@inproceedings{zermiani-etal-2024-interead,
    title = "{I}nte{R}ead: An Eye Tracking Dataset of Interrupted Reading",
    author = {Zermiani, Francesca  and
      Dhar, Prajit  and
      Sood, Ekta  and
      K{\"o}gel, Fabian  and
      Bulling, Andreas  and
      Wirzberger, Maria},
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.802",
    pages = "9154--9169",
    abstract = "Eye movements during reading offer a window into cognitive processes and language comprehension, but the scarcity of reading data with interruptions {--} which learners frequently encounter in their everyday learning environments {--} hampers advances in the development of intelligent learning technologies. We introduce InteRead {--} a novel 50-participant dataset of gaze data recorded during self-paced reading of real-world text. InteRead further offers fine-grained annotations of interruptions interspersed throughout the text as well as resumption lags incurred by these interruptions. Interruptions were triggered automatically once readers reached predefined target words. We validate our dataset by reporting interdisciplinary analyses on different measures of gaze behavior. In line with prior research, our analyses show that the interruptions as well as word length and word frequency effects significantly impact eye movements during reading. We also explore individual differences within our dataset, shedding light on the potential for tailored educational solutions. InteRead is accessible from our datasets web-page: https://www.ife.uni-stuttgart.de/en/llis/research/datasets/.",
}

Eye movements during reading offer a window into cognitive processes and language comprehension, but the scarcity of reading data with interruptions {--} which learners frequently encounter in their everyday learning environments {--} hampers advances in the development of intelligent learning technologies. We introduce InteRead {--} a novel 50-participant dataset of gaze data recorded during self-paced reading of real-world text. InteRead further offers fine-grained annotations of interruptions interspersed throughout the text as well as resumption lags incurred by these interruptions. Interruptions were triggered automatically once readers reached predefined target words. We validate our dataset by reporting interdisciplinary analyses on different measures of gaze behavior. In line with prior research, our analyses show that the interruptions as well as word length and word frequency effects significantly impact eye movements during reading. We also explore individual differences within our dataset, shedding light on the potential for tailored educational solutions. InteRead is accessible from our datasets web-page: https://www.ife.uni-stuttgart.de/en/llis/research/datasets/.
Zermiani, Francesca Dhar, Prajit Sood, Ekta K{\"o}gel, Fabian Bulling, Andreas Wirzberger, Maria
InteRead: An Eye Tracking Dataset of Interrupted Reading