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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.18468 |
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| _version_ | 1866915129482805248 |
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| author | Davalos, Eduardo Salas, Jorge Alberto Zhang, Yike Srivastava, Namrata Thatigotla, Yashvitha Gonzales, Abbey McFadden, Sara Cho, Sun-Joo Biswas, Gautam Goodwin, Amanda |
| author_facet | Davalos, Eduardo Salas, Jorge Alberto Zhang, Yike Srivastava, Namrata Thatigotla, Yashvitha Gonzales, Abbey McFadden, Sara Cho, Sun-Joo Biswas, Gautam Goodwin, Amanda |
| contents | Understanding reader behaviors such as skimming, deep reading, and scanning is essential for improving educational instruction. While prior eye-tracking studies have trained models to recognize reading behaviors, they often rely on instructed reading tasks, which can alter natural behaviors and limit the applicability of these findings to in-the-wild settings. Additionally, there is a lack of clear definitions for reading behavior archetypes in the literature. We conducted a classroom study to address these issues by collecting instructed and in-the-wild reading data. We developed a mixed-method framework, including a human-driven theoretical model, statistical analyses, and an AI classifier, to differentiate reading behaviors based on their velocity, density, and sequentiality. Our lightweight 2D CNN achieved an F1 score of 0.8 for behavior recognition, providing a robust approach for understanding in-the-wild reading. This work advances our ability to provide detailed behavioral insights to educators, supporting more targeted and effective assessment and instruction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_18468 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Instructed Tasks: Recognizing In-the-Wild Reading Behaviors in the Classroom Using Eye Tracking Davalos, Eduardo Salas, Jorge Alberto Zhang, Yike Srivastava, Namrata Thatigotla, Yashvitha Gonzales, Abbey McFadden, Sara Cho, Sun-Joo Biswas, Gautam Goodwin, Amanda Human-Computer Interaction Artificial Intelligence J.4 Understanding reader behaviors such as skimming, deep reading, and scanning is essential for improving educational instruction. While prior eye-tracking studies have trained models to recognize reading behaviors, they often rely on instructed reading tasks, which can alter natural behaviors and limit the applicability of these findings to in-the-wild settings. Additionally, there is a lack of clear definitions for reading behavior archetypes in the literature. We conducted a classroom study to address these issues by collecting instructed and in-the-wild reading data. We developed a mixed-method framework, including a human-driven theoretical model, statistical analyses, and an AI classifier, to differentiate reading behaviors based on their velocity, density, and sequentiality. Our lightweight 2D CNN achieved an F1 score of 0.8 for behavior recognition, providing a robust approach for understanding in-the-wild reading. This work advances our ability to provide detailed behavioral insights to educators, supporting more targeted and effective assessment and instruction. |
| title | Beyond Instructed Tasks: Recognizing In-the-Wild Reading Behaviors in the Classroom Using Eye Tracking |
| topic | Human-Computer Interaction Artificial Intelligence J.4 |
| url | https://arxiv.org/abs/2501.18468 |