ORIGINAL RESEARCH article
Front. Educ.
Sec. Assessment, Testing and Applied Measurement
Volume 10 - 2025 | doi: 10.3389/feduc.2025.1639273
Fixations, Regressions, and Results: Eye-Tracking Metrics as Real-Time Signals of Cognitive Engagement in Flipped-Class Quizzes
Provisionally accepted- 1Hong Kong Metropolitan University, Hong Kong, Hong Kong, SAR China
- 2Lingnan University, Tuen Mun, Hong Kong, SAR China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
We examined whether classroom‑friendly eye tracking yields interpretable, item‑level process signals in flipped‑class quizzes once surface text features are taken into account. Thirty‑four undergraduates produced 320 analysable attempts on 55 Bloom‑coded multiple‑choice items while a 60 Hz tracker captured fixation intensity (FI; total dwell) and regression rate (RR; proportion backward saccades). Crossed mixed‑effects models included a covariate for each item's total word count (stem + options). Controlling for length, Bloom category did not uniquely predict FI or RR, indicating that previously observed "demand" patterns largely reflect surface text. In a logistic model of accuracy, FI showed a positive, small effect (OR≈1.30 per 1 SD, p≈.068) and RR a negative, small effect (OR≈0.81, p≈.15), while gender contributed no unique variance. Block‑level self‑reports of mental effort correlated near zero with gaze metrics and accuracy. Overall, the results suggest that FI and RR can provide complementary, real‑time indicators of engagement in authentic pre‑class quizzes, but only when length/layout are standardised or explicitly modelled; claims about Bloom‑level differences should therefore be made cautiously. We outline design guidance for future item banks (length‑matched stems, fixed option counts, pre‑registered word‑count covariates) to enable firmer inferences and practical classroom diagnostics.
Keywords: eye tracking, Fixation Intensity, Regression rate, flipped classroom, Process data, Adaptive assessment
Received: 01 Jun 2025; Accepted: 30 Sep 2025.
Copyright: © 2025 WANG, XIE and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Nina XIE, ninaxie@ln.edu.hk
Yujun Liu, yujunliu@ln.hk
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.