Application of K-sets Unsupervised Clustering in Digital Learning Environment

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 tracke...

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Bibliographic Details
Main Authors:	Yu-Siang Hsu, 許鈺祥
Other Authors:	Hung-Ta Pai
Format:	Others
Language:	en_US
Published:	2015
Online Access:	http://ndltd.ncl.edu.tw/handle/09657539492703626294

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spelling	ndltd-TW-103NTPU06500102016-07-31T04:21:53Z http://ndltd.ncl.edu.tw/handle/09657539492703626294 Application of K-sets Unsupervised Clustering in Digital Learning Environment 在數位學習環境下K-sets非監督式分群演算法之應用 Yu-Siang Hsu 許鈺祥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. Hung-Ta Pai 白宏達 2015 學位論文 ; thesis 29 en_US
collection	NDLTD
language	en_US
format	Others
sources	NDLTD
description	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.
author2	Hung-Ta Pai
author_facet	Hung-Ta Pai Yu-Siang Hsu 許鈺祥
author	Yu-Siang Hsu 許鈺祥
spellingShingle	Yu-Siang Hsu 許鈺祥 Application of K-sets Unsupervised Clustering in Digital Learning Environment
author_sort	Yu-Siang Hsu
title	Application of K-sets Unsupervised Clustering in Digital Learning Environment
title_short	Application of K-sets Unsupervised Clustering in Digital Learning Environment
title_full	Application of K-sets Unsupervised Clustering in Digital Learning Environment
title_fullStr	Application of K-sets Unsupervised Clustering in Digital Learning Environment
title_full_unstemmed	Application of K-sets Unsupervised Clustering in Digital Learning Environment
title_sort	application of k-sets unsupervised clustering in digital learning environment
publishDate	2015
url	http://ndltd.ncl.edu.tw/handle/09657539492703626294
work_keys_str_mv	AT yusianghsu applicationofksetsunsupervisedclusteringindigitallearningenvironment AT xǔyùxiáng applicationofksetsunsupervisedclusteringindigitallearningenvironment AT yusianghsu zàishùwèixuéxíhuánjìngxiàksetsfēijiāndūshìfēnqúnyǎnsuànfǎzhīyīngyòng AT xǔyùxiáng zàishùwèixuéxíhuánjìngxiàksetsfēijiāndūshìfēnqúnyǎnsuànfǎzhīyīngyòng
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Application of K-sets Unsupervised Clustering in Digital Learning Environment

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