博碩士論文 972201031 完整後設資料紀錄

DC 欄位 語言
DC.contributor數學系zh_TW
DC.creator許弼凱zh_TW
DC.creatorBi-Kai Hsuen_US
dc.date.accessioned2012-7-16T07:39:07Z
dc.date.available2012-7-16T07:39:07Z
dc.date.issued2012
dc.identifier.urihttp://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=972201031
dc.contributor.department數學系zh_TW
DC.description國立中央大學zh_TW
DC.descriptionNational Central Universityen_US
dc.description.abstract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。 zh_TW
dc.description.abstractThe 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. en_US
DC.subject序列分析zh_TW
DC.subject知識建構模型zh_TW
DC.subject非同步線上討論zh_TW
DC.subjectIAMen_US
DC.subjectlag-sequential analysisen_US
DC.subjectCMCen_US
DC.subjectnonsynchronous discussen_US
DC.title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zh_TW
dc.language.isozh-TWzh-TW
DC.titleThe patterns feature of knowledge construction in online discussion: A case studyen_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

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