Effect of Gamification on Task Engagement During an Eye-Tracking Test Battery in 5-Year-Old Children Born Preterm: Observational Study
Bibliographic record
Abstract
BACKGROUND: Recent studies suggest that eye movements during tasks reflect cognitive processes and that analysis of eye movements using eye-tracking devices can identify developmental impairments in young children. Maintaining engagement during eye-tracking assessments in young children is challenging and often results in data loss due to distractions. This leads to incomplete recordings and repeated measurements, which can be taxing for young children. Gamification of eye-tracking procedures for cognitive diagnosis might increase engagement and help mitigate these problems, but its effects should be studied and quantified. OBJECTIVE: This study compares a standard eye-tracking test battery designed by us with a gamified cartoon version to evaluate the effectiveness of gamification in reducing data loss. The gamified test incorporated child-friendly visuals to provide context for the stimuli presented. The study has two objectives: (1) to compare the data quality between the two versions of the test and (2) to investigate whether, by applying a dynamic stopping criterion to both tests, the higher data quality of the gamified procedure allows earlier test termination. METHODS: Data were collected in a cohort of 25 children born preterm aged 5 years. We measured data quality using a metric derived from robustness, which we defined as the lost data index (LDI), along with task completion rates and feedback from participants. Data analyses were performed as follows: (1) direct comparison of the LDI for the two tests and (2) demonstrating that, although the base gamified test is longer, applying a stopping criterion results in comparable durations. The stopping criterion was based on the number of tasks with an LDI value below a predefined threshold. RESULTS: The gamified version demonstrated a significant reduction in average LDI compared with the standard version in the first (P<.001, Mann-Whitney U test) and second (P=.01, U test) quarters of the test. In addition, a lower rate of missing values, concentrated at the beginning of the tests, allowed the cartoon test to be stopped after fewer tasks. This, together with the longer tasks of the cartoon test, resulted in comparable test lengths for all thresholds measured by area under the curve (P=.50, U test) and at the chosen threshold of 0.2 LDI (P=.21, U test). Increased engagement was further supported by positive feedback, with 79% (11/14) of the participants who provided feedback preferring the gamified version. CONCLUSIONS: These findings highlight the potential of serious games in eye-tracking-based cognitive assessments for 5-year-old children born preterm. Specifically, gamification might reduce missing values and increase participant engagement, leading to higher retention rates and more effective tests, without significantly lengthening testing procedures.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".