The Effect of Combining Hopfield Neural Network and Fuzzy Ranking on E-Learning System Performance
碩士 === 國立嘉義大學 === 資訊工程學系研究所 === 98 === In recent years, e-learning system is gradually taken as a platform for users do learning. Therefore, how to improve the efficiency of e-learning system has become a significant topic. Based on related researches, learners' learning paths are often used as...
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ndltd-TW-098NCYU53920032015-10-13T18:35:11Z http://ndltd.ncl.edu.tw/handle/99715491692851728085 The Effect of Combining Hopfield Neural Network and Fuzzy Ranking on E-Learning System Performance 結合霍普菲爾類神經網路及模糊評比於改善網路學習成效之研究 Shu-Hoa Ye 葉書豪 碩士 國立嘉義大學 資訊工程學系研究所 98 In recent years, e-learning system is gradually taken as a platform for users do learning. Therefore, how to improve the efficiency of e-learning system has become a significant topic. Based on related researches, learners' learning paths are often used as a criterion to improve the learning materials. Teachers can realize the learners' learning behaviors and the degree of understanding for the learning materials from their learning paths analyzed by some data mining technologies. For this reason, this study utilizes fuzzy ranking to extract the students who have better learning efficiencies first, subsequently, analyzing their learning paths by Hopfield Neural Network to get the high-used learning item sets. The purposes in this study are providing teachers criterions to improve the learning materials and learning resources by the data of high-used learning item sets, and by the technology of Hopfield Neural Network, it can promote the efficiencies in operation and processes for data mining of high-used learning item sets. Yen-Chu Hung 洪燕竹 2010 學位論文 ; thesis 62 zh-TW |
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Others
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碩士 === 國立嘉義大學 === 資訊工程學系研究所 === 98 === In recent years, e-learning system is gradually taken as a platform for users do learning. Therefore, how to improve the efficiency of e-learning system has become a significant topic. Based on related researches, learners' learning paths are often used as a criterion to improve the learning materials. Teachers can realize the learners' learning behaviors and the degree of understanding for the learning materials from their learning paths analyzed by some data mining technologies. For this reason, this study utilizes fuzzy ranking to extract the students who have better learning efficiencies first, subsequently, analyzing their learning paths by Hopfield Neural Network to get the high-used learning item sets. The purposes in this study are providing teachers criterions to improve the learning materials and learning resources by the data of high-used learning item sets, and by the technology of Hopfield Neural Network, it can promote the efficiencies in operation and processes for data mining of high-used learning item sets.
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author2 |
Yen-Chu Hung |
author_facet |
Yen-Chu Hung Shu-Hoa Ye 葉書豪 |
author |
Shu-Hoa Ye 葉書豪 |
spellingShingle |
Shu-Hoa Ye 葉書豪 The Effect of Combining Hopfield Neural Network and Fuzzy Ranking on E-Learning System Performance |
author_sort |
Shu-Hoa Ye |
title |
The Effect of Combining Hopfield Neural Network and Fuzzy Ranking on E-Learning System Performance |
title_short |
The Effect of Combining Hopfield Neural Network and Fuzzy Ranking on E-Learning System Performance |
title_full |
The Effect of Combining Hopfield Neural Network and Fuzzy Ranking on E-Learning System Performance |
title_fullStr |
The Effect of Combining Hopfield Neural Network and Fuzzy Ranking on E-Learning System Performance |
title_full_unstemmed |
The Effect of Combining Hopfield Neural Network and Fuzzy Ranking on E-Learning System Performance |
title_sort |
effect of combining hopfield neural network and fuzzy ranking on e-learning system performance |
publishDate |
2010 |
url |
http://ndltd.ncl.edu.tw/handle/99715491692851728085 |
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