臺灣博碩士論文加值系統

　　近年來，學生藉由觀看磨課師平台上之影片進行學習已是新趨勢。有別於傳統教學模式，磨課師平台使得學生可隨時隨地透過網路學習，並從參與課程中學習新知識。研究資料顯示，學生觀看影片時之感受將會影響是否繼續學習意願，但目前磨課師平台缺乏反映學生學習狀態之機制，且磨課師平台並非由教師進行面對面教學，使得教師不易從學生方得到教學反饋，導致教師未能有效了解學生學習狀況並加以調整課程。故如何在不需詢問學生之狀況下提供教師了解學生學習狀況下，建議教師可能需要進行修改之影片片段，以利增進影片品質及減少學生學習困難為重要課題。
　　根據研究顯示，學生於磨課師平台上最頻繁使用之功能為瀏覽影片。因此，本研究以視覺化方式呈現學生對影片之操作行為，提供教師清晰明瞭之圖形以觀察學生行為，進而推知學生學習狀況。有鑑於此，本研究提供跳轉區間圖、影片事件圖、影片瀏覽量等三種圖形進行分析。其中，跳轉區間圖顯示學生經常重複觀看之區間；影片事件圖可提供影片事件分布及事件數急遽增加區間；影片瀏覽量則提供影片瀏覽次數及觀看日期分布。本研究所提供之三種圖形除了可找出具特殊行為之影片片段，也可輔助教師根據圖形找出影片需修改處進行修改，減少因遇到困難而停止學習之學生，進而提升完課率。

More and more students start to gain knowledge by watching videos in massive open online courses (MOOCs) in recent years, but most of the MOOC platforms lack of interfaces to reflect students’ learning behavior. Some students may feel confused while learning, but the instructor does not aware this problem. As encountering difficulties, the depressed students may eventually discontinue learning.
So as to solve the problems above, we could make use of the action logs which MOOC platforms record while students using the system. According to the research, students spend most of their time watching videos on the platforms. In this paper, we visualize the video viewing behaviors of learners, such as play, pause, seek and stop events. The results should provide an insight into the students’ learning behaviors. Instructors can know the sections which needs to be improve with the graphs. After modifying videos, students could have better learning experience than before. As a result, the completion rate of the course should increases. By the graphs, some interesting viewing behaviors are also discovered.

摘要 i
ABSTRACT ii
目錄 iii
圖目錄 vi
表目錄 vii
1 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
2 文獻探討 4
2.1 磨課師 4
2.2 完課率 4
2.3 影片行為分析 5
3 系統設計 9
3.1 開發環境 9
3.1.1 Apache 9
3.1.2 PHP 9
3.2 系統功能 9
3.2.1 影片瀏覽量 9
3.2.2 影片事件 10
3.2.3 跳轉區間圖 11
3.3 系統架構 11
3.3.1 收存取用四大步驟 11
3.3.2 資料分析流程 12
3.4 資料收集 13
3.4.1 資料簡介 13
3.4.2 追蹤紀錄 13
3.5 資料儲存 14
3.5.1 影片操作紀錄 14
3.5.2 影片長度紀錄 16
3.5.3 課程基本資料 17
3.5.4 影片基本資料 17
3.6 資料萃取與分析 18
3.6.1 影片瀏覽量－整體課程 18
3.6.2 影片瀏覽量－單一影片 18
3.6.3 影片事件處理 19
3.6.4 影片事件－整體課程 19
3.6.5 影片事件－單一影片 19
3.6.6 峰值找尋演算法 19
3.6.7 跳轉區間－單一影片 22
4 結果 23
4.1 整體課程畫面 23
4.1.1 影片瀏覽量 24
4.1.2 影片事件圖 25
4.2 單一影片畫面 25
4.2.1 跳轉區間圖 26
4.2.2 影片事件圖 27
4.2.3 影片瀏覽量 28
5 討論 29
5.1 完成課程與否之倒轉行為差異 29
5.2 搭配實體課程之課程與開放式課程之影片瀏覽量差異 31
5.3 搭配實體課程之影片瀏覽日期推進 32
6 結論及未來研究 35
參考文獻 36

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