| Summary: | 碩士 === 國立臺北大學 === 通訊工程研究所 === 103 === In recent years, eye trackers have been commonly used in learning research.
There have been many studies showing that the features of eye-movement are
strongly related to learning performance. In this study, we use an eye tracker to
retrieve related features and cluster analysis to classify learning performance into two
clusters comprising respectively of good and bad results, in order to discover the most
representative features. Here cluster analysis is applied to the collected eye tracker
data including the average fixation time, regression numbers, fixation maximum depth,
time spent on the subject. We compare the K-means and K-sets algorithms to
complete our study.
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