應用大數據分析提供自動化數位教學影片分類並改善推薦機制

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姓名

蘇可耘(Ko-Yun Su) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

應用大數據分析提供自動化數位教學影片分類並改善推薦機制
(Applying Big Data Analytics to Improve Digital Teaching Videos Classification and Recommendation Mechanism)

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摘要(中)

隨著大數據時代的來臨，網路上充斥著各種免費的開放資訊，讓人們隨時隨地都能透過網路取得資源，其中開放教育資源提供了豐富的教學資源，是可以提供大眾終身學習的管道，但同時大量的教育資源也導致不好維護平台品質的問題，往往需要教育專家幫忙整理教材。因此，本系統透過影片的相關介紹，自動幫影片分類，期望能降低教育專家的負擔，同時將影片結合教育部訂定的課綱，產生影片的推薦清單，以API的形式提供服務，期望透過服務能夠改善平台的推薦機制。
本研究應用了目前熱門的大數據分析技術提供自動化數位教學影片分類並改善推薦機制，使用Python的機器學習套件Scikit-learn將影片自動分類。影片相關資訊由LearnMode學習吧提供，另外透過網路爬蟲收集知識架構與數學詞庫，藉由詞庫擷取教學影片的特徵。經過比較多種不同分類器的準確度後，選前四個高準確度的分類演算法，比較加入 LDA特徵萃取後準確度的差別，接著再從四種方法中選擇分類準確度前三高的分類器，加入投票機制，成為本系統最終的分類方法。
影片分類後結合知識架構使得每支教學影片具有觀看的先後順序，將每一筆影片的觀看推薦清單存入資料庫中，再設計一個API，只要傳送該影片的ID，即可從資料庫中找到該影片的推薦清單並回傳給使用者。影片推薦清單包含該節點與前後各兩節點中，最多人觀看的兩部教學影片，一共推薦10支，可以提供學生複習與繼續學習的教材，節省搜尋教材的時間，不僅能夠了解教學影片的關聯性，同時也能整合學習平台上的教學影片。

摘要(英)

With the coming of the Big Data, there are lots of free and open resources on the Internet, so people can access resources through the Internet anytime, anywhere. Open Educational Resources(OER) provides a wealth of teaching resources and people can use OER for lifelong learning. It’s very difficult to maintain platform which stores large number of OER, so it needs experts to organize teaching resources on the platform. In this research, I use machine learning to automatically classify teaching videos by video’s information and hope that classifying videos by machine can reduce the burden of education experts. The system produces the video recommendation list by combining classified videos with learning sequence designed from Ministry of Education. The system offers the recommendation service by API. I expect that users can modify the recommendation mechanism by using the API.

關鍵字(中)

★ 大數據分析
★ 多元分類
★ 線性判別分析
★ 支援向量機
★ 隨機森林
★ 類神經網路
★ 結合多個分類器

關鍵字(英)

論文目次

目錄
摘要 I
ABSTRACT II
圖目錄 V
表目錄 VI
一、緒論 1
1.1 開放教育資源(Open Educational Resources, OER) 1
1.2 LearnMode學習吧 2
1.3 研究問題 2
二、文獻探討 3
2.1 特徵萃取(Feature extraction) 3
2.2 多元分類(Multiclass Classification) 4
2.3.1 支持向量機(Support Vector Machine, SVM) 4
2.3.2 隨機森林(Random Forests) 5
2.3.3 類神經網路(Neural Network) 5
2.3.4 邏輯回歸(Logistic Regression) 6
三、系統設計 7
3.1 系統環境 7
3.2 系統架構 11
3.3 資料收集 13
3.3.1 教學影片 13
3.3.2 國中知識架構 14
3.3.3 數學詞庫 15
3.3.4 資料前處理 15
3.4 資料儲存 19
3.5 資訊萃取與分析 20
3.5.1 多元分類(Multi-class classification) 20
3.5.2 Voting Classifier 22
3.6 資訊應用 22
四、實驗設計 23
五、結果與討論 25
實驗一：使用LDA降維是否能提升教材分類準確度 26
實驗二：Ensemble Method分類結果是否能接近專家 27
實驗三：影片推薦服務是否能呈現影片相關性 28
六、結論與未來研究 30
七、參考文獻 32

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指導教授

楊鎮華

審核日期

2017-7-19

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