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研究生:陳柏旭
研究生(外文):Po-Hsu Chen
論文名稱:應用資料探勘技術分析影響學生學習成效因素之研究
論文名稱(外文):An Application of Data-Mining Technology in Exploring Influence Factors of students Learning Performance
指導教授:謝楠楨謝楠楨引用關係
指導教授(外文):Nan-Chen Hsieh
學位類別:碩士
校院名稱:國立台北護理學院
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:92
中文關鍵詞:資料探勘資料倉儲學習成效自我組織映射圖類神經網路
外文關鍵詞:Data MiningData WharehouseLearning PerformanceSelf-Organizing MapsArtificial Neural Networks
相關次數:
  • 被引用被引用:0
  • 點閱點閱:291
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:4
以現行各大專院校校務行政系統來說,累積了大量且完整的學生學籍資料庫與成績紀錄資料庫以及選課紀錄等資料庫,在其中可能隱藏很多對於教學與學習有正面助益但未被發掘的知識,本研究就是透過資料探勘技術於學生學習相關資料庫找出潛藏在其中的知識,並將其運用於輔助學校規劃課程與輔導學生學習等方面。
本研究中,我們以國立台北護理學院為例,主要分析及挖掘88學年至94學年間大學部及研究所學生成績與其學籍資料,藉以分析學生學習成效,其研究有以下三個方向:
一、整合異質、分散於不同資料庫的學生成績資料與學生學籍資料,發展出出最易於維護、最能有效進行分析的資料倉儲架構與建置步驟。
二、利用資料探勘技術,將學生依學籍資料、學習成效等相關因素分群,透過自我組織映射圖將學生分為平均學業表現平均以上、平均學業表現一般、平均學業表現平均以下共三群。
三、利用線上分析技術深度分析分析學生的學習成效是否與入學身份、畢業學校以及居住地區有潛在的關係,做為未來學校制定更好的招生與學習策略。
Most of the administrative systems for universities and colleges, has accumulated a large amount of datasets of the student status, grade record and course enrollment, etc.. There might have useful but potential knowledge about teaching and learning that has not been explored. In this study, we proposed a data-mining mechanism for the analyzing of learning-relative database. The finding useful knowledge will apply to course planning, learning assistance and student guidance .
For this study, learning-relative datasets were provided by administrative database of National Taipei College of Nursing. Two major datasets were obtained: a set containing student grade report, and another set storing individual academic status records for these classical students from 1999 to 2004. Then, two datasets were joined using a student identifier to create a single learning-performance dataset for the successive mining tasks. This study has the following three objectives:
1. For the successful of data mining, we first integrate heterogeneous and disperse student grade, student status and student enrollment datasets, in achieving an easy maintenance and operational data warehousing architecture.
2. A conceptual student learning performance model was established to classify groups of students based on previous learning behavior and student status. This SOM was employed to classify students into three major groups of student: good student, general student, and bad student.
3. Once identified the groups of students, the OLAP technique profiled each group of students focusing on learning performance, demographic and geographic for building and maintaining the most noticeable student base. The student profile then were used to describe a representative case in each group of students, and served as a decision tool for establishing better enrolling and learning strategies.
摘要 I
Abstract II
誌謝 IV
目錄 .V
圖表索引 VI
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機 3
第三節 研究範圍與限制 5
第四節 論文架構 6
第二章 文獻探討 7
第一節 資料探勘 7
第二節 資料倉儲 14
第三節 資料探勘在高等教育之應用 24
第四節 群聚分析 27
第三章 研究方法 32
第一節 研究架構 32
第二節 資料描述與範圍 34
第三節 資料前處理 39
第四節 學生學習成效分析方法 41
第五節 以自我組織映射圖類神經網路評估學生學習表現 45
第六節 資料倉儲之星狀架構 48
第四章 資料分析與研究結果 53
第一節 敘述性統計分析 53
第二節 OLAP 分析 64
第五章 研究結論與建議 77
第一節 研究結論 77
第二節 對未來建議 78
參考文獻 79
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