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.
['Leana Copeland', 'Tom Gedeon', 'B. Sumudu U. Mendis']
Predicting reading comprehension scores from eye movements using artificial neural networks and fuzzy output error