Chiara Masci

534 total citations
19 papers, 317 citations indexed

About

Chiara Masci is a scholar working on Education, Computer Science Applications and Statistics and Probability. According to data from OpenAlex, Chiara Masci has authored 19 papers receiving a total of 317 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Education, 4 papers in Computer Science Applications and 4 papers in Statistics and Probability. Recurrent topics in Chiara Masci's work include School Choice and Performance (9 papers), Online Learning and Analytics (4 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). Chiara Masci is often cited by papers focused on School Choice and Performance (9 papers), Online Learning and Analytics (4 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). Chiara Masci collaborates with scholars based in Italy, Belgium and Netherlands. Chiara Masci's co-authors include Tommaso Agasisti, Francesca Ieva, Anna Maria Paganoni, Geraint Johnes, Kristof De Witte, Luca Viganò, Francesco Fiz, Guido Torzilli, Guido Costa and Martina Sollini and has published in prestigious journals such as Scientific Reports, European Journal of Operational Research and European Journal of Nuclear Medicine and Molecular Imaging.

In The Last Decade

Chiara Masci

18 papers receiving 300 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Chiara Masci Italy 10 104 78 52 50 27 19 317
Elizabeth Legowski United States 11 29 0.3× 30 0.4× 56 1.1× 202 4.0× 12 0.4× 14 408
Olga Medvedeva United States 11 20 0.2× 31 0.4× 79 1.5× 258 5.2× 11 0.4× 18 425
Mark S. Frank United States 13 15 0.1× 10 0.1× 141 2.7× 29 0.6× 53 2.0× 35 416
Bruno Bertaccini Italy 11 21 0.2× 10 0.1× 9 0.2× 9 0.2× 53 2.0× 34 402
Nader Ghotbi Japan 10 9 0.1× 24 0.3× 43 0.8× 78 1.6× 11 0.4× 31 387
J.L. Domínguez-Escrig Spain 10 33 0.3× 8 0.1× 30 0.6× 8 0.2× 83 3.1× 28 493
Yu-chen Hsu Taiwan 10 63 0.6× 34 0.4× 4 0.1× 37 0.7× 23 0.9× 27 317
Frank Ückert Germany 15 15 0.1× 5 0.1× 12 0.2× 98 2.0× 27 1.0× 69 649
Andrew Katz United States 9 79 0.8× 34 0.4× 8 0.2× 40 0.8× 30 1.1× 66 343
Lisa Federer United States 11 12 0.1× 13 0.2× 6 0.1× 37 0.7× 30 1.1× 26 562

Countries citing papers authored by Chiara Masci

Since Specialization
Citations

This map shows the geographic impact of Chiara Masci'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 Chiara Masci with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chiara Masci more than expected).

Fields of papers citing papers by Chiara Masci

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chiara Masci. 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 Chiara Masci. The network helps show where Chiara Masci may publish in the future.

Co-authorship network of co-authors of Chiara Masci

This figure shows the co-authorship network connecting the top 25 collaborators of Chiara Masci. A scholar is included among the top collaborators of Chiara Masci 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 Chiara Masci. Chiara Masci is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Homayounfar, Reza, et al.. (2024). Detection of cardiovascular disease cases using advanced tree-based machine learning algorithms. Scientific Reports. 14(1). 22230–22230. 7 indexed citations
2.
Masci, Chiara, et al.. (2024). Assessing the impact of hybrid teaching on students’ academic performance via multilevel propensity score-based techniques. Socio-Economic Planning Sciences. 92. 101824–101824. 1 indexed citations
3.
Masci, Chiara, et al.. (2023). Modelling time-to-dropout via shared frailty Cox models. A trade-off between accurate and early predictions. Studies in Higher Education. 49(4). 763–781. 2 indexed citations
4.
Lema, Melisa Diaz, et al.. (2023). The Determinants of Mathematics Achievement: A Gender Perspective Using Multilevel Random Forest. Economies. 11(2). 32–32. 4 indexed citations
5.
Fiz, Francesco, Chiara Masci, Guido Costa, et al.. (2022). PET/CT-based radiomics of mass-forming intrahepatic cholangiocarcinoma improves prediction of pathology data and survival. European Journal of Nuclear Medicine and Molecular Imaging. 49(10). 3387–3400. 47 indexed citations
6.
7.
Masci, Chiara, Francesca Ieva, & Anna Maria Paganoni. (2022). Semiparametric multinomial mixed-effects models: A university students profiling tool. The Annals of Applied Statistics. 16(3). 1 indexed citations
8.
Masci, Chiara, et al.. (2021). Early-predicting dropout of university students: an application of innovative multilevel machine learning and statistical techniques. Studies in Higher Education. 47(9). 1935–1956. 31 indexed citations
9.
10.
Masci, Chiara, et al.. (2021). Generalized mixed‐effects random forest: A flexible approach to predict university student dropout. Statistical Analysis and Data Mining The ASA Data Science Journal. 14(3). 241–257. 37 indexed citations
11.
Masci, Chiara, Francesca Ieva, Tommaso Agasisti, & Anna Maria Paganoni. (2021). Evaluating class and school effects on the joint student achievements in different subjects: a bivariate semiparametric model with random coefficients. Computational Statistics. 36(4). 2337–2377. 4 indexed citations
12.
Fontana, Luca, Chiara Masci, Francesca Ieva, & Anna Maria Paganoni. (2021). Performing Learning Analytics via Generalised Mixed-Effects Trees. Data. 6(7). 74–74. 12 indexed citations
13.
Masci, Chiara, Anna Maria Paganoni, & Francesca Ieva. (2019). Semiparametric Mixed Effects Models for Unsupervised Classification of Italian Schools. Journal of the Royal Statistical Society Series A (Statistics in Society). 182(4). 1313–1342. 3 indexed citations
14.
Schiltz, Fritz, Chiara Masci, Tommaso Agasisti, & Dániel Horn. (2018). Using regression tree ensembles to model interaction effects: a graphical approach. Applied Economics. 50(58). 6341–6354. 22 indexed citations
15.
Masci, Chiara, Geraint Johnes, & Tommaso Agasisti. (2018). Student and school performance across countries: A machine learning approach. European Journal of Operational Research. 269(3). 1072–1085. 67 indexed citations
16.
Masci, Chiara, Kristof De Witte, & Tommaso Agasisti. (2016). The influence of school size, principal characteristics and school management practices on educational performance: An efficiency analysis of Italian students attending middle schools. Socio-Economic Planning Sciences. 61. 52–69. 32 indexed citations
17.
Masci, Chiara, Anna Maria Paganoni, Francesca Ieva, & Tommaso Agasisti. (2016). Analysis of pupils’ INVALSI achievements by means of bivariate multilevel models.. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1–6. 1 indexed citations
18.
Masci, Chiara, Francesca Ieva, Tommaso Agasisti, & Anna Maria Paganoni. (2016). Bivariate multilevel models for the analysis of mathematics and reading pupils' achievements. Journal of Applied Statistics. 44(7). 1296–1317. 14 indexed citations
19.
Masci, Chiara, Francesca Ieva, Tommaso Agasisti, & Anna Maria Paganoni. (2016). Does class matter more than school? Evidence from a multilevel statistical analysis on Italian junior secondary school students. Socio-Economic Planning Sciences. 54. 47–57. 16 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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