Language Proficiency Assessment from Eye Movements in Naturalistic Passage Reading

2026-08-31

AI Summary

本文通过扩展基于眼动追踪的方法评估英语作为第二语言的阅读能力，解决了传统语言测试的问题，并提出了一种有效减少评分偏差的方法。

Abstract

Standard language proficiency tests rely on linguistic tasks such as vocabulary, grammar and reading comprehension quizzes. An alternative, cognitively motivated approach, introduced in Berzak et al. (2018), proposed instead to predict language proficiency from behavioral traces of eye movements in reading. In this work, we validate and extend this approach from single sentences to more naturalistic reading of contextualized passages in English as a second language, new proficiency measures, prediction models, and reading in an information seeking regime. We find that the approach is effective in all these evaluations. We further address two key open questions on eye movement based proficiency testing: (1) potential scoring biases that reflect the proximity of the reader's native language to English, which may undermine validity, and (2) its reliability. We find that eye movement based proficiency scores are indeed biased towards L1s that are linguistically closer to English. We propose a score debiasing method which effectively remedies this issue. The reliability analyses suggest that eye movement proficiency scores are more reliable than standard language proficiency scores. Overall, our results strengthen and broaden the empirical foundations for future eye movement based language assessment technologies.

Problem

• Language Proficiency
• Eye Movements
• Reading
• Second Language
• Scoring Bias

Innovation

• eye movements
• language proficiency assessment
• score debiasing
• reliability