線上討論的知識建構模式特徵 -以 批 踢 踢 實 業 坊 的 微 積 分 討 論 為 例;The patterns feature of knowledge construction in online discussion: A case study

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题名:	線上討論的知識建構模式特徵 -以批踢踢實業坊的微積分討論為例;The patterns feature of knowledge construction in online discussion: A case study
作者:	許弼凱;Hsu,Bi-Kai
贡献者:	數學研究所
关键词:	序列分析;知識建構模型;非同步線上討論;IAM;lag-sequential analysis;CMC;nonsynchronous discuss
日期:	2012-07-16
上传时间:	2012-09-11 18:44:29 (UTC+8)
出版者:	國立中央大學
摘要:	自從網際網路的普及之後,線上的專業討論活動也隨之蓬勃發展。我們透過滯後序列分析(lag-sequential analysis) 與 IAM(Interaction Analysis Model) 的五個分類方法,結合了定量分析(quantitative content analysis)與序列分析(sequential analysis)。希望可以了解在無明顯指導者存在的線上討論平台中,使用者與使用者之間,所產生知識建構層次的深度與線上互動交流模式。我們取得一個時間區段內(2010/09/01~2011/01/31)、523 個不同 ID 使用者的資料,對他們的行為做了互動模式編碼與知識建構層次編碼。結果我們發現,雖然電子佈告欄是一個純文本的系統,與多媒體學習系統在介面上有很大的差異。但是對結果來說,此系統的知識建構層次與 problem-solved based online form(Hou et al., 2008)的性質相近。比起其他存在教師參與的網路多媒體論壇,相對的會有較高層次的知識建構與成效。在互動模式分析中,發現了一個可能是回答者願意回答的動機-鼓勵。也發現到此分析方法的缺點與提供之後的研究可以如何修改模型,以進行更深的討論。The online professional discussion has grown rapidly with the popularization of the Internet. And we wish to know the depth of knowledge construction level and the online interaction model between users without supervisor. We use five different methods include lag-sequential analysis, five sorting model of IAM (interaction analysis model), quantitative content analysis and sequen- tial analysis. We have got data from 523 different user in a time period (2010/09/01 2011/01/31), and we encode their interaction model and knowl- edge construction level. Finally we found that although BBS is a pure texture system- its user interface is much different from electric learning system, its system knowledge construction level is similar to problem-solving based online form (Hou et al., 2008), which will have higher knowledge construction level rather than other online mass media forum having teacher taking parts in. In interaction analysis, we found a possible motivation which makes people willing to answer- applaud. We also found the disadvantage of this analysis method and prove some suggestion to improve the model to have deeper research for others.
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