• 沒有找到結果。

第五章 研究結果與建議

5.2 研究建議

針對本研究之研究目的、系統架構以及實驗結果,歸納了下列七點可 供作未來改善及繼續研究之參考:

1. 本研究在計算學習者間的互動值時,並未特別區分學習者間的互動討 論是否是針對同一問題的來回互動,且針對這樣聚焦的互動討論給予 較高權重計算互動值,也就是忽略了互動討論是否具有方向性且深 入;也未針對互動內容進行自動分析是否其討論內容具有實質學習意 涵,在未來的研究中可將此關係納入到社會網路演算法中提供更適合 的社會網路關係分析,媒介更合適的學習伙伴。

2. 本研究目前只將合作式問題解決學習系統提供的訊息中心互動的情形 納入社會網路分析的考量,在未來的研究中可考量增加更多其他的互 動維度,例如:將留言版中學習者的互動情形考慮進來或者將聊天室 的互動也納入考量範圍,提供多方向的社會網路關係分析。

3. 本研究為了區分學習者個體互動強度的不同,在計算互動分數(公式 3-4)上區分出四個不同的互動強度,但此是否能完全代表學習者本身 的互動強度值還需作進一步的研究與修正。

4. 本實驗限於研究時間及人力考量,實驗對象人數為 33 人(實驗組為 15 人,控制組為 18 人),在實驗分析中實驗組及控制組在人數上均未達 到 30 人,無法假定母群呈現常態,故實驗的分析結果無法作廣泛推 論,未來應該設計更具代表性的大規模實驗進行更嚴謹的實驗分析 5. 在圖形介面上可更美化,提供及時圖形化界面將使用者關係展示給學

習者,讓學習者可更熱衷在互動學習上。

6. 本研究所提出的社會網路分析方法可以進一步應用於輔助網路合作式 學習之實際合作學習小組的分組上,過去的研究指出合作式學習成效

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不彰的原因在於未做到足夠的異質性分組,以及小組缺乏人際技巧較 佳的學習者帶領[17][31]。未做到異質性分組可能導致每一個成員對 於小組的貢獻有限;而缺乏人際較佳的學習者往往造成小組成員互動 不佳,失去合作式學習意涵。社會網路分析可以有效提供個人的人際 互動特質及社會地位關係,正好可以提供資訊輔助更好的網路合作式 學習分組。

7. 合作式學習社會網路是否能有效的降低學習者的孤立感與挫折感,在 未來研究中可以利用問卷作分析。

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個人簡介

姓名:張家成

就讀學校:國立台灣師範大學 系所:工業教育所電機電子組 學歷:輔仁大學電子工程系 專長:資料探勘

軟體能力:PHP、HTML、JAVA、ASP

研究方向:Cooperative learning social network and data mining 著作:

1. “ Mining Interactive Social Network for Recommending Appropriate Learning Partners in a Web-based Cooperative Learning Environment," 2008 IEEE International Conferences on Cybernetics & Intelligent Systems(EI).(accepted)

2. “基於自動查詢語句擴展之主題地圖智慧型新聞搜尋引擎," 十二屆 人工智慧與應用研討會(TAAI 2007)

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