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研究生:何宜儐
研究生(外文):Yi-Bin Ho
論文名稱:運用關聯規則分析測驗結果建構個人化學習回饋系統
論文名稱(外文):Personalized Learning Feedback System based on Mining the Assessment Results
指導教授:曾修宜曾修宜引用關係
指導教授(外文):Shou-Yi Tseng
學位類別:碩士
校院名稱:東吳大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
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:50
中文關鍵詞:資料探勘個人錯誤率學習回饋系統關聯規則補救強度
外文關鍵詞:Data MiningInitial Failed RateLearning Feedback SystemAssociation RulesRemedial Weight
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隨著電腦與網路科技的進步與普及,數位學習的進化為我們帶來了不同於以往對學習的刻版印象,學習者可以透過各式各樣的數位學習技術來獲得教材並參與學習活動,不僅可以提升學習的成效,也讓學習的模式變得更加多元化。因此,如何在豐富的數位資源中提供符合學習者個人需求的教材,已成為現今受重視的研究的議題之一。由於線上測驗系統的發展快速,經由測驗來探討個人的學習狀況以提升學習者的學習成效已成為一個值得研究的方向。若能經由分析大量測驗的過程與結果,並且依據個人的學習狀況找到適合個人所需的建議,將可大幅省去篩選教材的時間,改善學習行程的安排。
本研究嘗試發展一個有效的個人化學習回饋系統(Personalized Learning Feedback System),根據大量學生測驗的結果,整理出試題回答錯誤的情況後,應用資料探勘(Data Mining)中的關聯規則(Association Rules)發現章節關聯性,且經由概念間的關聯確認關聯規則的正確性,並結合個人學習情況的個人錯誤率(Error Rate)與章節權重的章節影響性,提供學生試題回答錯誤的情況,章節的誤解程度等回饋訊息,安排符合個人所需的章節補救資訊。目的是希望給予使用者合適的個人化學習回饋,並有效利用回饋建議,了解個人的學習歷程,改善個人學習成效。經由實驗結果顯示,學生在使用本系統後,測驗成績進步的幅度呈現顯著的表現。
With advances in computer and network technology and the popularity of e-learning evolution brought us different from the stereotypes of past learning. With amount of learning resources, the learners may confuse to choose the suitable learning materials for themselves. Therefore, a good feedback learning system to meet the individual needs has become an important research topic.
The online testing system has a rapid development in the recent years. The testing results are not only the scores to evaluate the learners, the testing answers can also provide valuable information about the learners learning details. By analyzing the large number of testing results and based on the individual's testing results, to find the necessary recommendations for individuals can improve the learners learning process on the e-learning system.
This study attempts to develop an effective personalized learning feedback system. According to a large number of students test results, we applied the technology of data mining to find the association rules between chapter sections. And then, we combined with personal error rate from testing answers and impact of the chapter sections to provide students feedback information. The feedback information includes the error answered questions, the degree of misunderstanding of chapter sections, and the personal information needed to remedy the learning. Purpose is to improve the learning effect by giving users personalized learning feedback and advising about personal learning process. Experimental results showed that students using this system, the magnitude of test scores showed significant progress in the performance.
誌謝
論文摘要
Abstract
目 錄
圖目錄
表目錄
1.緒論
2.文獻探討
2.1關聯規則分析
2.2概念圖
2.3個人化回饋
3.系統分析與設計
3.1系統架構
3.2章節關聯規則探勘模組
3.3個人學習回饋模組
4.研究結果
4.1實驗背景
4.2系統實作
4.2.1章節關聯分析
4.2.2個人回饋建置
4.3實驗設計與結果
4.4討論
5.結論與未來展望
參考文獻
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附件二、概念關係表 38
附件三、章節專有影響性 40
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