南華大學機構典藏系統:Item 987654321/25921
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    题名: 以資料採礦探討多元入學學生之流失率與學業表現之研究
    其它题名: A Study on Dropout Rates and Course Performances of Multiple-enrolled Students Using Data Mining Techniques
    作者: 林青翰
    LIN, CHING-HAN
    貢獻者: 資訊管理學系
    邱宏彬
    CHIU, HUNG-PIN
    关键词: 資料採礦;決策樹;學生流失
    data mining;decision tree;student dropout
    日期: 2017
    上传时间: 2017-12-08 17:20:41 (UTC+8)
    摘要: 　　有鑑於國內少子化的影響，大專校院學生數劇減在民國 95年至105年間呈現負成長，招生日趨競爭，若能減少在校學生的流失，對學校而言則是一大助力。本研究以一大學 102學年度入學大學日間部資管系學生歷史學籍資料，運用資料採礦技術決策樹分析，找出多元入學學生流失率與學業表現等因素，提供相關之建議以降低學生流失。　　本研究共取得有效資料 76筆，在資料特性分析發現學生學業表現以繁星推薦學生學業表現仍為各入學管道中，學業表現較為突出部分；反觀轉學考進入學校之學生學業表現仍為最弱；所以學業表現與學生入學方式有關。在學生流失率部份，與居住地區和入學方式有關。在資料採礦部分發現，以學業總成績為最重要的因素，入學方式與居住地區為其次因素，再其次為性別與班級。
    　　In view of the impact of the domestic minority, college students in D.C. 2006 to 2016 years showed negative growth, enrollment increasingly competition, if the loss of students in school, the school is a big help. This study uses the data mining technology decision tree analysis to find out the factors such as the loss rate of the students and the academic performance of the students, and provide relevant suggestions to reduce the loss of students.　　In this study, a total of 76 valid data were obtained. In the analysis of the data, it was found that the academic performance of the students was still recommended by the stars. The academic performance of the students was still the most prominent part of the students. The academic performance of the students was still the weakest. So academic performance and student enrollment. In the part of the student's wastage, related to the area of residence and admission. In the data mining part of the discovery, to academic total score for the most important factor, school enrollment and residential areas as the second factor, followed by gender and class.
    显示于类别:[資訊管理學系] 博碩士論文

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