Automatic evaluation of learning objects based on cross-entropy of eye fixations minimization

Abstract

Learning objects (LOs) are important information resources that support traditional learning methods. To evaluate the impact, effectiveness, and usefulness of learning objects it is necessary a theoretically, reliable, and valid evaluation tool. This paper presents a cross-entropy metric to compare the design of LO that uses the information provided by visual fixations measured from a small focus group. The cross-entropy is measured on the test set to assess how accurate the entropy constancy rate principle is in predicting the test data. We conducted an experiment with children of elementary school (n=23). Results show that images with lower values of the proposed metric can be easily read (Mean = 0.746 min/image) than those LO composed of random images (Mean = 0.977 min/image). Hence, the metric is useful to optimize the fluency. This is an important step through the design of a fully automated tool to evaluate LO.