Agentic education · Learning science

Actionable companion · Feedback & scaffolding

Learning with agency: Estimation of reading subjective understanding based on eye gaze analysis

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Age / stage not established. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

The integration of ubiquitous technologies in the field of education has considerably enhanced our way of learning. Such technologies enable students to get a gradual feedback about their performance and to provide adapted learning materials. It is particularly important in the domain of foreign language learning which requires intense daily practice. One of the main inputs of adaptive learning systems is the user’s understanding of a reading material. The reader’s understanding can be divided into two parts: the objective understanding and the subjective understanding. The objective understanding can be measured by comprehension questions about the content of the text. The subjective understanding is the reader’s perception of his own understanding. The subjective understanding plays an important role in the reader’s motivation, self-esteem and confidence. However, its automatic estimation remains a challenging task. This paper is one of the first to propose a method to estimate the subjective understanding. We show that using the eye gaze to predict the subjective understanding improves the estimation by 13% as compared to using comprehension questions.

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Survey / observational study

Stage: Age / stage not established. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

Failure in communication or lack of knowledge: We minimized this factor by explaining carefully which marks corresponds to which level of understanding (equivalence).

Limitations and uncertainty · source passage

In the results above, we have shown that using the eye gaze is an efficient way to predict the understanding of a text for a participant. Analyzing the fixations and the saccades information helps to distinguish a reader who did not understand the text from a reader who did understand, confirming our hypotheses H1, H2, H3 and H4. However, we have underlined some limitations which affect the algorithm performance. One of the first error is linked to the eye tracker accuracy. Because of the vertical error of the eye tracker, some fixations are not on the text characters. However, because we do not use the position of the fixations relatively to the text in our algorithm (only the fixations number and their duration), this misplacement does not affect the algorithm. As a consequence, no fixations were excluded from the analysis. Nevertheless, the recordings can contain some noise which will be interpreted as a specific reading behavior by the algorithm. For example, in the Fig 8 we can see a drift at the beginning of the recording which is an error of the eye tracker. Moreover, with the vertical error of the eye tracker, some fixations are not on the text characters. This kind of errors can be avoided by improving the calibration step or by using post-processing algorithms [28].

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

  • Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Charles Lima Sanches; Olivier Augereau; Koichi Kise. Estimation of reading subjective understanding based on eye gaze analysis. PLoS ONE (2018-10-25). https://doi.org/10.1371/journal.pone.0206213

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.