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研究生:歐懷文
研究生(外文):Ou, Huai-Wen
論文名稱:基於隱藏式馬可夫模組之電子書閱讀行為分析與推薦
論文名稱(外文):E-book Reading Behavior Analysis and Recommendation Based on Hidden Markov Models
指導教授:林育慈林育慈引用關係
指導教授(外文):Lin Yu-Tzu
口試委員:張貴雲陳恆佑
口試委員(外文):Chang Guey-YunChen Herng-Yow
口試日期:100/07/22
學位類別:碩士
校院名稱:國立暨南國際大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:71
中文關鍵詞:適性化學習電子書閱讀行為隱藏式馬可夫模組
外文關鍵詞:Adaptive LearningE-bookReading BehaviorHidden Markov Model
相關次數:
  • 被引用被引用:1
  • 點閱點閱:283
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:4
資訊與網路科技的發展使得線上學習日益普及,近年來電子書技術與應用更發展蓬勃,相較於傳統紙本書籍,能提供學習者更豐富的多媒體資訊。在缺乏面授教學的線上學習環境中,適性化學習的重要性更加提升。適性化學習可以依據學生的個別化特性調整教授方式以增加學生學習成效。然而目前的研究卻鮮少提供可適性化的電子書閱讀系統的相關探討與應用。
本研究建立一可適性化的電子書閱讀系統,能針對不同學習者提供適性化的閱讀建議。根據學生閱讀行為的分析結果,我們根據不同的行為類別提供不同的閱讀建議,以適合不同學生的需要。本論文所提學生閱讀行為分析之演算法是基於隱式馬可夫鏈,透過擷取學生的動態閱讀行為,我們能針對學生章節與媒體的閱讀順序進行閱讀行為的分類。在推薦閱讀行為的步驟中,我們根據學生閱讀行為所探勘出的閱讀認真程度給予閱讀推薦。
實驗結果顯示本研究所提之學生閱讀行為分析與推薦演算法是可行的。在基於隱式馬可夫鏈的學生閱讀行為分類上,我們可以達到正確的分類結果;在閱讀行為推薦上,研究所提方法亦可針對不同學生作有效的推薦。因此,本研究的確可為未來適性化電子書閱讀系統提供一可行的研究方向,增加未來的線上閱讀環境的學習者閱讀行為分析與推薦。
With the rapid development of information and network technology, more and more teachers utilize e-books in the class which provide not only multimedia contents but also more interactive and friendly user interfaces and thereby change students’ reading behaviors. Therefore, exploring the design elements and principles of e-books and evaluating their corresponding effectiveness for teaching purposes are significant issues in the field of education. However, few existing works analyzed learners’ e-book reading behaviors or considered the adaptability of the e-book for the purpose of giving different functions and recommendations for different users. In this study, we try to build an adaptive e-book system which providing recommendations for e-book readers according to the analyzing results of their reading behaviors. The proposed algorithms of reading behavior analysis and recommendation are based on Hidden Markov Models, with which students’ dynamic reading behaviors can be understood by extracting the features of sequential reading actions and producing corresponding suggestions according to the results of reading behavior classification. By conducting both technical and subjective experiments, the proposed reading behavior analysis and recommendation system is proved to be feasible for students’ e-book reading.
誌謝 I
論文摘要 II
Abstract III
目錄 IV
圖目錄 VI
表目錄 VII
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 研究範圍與現制 2
第二章 文獻探討 4
2.1 適性化學習 4
2.2 適性化學習上的行為分析 5
2.3 學習特性分類 6
2.4 電子書 8
2.5 推薦系統 10
第三章 閱讀行為分類與推薦 12
3.1 電子書閱讀行為分析與分類 12
3.2 系統流程 23
3.3 閱讀行為分類 24
3.4 推薦章節閱讀行為 37
第四章 實驗結果與討論 42
4.1實驗架構 42
4.2分類分析 42
4.3推薦系統的實驗 64
第五章 結論與未來展望 66
參考文獻 68
附錄 一
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