博碩士論文 109552010 詳細資訊




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姓名 林吳憲(WU-SIAN LIN)  查詢紙本館藏   畢業系所 資訊工程學系在職專班
論文名稱 應用關聯規則與機器學習探索閱讀行為與學生學習成效關係
(Applying association rules and machine learning to explore the relationship between reading behavior and student academic performance)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2027-7-19以後開放)
摘要(中) 隨著網路與資訊科技的持續發展,線上數位學習越來越盛行,學生學習的管道不再侷限於面對面的實體課程。同時,2020受到COVID-19新冠肺炎疫情的影響,使得人們學習需求與學習的行為有了非常大幅度的改變。因疫情的影響使人們需保持社交距離的政策,讓世界各國對線上數位學習的重視程度有明顯的提升。常見的線上數位學習平台有Coursera、Moocs、edX,及本論文研究的BookRoll線上電子書學習平台。

而BookRoll線上電子書學習平台是由日本京都大學所開發的一套電子書閱讀系統,不僅支援行動學習,也提供了書籤(Bookmark)、標記(Marker)、備忘錄(Memo)等相關功能等,其系統能夠記錄學生的所有閱讀行為,藉此蒐集學生完整的學習日誌(Log)。同時能搭配複習系統、概念評量系統、程式評量系統來評估學生的學習狀況。

本論文研究將使用學生於BookRoll線上電子書學習平台上的閱讀行為紀錄、複習、評量系統上的答題紀錄作為數據基礎。透過資料前處理提取特徵出來,並先使用統計方法,來分析學生閱讀行為與學習成效之間的關聯性。接著利用關聯分析來探索閱讀行為與學生學習成效的關係,最後,使用機器學習分類演算法對學生的學習成效進行預測分類,並比較不同的分類演算法的預測成效。
摘要(英) With the continuous development of the Internet and information technology, online digital learning has become more and more popular, and the way students learn is no longer limited to physical courses. Besides, society was affected by the COVID-19 pandemic in 2020, which made people′s learning needs and learning behaviors have changed significantly. Due to the pandemic, social distancing is a new policy that has apparently increased the importance of digital learning around the world. Common online digital learning platforms include Coursera, Moocs, edX, and the BookRoll, the online e-book learning platform studied in this paper.

The BookRoll online e-book learning platform is a reading system developed by Kyoto University in Japan. It not only supports active learning but also provides Bookmark, Marker, Memo and other functions. The system collects students’ complete learning logs by recording all the reading behaviors of students. Moreover, it can be used with a review system, concept evaluation system, and program evaluation system to evaluate students′ learning situations.

Students′ reading behavior records, review records, and answer records on the evaluation system on the BookRoll online e-book learning platform will be applied as the data basis in this research. Features are extracted and statistical methods are used to analyze the correlation between students′ reading behavior and learning outcomes. Next, analyzing the relationship between reading behavior and students′ learning outcomes by the association rule. Lastly, using machine learning classification algorithms to predict and classify students′ learning outcomes and compare the predicted performances of different classification algorithms.
關鍵字(中) ★ 線上數位學習
★ 電子書
★ 資料探勘
★ 關聯規則
★ 機器學習
★ 決策樹
★ 隨機森林
★ 極限梯度提升
★ 邏輯斯迴歸
★ 支援向量機
★ 單純貝氏
★ k鄰近演算法
關鍵字(英) ★ Online digital learning
★ e-book
★ Data Mining
★ Association Rule
★ Machine Learning
★ Decision Tree
★ Random Forest
★ XGboost
★ Logistic Regression
★ Support Vector Machine
★ Naive Bayes
★ k-Nearest Neighbors
論文目次 摘要 i
ABSTRACT ii
致謝 iv
目錄 v
圖目錄 vii
表目錄 viii
一、 緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 3
二、 文獻探討 4
2.1 電子書系統與閱讀行為 5
2.2 線上學習行為相關研究 5
2.3 資料探勘 7
2.3.1 資料探勘的定義 7
2.3.2 資料探勘的功能 7
2.3.3 資料探勘的流程 8
2.4 探索性因素分析 10
2.4.1 主成份分析 10
2.5 關聯規則 11
2.6 機器學習分類演算法 13
2.6.1 決策樹(Decision Tree) 15
2.6.2 隨機森林(Random Forest) 15
2.6.3 極限梯度提升(XGboost) 16
2.6.4 邏輯斯迴歸(Logistic Regression) 16
2.6.5 支援向量機(Support Vector Machine) - SVC 17
2.6.6 支援向量機(Support Vector Machine) - Linear SVC 17
2.6.7 單純貝氏(Naive Bayes , NB) 17
2.6.8 K鄰近演算法(K-Nearest Neighbors , KNN) 17
2.7 機器學習模型評估 18
2.7.1 機器學習模型的評估方法 18
2.7.2 機器學習模型的評估指標 19
三、 研究內容與方法 21
3.1 系統架構 21
3.2 研究平台 22
3.3 課程描述 22
3.4 資料集描述 23
3.4.1 BookRoll Log各欄位定義 25
3.5 資料前處理 26
3.5.1 電子書閱讀動作編碼 26
3.5.2 關聯規則分析(Association Rule)特徵離散化 28
3.5.3 機器學習(Machine Learning)成績離散分群 34
四、 研究結果與討論 35
4.1 實驗環境 35
4.2 探索性因素分析 35
4.3 探討學生閱讀行為與學習成效之關聯(使用T檢定) 37
4.4 探討學生於複習/評量系統與學習成效之關聯(使用T檢定) 40
4.5 探討閱讀行為特徵與學習成效之關聯(Spearman) 42
4.6 探討複習/評量系統特徵與學習成效之關聯(Spearman) 44
4.7 關聯分析結果(基於學生學期總成績) 45
4.7.1 關聯分析結果進行T檢定 49
4.8 機器學習分類演算法預測成效比較 54
五、 結論與未來研究 60
參考文獻 61
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指導教授 楊鎮華(Stephen J.H. Yang) 審核日期 2022-8-13
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