eye-tracking
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- PaperETS Research Report Series16 Jul 2026
Second Language Writing Processes in the TOEFL iBT® Test: Examining the Write for an Academic Discussion Task Using Eye-Tracking, Keystroke Logging, and Stimulated Recall
Ching-Ni Hsieh, Renka Ohta
The study used eye-tracking, keystroke logging, and stimulated recall to examine the writing processes of 19 L2 English users completing the TOEFL iBT Write for an Academic Discussion (WAD) task. Five core cognitive processes were identified: prompt (re)reading, planning, formulation, monitoring, and revision. Findings support the construct validity of the WAD task and show that task design features influence L2 writing processes and strategic behaviors.
Original abstract
The present study investigates the construct validity of the TOEFL iBT® writing test and focuses on the writing processes elicited by the Write for an Academic Discussion (WAD) task. Nineteen adult L2 English users completed the WAD task while their eye movements and keystrokes were recorded, followed by stimulated recall interviews to capture their writing strategies and thought processes. Eye-tracking data revealed sustained attention to the writing window, with frequent reference to the task question and example posts. Keystroke logging indicated substantial initial pauses and language-related revision behaviors. Qualitative analysis of stimulated recalls identified five core cognitive processes: prompt (re)reading, planning, formulation, monitoring, and revision. The integration of eye-tracking, keystroke logging, and stimulated recall demonstrates that the WAD task engages L2 writers in processes consistent with theoretical models of academic writing, supplying backing for the construct validity of the TOEFL iBT Writing test. The findings also reveal that task design features may influence L2 writing processes and shape writers’ strategic behaviors and attention allocation. Suggested citation: Hsieh, C.-N., & Ohta, R. (in press). Second language writing processes in the TOEFL iBT® test: Examining the Write for an Academic Discussion task using eye-tracking, keystroke logging, and stimulated recall (Research Report). ETS. https://doi.org/10.64634/tk1sd682
- PaperComputers & Education20 Jun 2026
Corrigendum to “Print versus digital reading in an era of digital immersion: Eye-tracking evidence from pre- and post-pandemic cohorts” [Computers & Education 252 (2026) 105669]
Yu-Cin Jian, Yi-Jye Wu
This corrigendum corrects errors in a study comparing print and digital reading comprehension using eye-tracking data from pre- and post-pandemic cohorts, published in Computers & Education.
- PaperarXiv — Language & NLP (cs.CL)6 May 2026
Assessing Cognitive Effort in L2 Idiomatic Processing: An Eye-Tracking Dataset
Eduardo Santos, Juliana Carvalho, César Rennó-Costa
This paper presents an eye-tracking dataset to investigate how L2 learners process idiomatic expressions, revealing that L2 speakers often adopt a literal-first approach with measurable cognitive costs. The dataset, recorded from Portuguese L1 speakers of English across all CEFR levels using 60 Hz hardware, validates an inverse correlation between proficiency and regressive eye movements. It serves as a cognitively grounded benchmark for evaluating human processing models and alignment of large language models with human-like figurative understanding.
Original abstract
This paper presents the development and validation of an eye-tracking dataset designed to investigate how second-language (L2) learners process idiomatic expressions. While native speakers often rely on direct retrieval of figurative meanings, L2 speakers frequently adopt a literal-first approach, which incurs measurable cognitive costs. This resource captures these costs through ocular metrics recorded from Portuguese L1 speakers of English across all CEFR proficiency levels (A1-C2). Although the study uses entry-level 60 Hz hardware (Tobii Pro Spark), we demonstrate that this sampling rate provides sufficient data density to detect macro-cognitive events such as fixations and regressions in reading. Preliminary analysis validates the dataset by revealing a strong inverse correlation between language proficiency and regressive eye movements. Integrated into the MIA (Modeling Idiomaticity in Human and Artificial Language Processing) initiative, this dataset serves as a cognitively grounded benchmark for evaluating both human processing models and the alignment of large language models with human-like figurative understanding.