Predicting reading comprehension scores from eye movements using artificial neural networks and fuzzy output error
Why this work is in the frame
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Bibliographic record
Abstract
Predicting reading comprehension from eye gaze data is a difficult task. We investigate the use of artificial neural networks(ANNs) to predict reading comprehension scores from eye gaze collected from participants who read and completed an onlinetutorial in our lab. Problems such as large feature sets and small highly imbalanced data sets compound to make this task evenmore complex. We propose using fuzzy output error (FOE) as an alternative performance function to mean square error (MSE)for training feed-forward neural networks to overcome these problems. We show that the use of FOE as the performance functionfor training ANNs provides significantly better classification of eye movements to reading comprehension scores. ANNs withthree hidden layers of neurons gave the best classification results especially when FOE is used as the performance functionfor training. In these cases we found up to 50% reduction in misclassification rates compared to using MSE. We found thatANNs give optimal classification results in comparison to other classification techniques. When FOE is used as the performancefunction for training the ANNs the misclassification rates are halved compared to the other techniques. Cluster analysis wasperformed on one of the more complex data sets. Interesting reading behaviour properties were found within the data set.The intended use of this research is in the design of adaptive online learning environments that use eye gaze to predict usercomprehension from reading behavior.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it