博碩士論文 102522076 詳細資訊




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姓名 柳嘉樺(Chia-hua Leou)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 發展智慧評量機制於大規模開放線上課程提升學生的學習成效之影響:以均一平台為例
(Effectiveness of smart evaluation on improving students’ learning performance in MOOCs: 以均一平台為例)
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摘要(中) 大規模開放式線上課程(MOOCs)讓教師、學生及自學者不受時間、地點 的限制,透過網路連線即可立即取得學習資源。同時透過線上評量的自動閱卷功 能更能讓學生即時取得回饋,達到適性化的評量,而目前台灣使用者人數較多, 發展較完全的 MOOCs 平台為均一教育平台。然而,均一平台缺乏學習能力檢測 機制造成三項缺點,1:學生缺乏引導,2:教材推薦不準確,3:答題機制不完 善。因此,本研究提出基於兩階段式評量策略之智慧評量機制來解決均一平台所 面臨的三項問題。智慧評量機制是由教師先設定教學目標,接著透過診斷性評量 的結果確認學生的先備知識程度,再依據形成性評量的結果來調整教學方法,最 後透過總結性評量確認學生對於課程內容的熟練程度。而研究結果顯示本研究所 提出的智慧評量能讓學生產生正向的學習態度,並提升學生的學習成效和沉浸程度。
摘要(英) MOOCs have become an important trend in recent years. The main advantages of taking an evaluation on MOOCs are the automatic marking, the instant feedback toward students, and the fact that it only requires Internet connection. However, there is lack of a strategy to knowing exactly students’ capability and competence, not to mention delivering pinpoint materials for learning. To deal with this problem, this study presents a smart evaluation system called Destinee that employs a two-tier test aligning with diagnostic, formative, and summative assessments. The experiment results from a math course of fifth graders show that this approach is able to improve students’ learning performance as well as their online course engagement.
關鍵字(中) ★ 智慧評量
★ 兩階段式評量
★ 大規模開放式線上課程
關鍵字(英) ★ Smart evaluation
★ two-tier test
★ MOOCs
論文目次 摘要.i
Abstract..ii
目 錄 iii
圖目錄.. v
表目錄..vi
第一章 緒論 1
第二章 文獻探討 3
2.1 兩階段式評量策略. 3
2.2 線上評量系統. 4
第三章 線上智慧評量系統 Destinee6
第四章 實驗設計..12
4.1 實驗對象 12
4.2 實驗教材 12
4.3 實驗流程 13
4.4 研究工具 14
4.4.1 學習成效測驗14
4.4.2 學習態度量表15
4.4.3 沉浸程度量表15
第五章 結果與討論..17
5.1 學習成效. ..17
5.2 學習態度 19
5.3 沉浸程度. ..21
第六章 結論與建議..23
6.1 結論 23
6.2 建議 24
參考文獻 25
附錄、學習態度與沉浸程度問卷 31
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指導教授 楊鎮華(J.H. Yang) 審核日期 2015-7-14
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