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@misc{2429757, author = {Dostálová, Nicol and Švaříček, Roman and Sedmidubský, Jan and Culemann, Wolf and Šašinka, Čeněk and Zezula, Pavel and Čeněk, Jiří}, keywords = {dyslexia; eye-tracking; dataset}, language = {eng}, title = {ETDD70: Eye-Tracking Dyslexia Dataset}, url = {https://doi.org/10.5281/zenodo.13332134}, year = {2024} }
TY - JFULL ID - 2429757 AU - Dostálová, Nicol - Švaříček, Roman - Sedmidubský, Jan - Culemann, Wolf - Šašinka, Čeněk - Zezula, Pavel - Čeněk, Jiří PY - 2024 TI - ETDD70: Eye-Tracking Dyslexia Dataset KW - dyslexia KW - eye-tracking KW - dataset UR - https://doi.org/10.5281/zenodo.13332134 N2 - The ETDD70 dataset comprises eye-tracking recordings from 70 Czech participants, equally divided into dyslexic and non-dyslexic readers, all aged 9–10 years. The dataset captures eye movements during three text-reading tasks in Czech: syllable reading (Task 1), meaningful text reading (Task 4), and pseudo-text reading (Task 5). This dataset is the result of the project “Diagnostics of Dyslexia Using Eye-Tracking and Artificial Intelligence” conducted by our research team. The project aims to leverage artificial intelligence tools and advanced technical equipment (eye tracking) to more effectively diagnose dyslexia, one of the most common specific learning disorders, and thereby significantly improve re-education strategies for dyslexic students. The primary goal is to develop models that accurately distinguish between dyslexic and non-dyslexic readers based on eye movement patterns recorded during these tasks. Data collection took place between October 2022 and August 2023, adhering to ethical standards. The project was approved by the Research Ethics Committee of Masaryk University in Brno, Czech Republic. ER -
DOSTÁLOVÁ, Nicol, Roman ŠVAŘÍČEK, Jan SEDMIDUBSKÝ, Wolf CULEMANN, Čeněk ŠAŠINKA, Pavel ZEZULA and Jiří ČENĚK. \textit{ETDD70: Eye-Tracking Dyslexia Dataset}. 2024.
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