Unsupervised Machine Learning in the Evaluation of Telerehabilitation Interventions for Reading Fluency

Authors

  • Chiara Pecini Department of Education, Intercultures, Literature and Psychology (FORLILPSI), University of Florence, Florence, Italy
  • Andrea Frascari Anastasis Social Cooperative Society, Bologna, Italy
  • Viola Margheri Department of Education, Intercultures, Literature and Psychology (FORLILPSI), University of Florence, Florence, Italy
  • Kianna Kazemi IFAB International Foundation Big Data and Artificial Intelligence for Human Development, Bologna, Italy
  • Pierluigi Zoccolotti Department of Psychology, Sapienza University of Rome, Rome, Italy; Tuscany Rehabilitation Clinic, Montevarchi (Arezzo), Italy; Gianfranco Salvini Foundation, Montevarchi (Arezzo), Italy
  • Gionata Manduchi IFAB International Foundation Big Data and Artificial Intelligence for Human Development, Bologna, Italy

DOI:

https://doi.org/10.63144/ijt.2026.6735

Keywords:

Learning disorder, Machine learning, Reading, Telerehabilitation, Unsupervised learning

Abstract

The implementation of machine learning techniques enables the analysis of large data corpora to differentiate response patterns based on exercise parameters, providing insights for implementing efficient telerehabilitation of reading skills. In this study, we applied unsupervised machine learning methods to investigate rehabilitation training trajectories in response to a self-adaptive teleintervention of reading decoding. We analyzed data from 6,692 children and adolescents using the Reading Trainer app for at least two months.  Using K-means clustering, we identified eight distinct learning curve patterns, subsequently categorized as No-responders, Partial Responders, and High Responders based on the differences between initial and final reading performance. Multinomial regression analysis showed that younger children with greater initial difficulties and those who completed a higher number of in-session exercises during treatment were more likely to be classified as High Responders. These findings provide crucial insights to predict responses to reading intervention and help in personalizing telerehabilitation strategies.

  

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Published

2026-06-24

How to Cite

Pecini, C., Frascari, A., Margheri, V., Kazemi, K., Zoccolotti, P., & Manduchi, G. (2026). Unsupervised Machine Learning in the Evaluation of Telerehabilitation Interventions for Reading Fluency. International Journal of Telerehabilitation, 18(1). https://doi.org/10.63144/ijt.2026.6735

Issue

Vol. 18 No. 1 (2026)

Section

Clinical Applications

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Copyright (c) 2026 Chiara Pecini, Andrea Frascari, Viola Margheri, Kianna Kazemi, Pierluigi Zoccolotti, Gionata Manduchi

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Funding data

  • https://doi.org/10.13039/501100000780
    Grant numbers CUP: B83C22003920001