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Eye-tracking technology in identifying visualizers and verbalizers: data on eye-movement differences and detection accuracy

Data in this article revealed the eye movement differences of visualizers and verbalizers in viewing four pictures-in-text by analyzing gaze path and fixation data (fixation duration, fixation counts and the average time on each fixation). After imported the documents into Tobii eye-tracker, authors...

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Detalles Bibliográficos
Autores principales: Luo, Zhanni, Wang, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6811880/
https://www.ncbi.nlm.nih.gov/pubmed/31667221
http://dx.doi.org/10.1016/j.dib.2019.104447
Descripción
Sumario:Data in this article revealed the eye movement differences of visualizers and verbalizers in viewing four pictures-in-text by analyzing gaze path and fixation data (fixation duration, fixation counts and the average time on each fixation). After imported the documents into Tobii eye-tracker, authors triggered participants’ natural reading habits, recorded their eye movement data, and predicted participants as visualizers or verbalizers based on the Felder and Silverman Learning Style Model (FSLSM). Comparing the predictions with self-report results tested by the Index of Learning Styles (ILS) questionnaire, authors got the accuracy results of using eye-tracking technology in identifying visualizers and verbalizers. The data revealed natural preferences of people with different styles, and it can be used in future studies in the field of adaptive learning systems, individual differences, neuroscience in reading habits, and individualized instruction.