臺灣博碩士論文加值系統

隨著現今大數據的時代中，科技的進步普及已經是不可小覷的因素，隨著數位科技的發展，在吸引全球各地的學員進行修課，MOOCs成為當今高等教育數位學習的重要趨勢。因此在課程數據資料繁多且複雜的資料量中，為了瞭解資料背後真正的意義，從資料探勘、計算機圖學、與人機互動介面等科目所衍生出的新學科：資料視覺化，資料視覺化藉由圖形化方式提供了一條快速、清晰的捷徑，來瞭解資料背後所隱藏的含意。
本研究透過使用 Tableau進行實作範例ewant課程數據，利用資料視覺化屬性分類，再來將資料對應到視覺屬性呈現，決定哪一種視覺屬性來表達資料類型是最有效率，方便操作者以圖形化即時性呈現圖表工具、儀表板、資料視覺化等工具。
在四門MOOCs課程分析結果進行討論，根據研究結果發現基礎學科重視理論性知識，知識門檻較高；應用學科重視經驗與應用，知識門檻較低；硬學科知識客觀且有標準答案，重視逐步累積知識而成的；軟學科知識主觀而無標準答案，重視個人觀點並需要多人建議表達。
在分析資料視覺化分析後除了可以幫助，課程開課教師與研究者快速瞭解學習者的學習狀況，且能依據分析圖表來適當改善教學內容、教學策略與課程經營等，使未來相關教學設計之借鏡參考。
透過本研究應該建立一套SOP來針對未來各項課程可套用之模式，並且由數據分析，建立模型以提供未來課程開課教師與研究者使用，以利後續研究者參考。

With the current era of big data, the advancement of science and technology has become an indispensable factor. With the development of digital technology, attracting students from all over the world to take courses, MOOCs have become an important trend in digital learning in higher education today. In order to understand the meaning behind the complex information, and from data exploration, computer graphics, and human-computer interaction interface and other disciplines derived from the new disciplines: data visualization, visualization of data by graphics Way to provide a fast, clear shortcut, used to understand the hidden meaning behind the information.
This research adopts Tableau software to analyze the data of the broad band provided by ewant course data, by completing the classification of the data type, we map the data and its visible attributes to decide what kind of attributes to express the data type is the most efficient. These presentations include real-time reporting tools, dynamic dashboard, and data visualization tools such as simulation.
The results of the analysis of the four MOOCs courses are discussed. According to the research results Pure Discipline emphasis on theoretical knowledge, high knowledge threshold; Applied emphasis on experience and application, lower knowledge threshold; Hard Discipline knowledge is objective and has standard answers, and it is important to gradually accumulate knowledge; Soft Discipline knowledge is subjective and there is no standard answer, and personal opinions are emphasized and many people are required to express their opinions.
After data analysis in addition to visual analysis can help teachers and researchers Courses fast learner understand learning situation, based on analysis of the chart and can be appropriate to improve the teaching content, teaching strategies and curriculum management, so as to make reference to the future related teaching design.
Through this research, a set of SOP should be established to map the models that can be applied in the future courses, and data analysis and modelling will be provided to provide future courses for teachers and researchers to use for reference by follow-up researchers.

摘要 I
Abstract II
誌謝 IV
目錄 V
圖目錄 VII
表目錄 X
第一章　緒論 1
1.1 前言 1
1.2 研究動機與目的 2
1.3 研究流程 3
第二章　文獻探討 4
2.1 資料視覺化(Data Visualization) 4
2.1.1 資料視覺化的定義 5
2.1.2 資料視覺化的圖形 7
2.1.3 資料視覺化的相關應用 8
2.1.4 視覺化工具 9
2.2 學習分析(Learning Analytic) 11
2.2.1 什麼是學習分析 11
2.2.2 學習分析方法 11
2.3 學科差異 13
第三章　研究方法 15
3.1 研究架構與流程 15
3.1.1 研究架構 15
3.1.2 研究流程 15
3.2 研究方法 17
3.2.1 研究對象 17
3.2.2 實驗數據處理與分析 25
3.2.3 實驗數據視覺化分析 28
第四章　實驗結果 29
4.1 四門MOOCs課程影片觀看次數 29
4.2 四門MOOCs課程影片觀看時間 43
4.3 四門MOOCs課程討論區分析 49
4.4 四門MOOCs課程測驗分析 59
4.5 四門MOOCs課程同儕評分分析 64
4.6 四門MOOCs課程學習曲線分析 66
4.7 小結 69
第五章　結論與未來展望 70
5.1 結論 70
5.2 研究建議 71
5.2.1 MOOCs課程規畫者 71
5.2.2 未來展望 71
參考文獻 72

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