSA

55 ×1.0

MSOR

3 ×0.1

CTM

Citations per year, relative to Debora de Chiusole Debora de Chiusole (= 1×) peers Masaki Uto

Countries citing papers authored by Debora de Chiusole

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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20 of 20 papers shown

Anselmi, Pasquale, et al.. (2025). Automatic Generation of Rule-Based Raven-Like Matrices in R: The matRiks Package. Applied Psychological Measurement. 50(3). 148–149.

Stefanutti, Luca, Pasquale Anselmi, Debora de Chiusole, Andrea Spoto, & Jürgen Heller. (2025). The BLIM, the DINA, and their polytomous extensions. Rejoinder to the Commentary by Chiu, Köhn, and Ma. Psychometrika. 90(2). 717–733. 1 indexed citations

Chiusole, Debora de, Pasquale Anselmi, Noemi Mazzoni, et al.. (2025). Multi-method validation of the new computerized test of fluid intelligence MatriKS. Iris (University of Trento).

Chiusole, Debora de, Giulia Balboni, Noemi Mazzoni, et al.. (2024). PsycAssist: A Web-Based Artificial Intelligence System Designed for Adaptive Neuropsychological Assessment and Training. Brain Sciences. 14(2). 122–122. 4 indexed citations

Chiusole, Debora de, Umberto Granziol, Andrea Spoto, & Luca Stefanutti. (2024). Reliability of a probabilistic knowledge structure. Behavior Research Methods. 56(7). 8022–8037. 1 indexed citations

Chiusole, Debora de, et al.. (2022). Algorithms for the adaptive assessment of procedural knowledge and skills. Behavior Research Methods. 55(7). 3929–3951. 6 indexed citations

Stefanutti, Luca, et al.. (2021). Markov solution processes: Modeling human problem solving with procedural knowledge space theory. Journal of Mathematical Psychology. 103. 102552–102552. 7 indexed citations

Stefanutti, Luca, Debora de Chiusole, Pasquale Anselmi, & Andrea Spoto. (2020). Extending the Basic Local Independence Model to Polytomous Data. Psychometrika. 85(3). 684–715. 11 indexed citations

Chiusole, Debora de, Luca Stefanutti, Pasquale Anselmi, & Egidio Robusto. (2020). Stat-Knowlab. Assessment and Learning of Statistics with Competence-based Knowledge Space Theory. International Journal of Artificial Intelligence in Education. 30(4). 668–700. 21 indexed citations

10.

Stefanutti, Luca, et al.. (2020). Modeling misconceptions in knowledge space theory. Journal of Mathematical Psychology. 99. 102435–102435. 6 indexed citations

11.

Chiusole, Debora de, Andrea Spoto, & Luca Stefanutti. (2019). Extracting partially ordered clusters from ordinal polytomous data. Behavior Research Methods. 52(2). 503–520. 11 indexed citations

12.

Chiusole, Debora de, Luca Stefanutti, Pasquale Anselmi, & Egidio Robusto. (2018). Testing the actual equivalence of automatically generated items. Behavior Research Methods. 50(1). 39–56. 4 indexed citations

13.

Stefanutti, Luca & Debora de Chiusole. (2017). On the assessment of learning in competence based knowledge space theory. Journal of Mathematical Psychology. 80. 22–32. 36 indexed citations

14.

Chiusole, Debora de, Luca Stefanutti, & Andrea Spoto. (2016). A class of k-modes algorithms for extracting knowledge structures from data. Behavior Research Methods. 49(4). 1212–1226. 19 indexed citations

15.

Anselmi, Pasquale, Egidio Robusto, Luca Stefanutti, & Debora de Chiusole. (2016). An Upgrading Procedure for Adaptive Assessment of Knowledge. Psychometrika. 81(2). 461–482. 16 indexed citations

16.

Chiusole, Debora de, Luca Stefanutti, Pasquale Anselmi, & Egidio Robusto. (2015). Modeling missing data in knowledge space theory.. Psychological Methods. 20(4). 506–522. 21 indexed citations

17.

Chiusole, Debora de, Luca Stefanutti, Pasquale Anselmi, & Egidio Robusto. (2015). Naïve Tests of Basic Local Independence Model’s Invariance. The Spanish Journal of Psychology. 18. E26–E26. 4 indexed citations

18.

Chiusole, Debora de, Luca Stefanutti, Pasquale Anselmi, & Egidio Robusto. (2013). Assessing Parameter Invariance in the BLIM: Bipartition Models. Psychometrika. 78(4). 710–724. 18 indexed citations

19.

Chiusole, Debora de, Pasquale Anselmi, Luca Stefanutti, & Egidio Robusto. (2013). The Gain-Loss Model: Bias of the Parameter Estimates. Electronic Notes in Discrete Mathematics. 42. 33–40. 10 indexed citations

20.

Chiusole, Debora de & Luca Stefanutti. (2011). Rating, ranking or both? A joint application of two probabilistic models for the measurement of values. Research Padua Archive (University of Padua). 18. 1–12. 4 indexed citations

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.

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