臺灣博碩士論文加值系統

巨量資料分析話題持續延燒，該如何運用資料分析讓資料進行加值，是目前的一個重要議題，不僅僅只是產業界欲利用資料加值創造競爭優勢，如今因社會環境逐漸變遷，少子化現象成為未來隱憂，同時大專院校自主權增加，需自行承擔招生壓力，該如何將有限的校務資源達到最妥善運用，是大專院校目前需要克服的問題。國外利用資料分析進行「校務研究」（Institutional Research）目前已行之有年，目的為分析校園內、外部資料，將資源使用率最大化或提供決策資訊，這股風氣漸漸蔓延至國內，國內開始有大專院校發展校務研究資料分析平台。
在現有校務研究資料分析平台中，常見於分析課程與學生學習成效，但學生學習成效至目前為止，定義相當模糊，沒有統一基準進行評估。有鑑於此，本研究針對學生學習成效部份，試圖定義出學生學習成效之落點族群，目標在於發現可能需要接受輔導之學生，並找出影響學習成效之關鍵指標變數，以供後續區別預測與追蹤，本研究以C大學資訊管理系為例。
本研究使用分群的方式，區分出學生之學習效果，再對學習效果進行預測，可準確預測出可能需要接受輔導學生群組。根據研究結果顯示，前一學期的成績類變數，具有高度關聯性，其中以大二下學期時，上學期資管課程平均影響最甚，使用該變數預測結果最為精準。

Big Data continues popular. The issue pertaining to ways of utilizing data analysis for value-added data becomes important, which is not just because industries intend to make use of its creating competitive advantages, and current social environment changes gradually that declining birthrate is worthy of worry in the future, but also that the autonomy of universities and colleges enlarges which make them bear enrolling pressure on their own. In light of this, how to make the most proper use of limited school resources to achieve the most appropriate use of institutional resources becomes an issue needing universities and colleges to overcome. These of data analysis for "institutional research" has been implemented for years, which aimed at analyzing campus data, external data, and maximize resource utilization or provide decision-making information. This trend spreads gradually throughout Taiwan, and universities and colleges of Taiwan began to develop institutional research data analysis platform.
Many of the current platform of institutional research target on the analysis of curriculum and student learning effectiveness. However, as of now, the definition of student learning effectiveness is quite vague without uniform benchmarks for further evaluation. For this reason, this study intended to define target group in evaluating student learning effectiveness, aimed at finding out students who need counseling, and identifying key indicator variables among for subsequent differentiated prediction and tracking. Department of Information Management, C University was served as the subject in the case study.
Finally student’s learning effectiveness was clustered and distinguished and further for prediction. From these, we may predict student groups who might need counseling accurately. According to the findings, in the last semester, effectiveness variables had high degree of correlation, among of which, in the 2nd semester as a sophomore, the average score of the course titled Information Management was affected to the most, where prediction via such variables generated the most accurate effectiveness.

目次
致謝 I
摘要 II
目次 IV
圖目次 V
表目次 VI
第一章緒論 1
第一節 研究背景與動機 1
第二節 研究目的 2
第三節 研究步驟 3
第二章 文獻探討 4
第一節 Big Data 4
第二節 R語言（R Language） 9
第三節 校務研究（Institutional Research） 11
第四節 雲端運算 17
第三章 現況分析與研究方法 19
第一節 執行程序 19
第二節 定義研究問題 21
第三節 資料取得狀況與分析工具 22
第四節 資料處理流程 23
第五節 資料分析方法 24
第六節 變數說明 26
第四章 資料分析 27
第一節 有效樣本之敘述性統計分析 27
第二節 100屆大二第二學期學生線性迴歸分析 33
第三節 96至99屆學生線性迴歸分析 36
第四節 100屆大二第二學期學生進行區別分析 39
第五章 結論與建議 43
第一節 研究結果 43
第二節 管理意涵 46
第三節 研究限制 47
第四節 未來研究建議 48
參考文獻 49
一、 中文部分 49
二、 英文部分 49
三、 網路資料 50

圖目次
圖1. 本研究步驟流程圖 3
圖2. R語言使用介面 10
圖3. 執行程序 20
圖4. DFD資料處理流程圖 23

表目次
表 1 大數據資料來源 4
表 2 R語言優缺點整理 10
表 3 國內校務研究暨資料分析平台整理 16
表 4 96至100屆學生資料變數介紹 26
表 5 100屆學生資料描述性統計分析 28
表 6 96至100屆學生資料描述性統計分析 29
表 7 資管系所開設院必修與系必修課程 30
表 8 資管系次領域之必選修課程 31
表 9 96至100屆學生資料平均數 32
表 10 96至100屆學生資料標準差 32
表 11 100屆學生迴歸分析結果 33
表 12 100學年入學學生大一大二必修課程之相關分析 34
表 13 96至99屆學生迴歸分析結果 36
表 14 100學年入學學生大一大二必修課程之相關分析 37
表 15 100屆大二第二學期學生區別分析結果 39
表 16 Box’s M檢定 39
表 17 典型區別函數 40
表 18 Wilks’Lambda區別函數檢定 40
表 19 結構矩陣分析 40
表 20 Fisher線性區別重心係數 41
表 21 區別分析預測結果 41

中文文獻
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林世昌, 周愛倫, &陳信文. (2014). 清大學習評鑑中心-行政服務與校務研究結合的新體制. 評鑑雙月刊(48), 32-35.
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謝寶煖. (2006). 判別分析.
英文文獻
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網路資料
CRAN. CRAN, from http://cran.r-project.org/
University, B. S. (2015). Office of Institutional Effectiveness from http://cms.bsu.edu/about/administrativeoffices/effectiveness
Wikipedia. Discriminant function analysis, from https://en.wikipedia.org/wiki/Discriminant_function_analysis
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Wikipedia. S語言, from https://zh.wikipedia.org/wiki/S%E8%AA%9E%E8%A8%80
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