Combining Automation and Expertise: A Semi-automated Approach to Correcting Eye Tracking Data in Reading Tasks

TL;DR

This paper introduces Fix8, a semi-automated GUI tool that significantly speeds up eye tracking data correction in reading tasks while maintaining high accuracy, combining automation with user expertise.

Contribution

The paper presents a novel semi-automated correction approach integrated into Fix8, improving correction speed and user experience over manual methods.

Findings

44% faster correction time compared to manual correction

No loss in correction accuracy with the semi-automated approach

Users preferred the semi-automated method and reported lower workload

Abstract

In reading tasks drift can move fixations from one word to another or even another line, invalidating the eye tracking recording. Manual correction is time-consuming and subjective, while automated correction is fast yet limited in accuracy. In this paper we present Fix8 (Fixate), an open-source GUI tool that offers a novel semi-automated correction approach for eye tracking data in reading tasks. The proposed approach allows the user to collaborate with an algorithm to produce accurate corrections faster without sacrificing accuracy. Through a usability study (N=14) we assess the time benefits of the proposed technique, and measure the correction accuracy in comparison to manual correction. In addition, we assess subjective workload through NASA Task Load Index, and user opinions through Likert-scale questions. Our results show that on average the proposed technique was 44% faster than manual correction without any sacrifice in accuracy. In addition, users reported a preference for the proposed technique, lower workload, and higher perceived performance compared to manual correction. Fix8 is a valuable tool that offers useful features for generating synthetic eye tracking data, visualization, filters, data converters, and eye movement analysis in addition to the main contribution in data correction.