Beyond Instructed Tasks: Recognizing In-the-Wild Reading Behaviors in the Classroom Using Eye Tracking

TL;DR

This paper presents a new framework combining theoretical modeling, statistical analysis, and AI classification to recognize natural reading behaviors in classroom settings using eye tracking, moving beyond instructed tasks.

Contribution

It introduces a mixed-method approach with a lightweight 2D CNN for in-the-wild reading behavior recognition, addressing limitations of prior instructed-task studies.

Findings

Achieved an F1 score of 0.8 with the CNN classifier.

Collected and analyzed both instructed and natural reading data.

Provided a theoretical model for reading behavior archetypes.

Abstract

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.