Eye tracking technology for examining cognitive processes in education: A systematic review
Bibliographic record
Abstract
Eye-tracking studies have been increasingly used to investigate student cognitive processes. While some existing literature has reviewed commonly used eye-tracking measures and analytical methods across different contexts, there remains a gap in clarifying the educational themes, cognitive theories, and specific cognitive processes most frequently examined in recent eye-tracking studies. To address this, this review synthesizes recent educational eye-tracking research analyzing student cognitive processes from January 2012 to November 2024. In total, 313 articles were reviewed, and several main findings emerged: (1) The majority of eye-tracking studies examined the themes of multimedia learning, reading performance, and problem-solving, while fewer focused on second language learning, computer-based tests, and individual differences. (2) About half of the studies were conducted under one or more theories, with Cognitive Load Theory (CLT), Cognitive Theory of Multimedia Learning (CTML), and Dual-coding Theory (DCT) being the most frequently applied. (3) Eye-tracking data were primarily collected from college students, and temporal and count measures were typically used to investigate the cognitive process of information organization rather than those of information selection and integration. (4) Basic statistical methods and linear mixed models were widely used to analyze student attention differences. The results of this review provide theoretical and practical insights into the strengths and challenges of using eye-tracking technology to assess the impact of pedagogical interventions in computer-based learning environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".