The Effect of Combining Hopfield Neural Network and Fuzzy Ranking on E-Learning System Performance

• Main Authors: Shu-Hoa Ye, 葉書豪
• Other Authors: Yen-Chu Hung
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/99715491692851728085

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.