Analysis of Key Factors in Constructing Decision-making Model of Customized Early Childhood Education Teaching Method

• Main Authors: CHANG,CHAO-HSUAN, 張櫂烜
• Other Authors: HSIEH,HSIN-JUNG
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/q877m8

碩士 === 開南大學 === 商學院碩士在職專班 === 107 === Taiwans social environment is changing rapidly. Coupled with the impact of the poor environment, more and more couples are reluctant to have children, and even at most they are willing to have one. The proportion of only children is also rising. However, children are the hope and master of the countrys future construction, and also the key to the future prosperity and strength of the nations nationality. Under the premise that early childhood education is an important national basic education that cannot be abandoned and failed, every kindergarten is trying to find its own. Competitive advantage, I hope to provide new thinking direction for early childhood education in the field of strengthening early childhood education. And we found out how to adapt to the content of customized education for each child, which is the best way to improve the learning effect of young children. Therefore, this study chose to use AHP and DEMATEL two research methods, combined with genetics-based cortical texture verification technology, hope to find the best solution for customized childrens teaching methods according to their characteristics. . This study analyzed five patterns of cortex patterns that can be found most commonly in young children. It was found that early childhood courses with arc pattern (A) characteristics are the most difficult to design and plan, and the difficulty is also higher than other. The pattern is complex, so it is necessary to plan for the arc pattern (A) in the design of childrens curriculum, which is also in line with the future development trend of domestic early childhood education. Keywords：Preschool education industry, Cortical pattern detection, Customized, Key Success Factor