Toward an Educative EEG-Based neuroIIR System for Adapting Contents
The pursuit of information is an integral aspect of a learner’s daily routine, often facilitated by information retrieval systems. In this study, we explore the correlation between text complexity and reading comprehension through the lens of neuroinformatics. Our objective is to ascertain whether i...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universidad de Jaén |
| Repositorio: | RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén |
| OAI Identifier: | oai:ruja.ujaen.es:10953/1912 |
| Acceso en línea: | https://doi.org/10.1080/10447318.2023.2275088 https://www.tandfonline.com/doi/full/10.1080/10447318.2023.2275088 https://hdl.handle.net/10953/1912 |
| Access Level: | acceso abierto |
| Palabra clave: | Interactive information retrieval (IIR) reading comprehension neuroscience Brain-Computer Interfaces (BCI) electroencephalograms (EEG) text complexity |
| Sumario: | The pursuit of information is an integral aspect of a learner’s daily routine, often facilitated by information retrieval systems. In this study, we explore the correlation between text complexity and reading comprehension through the lens of neuroinformatics. Our objective is to ascertain whether it is feasible to infer the difficulty level of a text for a learner based on their brain activity, quantified by an electroencephalogram (EEG), during the information retrieval process. To accomplish this, we administered a section of the Woodcock Reading Mastery Test to our participants, evaluating their average reading fluency. Subsequently, the 18 participants under observation read paragraphs of varying complexity and responded to questions pertaining to the text. Utilizing the amassed data, we trained a deep learning model known as EEGNet to autonomously discern the complexity of the text being read, relying on the EEG signals. Furthermore, we conducted a comparison with previous research that employed a distinct characterization approach and employed a random forest algorithm for classification. The outcomes revealed that the present approach (EEGNet) outperformed the former method, achieving an accuracy rate of 80.83% across all subjects, whereas the random forest algorithm achieved a mere 50.28% accuracy. Our findings suggest the potential to classify the EEG signals in accordance with the level of difficulty a student encounters when comprehending the text. We propose several avenues for extending our work into real-time scenarios. |
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