臺灣博碩士論文加值系統

在大數據的時代中，雜亂無章的資料充滿在生活之中，為了理解複雜資料背後的意義，而從資料探勘、計算機圖學、與人機互動介面等學科所衍生出的新學科：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，用來瞭解資料背後所隱藏的含意。
資料視覺化經常被使用於商業與科學領域中，用來提升商業利益或者是探索資料中未被發現的科學理論。而近幾年，MOOC形式的學習型態席捲了整個數位學習領域，為全球教育帶來了很大的衝擊，其線上與公開的特點吸引了眾多學習者，數位學習平台也如雨後春筍般地推出。隨著大量數位學習者的使用，也產生大量的學習者數據，若能有效整理並分析其背後所隱藏的涵義，相信能夠不斷地提升教學品質。
本研究製作一個資料視覺化分析網頁，此網頁將針對幾項數據進行進行圖形化呈現，並在分析之後我們可以快速提供如何改善建議，：
(1) 在各項目人數統計圖表中可以快速知道各項指標的人數統計。
(2) 在影片觀看比較圖表中我們可以針對觀看次數較左右兩側異常的影片加以分析，了解哪一種類型的影片能夠吸引到更多學生。
(3) 在熱門觀看時間圖表中可以發現在某些異常高峰區段往往是因為某些原因所造成的，例如國定假日、作業或測驗的繳交期限，由此可以觀察出老師或助教的期限日期訂定對學生的觀看意願有一定程度的影響。
(4) 在學生觀看影片的時間點探討上，可以問卷的形式來調查學生在某些時間點觀看影片的原因。
(5) 在討論區活躍程度分析圖表中可以看出某些學生活躍程度相較於其他學生來的高，可以藉此機會觀察活躍程度是否與成績之間有沒有甚麼關聯。
(6) 在測驗分析圖表中可以看出某些測驗的數量相較其他測驗來的高，可以藉此探討那些主題是學生較感興趣的課程，由總成績與同儕互評和討論區比較後發現，不少學生在這兩個部份的分數偏低，若能使學生提高得分意願的話，將有效增加課程通過人數。
(7) 利用學習曲線的判別能夠在下次開課時及早發現需要幫助的學生，並給予適當的關心，有利於提高通過率。
透過資料視覺化的分析，除了可以幫助教師快速地瞭解學習者的學習狀況，還能夠依據學習者觀看教學影片的行為來適當地改善教學內容，使教師能夠輕鬆掌握學習者的學習趨勢。

Chaotic information is full of life, 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.
In recent years, MOOC swept through the entire digital learning area. Global education has faced a great impact. Its online and open features attract a large number of learners, digital learning platform is also springing up.With the use of a large number of digital learners, but also produce a large number of learner data. I believe that can continue to improve teaching quality if we can analysis of the hidden meaning behind it effectively.
This study attempts to create a web of visualization analysis that will be graphically presented for several data and provide suggestions on how to improve after the analysis. The chart name is as follows:
1. Statistics on the number of indicators can be quickly know in the cahrt of statistics on the number of projects and grades.
2. Analyze the vedio which the number is abnormal on both sides in the cahrt of video watched comparison and understand which type of video can attract more students.
3. In the cahrt of video popular times, it can be found in some abnormal peak sections are caused by certain reasons, such as national holidays, deadline of homework or quiz which can be observed that the deadline for the students to realize the intention to have a certain degree of influence.
4. In the student watching the vedio at the point in time, we can investigate the reasons why students watch the video at some point in time.
5. It can be seen that the degree of activity of some students is higher than others in the chart of analysis of discussion area and it is a opportunity to exolore if there is any relationship between the degree of activity and the grade.



6. In the chart of analysis of test, it can be seen that the number of test is higher than that others. It can be used to explore the subjects that are more interested in the students. After comparing the final score and the peer review and discussion area, a large of student in these two parts of the low score. It will increase the number of courses effectively if students can improve the scoring in peer review and discussion area.
7. Learning curve : It can be found students who need help and give appropriate care.It can improve the pass rate.
Through the analysis of visualization of data, in addition to helping teachers to quickly understand the learning situation of learners, but also based on the learners to observe the behavior of videos to properly improve the videos content, so that teachers can easily grasp the learners learning trends.

摘要 I
Abstract II
誌謝 IV
目錄 V
表目錄 VII
圖目錄 VIII
第一章　緒論 1
1.1 研究背景與動機 1
1.2 研究目的 1
1.3 論文架構 2
第二章　文獻探討 3
2.1 資料視覺化 3
2.1.1 資料視覺化的由來 3
2.1.2 資料視覺化的定義與目的 3
2.1.3 資料視覺化的應用 6
2.1.4 視覺化工具 8
2.2 學習者數據的視覺化 13
2.3 數位學習平台 14
2.3.1 數位學習平台探討 14
2.3.2 Moodle數位學習平台探討 16
第三章　研究方法 18
3.1 開發工具與環境 18
3.1.1 開發工具的選擇 18
3.1.2 實驗環境介紹 18
3.2 實驗流程介紹 18
3.3 實驗數據介紹及取得方式 20
第四章　實驗結果 23
4.1 完成開發資料視覺化分析網頁 23
4.2 各類型土俵呈現、說明及分析 24
4.2.1 各項目人數統計與各項目成績 24
4.2.2 課程影片觀看比較 26
4.2.3 熱門觀看時間 31
4.2.4 討論區活躍程度 34
4.2.5 測驗分析 39
4.2.6 同儕互評分析 40
4.2.7 學習曲線 41
4.2.8 影片點擊事件 42
第五章　結論與未來展望 43
參考文獻 44

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