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題名 基於學習行為特徵之學習者電腦中介溝通能力預測模型設計-以網路合作問題導向學習為例
Developing a Computer-Mediated Communication Competence Forecasting Model Based on Learning Behavior Features: A Case Study of Online Collaborative Problem-Based Learning作者 連英佑
Lian, Ying-You貢獻者 陳志銘
Chen, Chih-Ming
連英佑
Lian, Ying-You關鍵詞 電腦中介溝通
行為特徵
微歷程
機器學習
預測模型
Computer-Mediated Communication (CMC)
Learning behavior feature
Micro-behavior
Machine learning
Forecasting model日期 2018 上傳時間 27-八月-2018 14:41:15 (UTC+8) 摘要 本研究旨在透過發展之學習行為微歷程記錄器蒐集網路合作式問題導向學習課程活動中學習者的學習行為微歷程，根據所蒐集資料歸納學習行為特徵分類架構，發展基於學習行為微歷程特徵建立電腦中介溝通能力預測模型的有效方法，以建立即時預測模型並檢驗預測模型正確率與穩定性。此外，也探討學習情境與學習者特質對於電腦中介溝通能力預測準確度之影響。研究結果顯示，運用線性迴歸、樹狀迴歸演算法、序列最小優化迴歸演算法、卷積類神經網路演算法以及多層感知機演算法建立之預測模型都具有良好的預測能力，其中序列最小優化迴歸演算法建模具有最高的預測準確率與穩定性，其預測平均絕對誤差低至0.2522分。而「溝通行為」與「溝通目的」為影響電腦中介溝通能力預測模型之關鍵特徵子群，並且發展電腦中介溝通能力預測模型必須考量學習情境及活動與學習者學習行為微歷程的關係。若學習者有更多的互動討論行為、較熟悉教學活動主題、具有較多的學習者案例，都有助於建立預測成效更準確的電腦中介溝通能力預測模型。
This study aims to develop a Computer-Mediated Communication (CMC) competence forecasting model based on several considered well-known machine learning schemes and learning behavior features collected by a micro-behavior recorder from the learners while using an online collaborative problem-based learning system to perform a problem-solving learning activity. To summarize the big data generated from a huge amount of micro behaviors into the useful behavior features for constructing a good CMC competence forecasting model, this study developed the learning micro-behavior classification structure according to the collected data features and the concept of CMC. An effective method for constructing a high correctness and stableness CMC competence forecasting model was proposed and examined. In addition, the effects of learning situations and learners’ personal trait on the accuracy of CMC competence forecasting model were also discussed.The results show that the CMC competence forecasting models developed by the linear regression algorithm, M5P algorithm, sequence minimum optimization regression algorithm, convolutional neural network algorithm and multi-layer perceptron algorithm all have good prediction performance. Among the five machine learning schemes, this study found that the sequence minimum optimization regression algorithm has the highest prediction accuracy and stability of prediction accuracy. The key features that influence the forecasting accuracy most are “communication behavior” and “communication purpose.” Moreover, the development of the forecasting model must consider the relationship between learning situations and learners’ personal trait such as discussion trait and familiar degree with the problem-solving subject because those would affect the accuracy of the developed forecasting model.參考文獻 王文芳（2016）。基於序列探勘之網路科學探究學習歷程分析（碩士論文）。國立政治大學，臺北市。王逸翔（2015）。基於腦波注意力之影音教材學習診斷系統（碩士論文）。國立臺灣師範大學，臺北市。吳清山、林天祐（2014）。教育U辭書 U dictionary of education。臺北市：智勝文化。宏侯政、萍崔夢（2013）。問題導向網路學習系統應用於國小五年級資訊素養與倫理之研究－著作權單元為例。教育傳播與科技研究，104，17–36。doi：10.6137/RECT.2013.104.02林上資（2011）。數位圖書館資訊架構對數位學習之學習成效影響研究－以自然與人文數位博物館為例（碩士論文）。國立政治大學，台北市。林佳慧（2011）。電腦化測驗系統使用效能評估之研究-以 [桃園縣國中生網路檢測系統] 為例（碩士論文）。淡江大學，新北市。林宓（2013）。基於腦波注意力發展數位筆結合紙本學習情境之英語診斷複習系統（碩士論文）。國立臺灣師範大學，台北市。唐士哲（2014）。重構媒介？「中介」與「媒介化」概念爬梳。新聞學研究，121，1–39。袁梅宇（2017）。王者歸來: WEKA機器學習與大數據聖經。臺北市：佳魁資訊。張家成（2007）。探勘合作式學習社會網路支援問題導向學習知學習伙伴推薦（碩士論文）。國立臺灣師範大學，台北市。陳志銘（2009）。創新數位學習模式與教學應用（初版.）。臺北市：文華圖書。陳勇汀（2017）。M5P：預測非線性連續資料的樹狀迴歸演算法。布丁布丁吃什麼？。取自：http://blog.pulipuli.info/2017/11/m5p-m5p-trees-with-linear-models-in-weka.html曾憲雄、蔡秀滿、蘇東興、曾秋蓉、王慶堯（2005）。資料探勘。臺北市：旗標出版社。鄭江宇、曾瀚平（2015）。指尖下的大數據：運用Google Analytics發掘行動裝置裡的無限商機。臺北市：遠見天下文化。Abdulkader, S. 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Birmingham, England: Packt Publishing Ltd.Zhang, X. (2017). Support Vector Machines. In Encyclopedia of Machine Learning and Data Mining (pp. 1214–1220). Springer, Boston, MA. DOI: 10.1007/978-1-4899-7687-1_810Zweig, M. H., & Campbell, G. (1993). Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry, 39(4), 561–577. 描述 碩士
國立政治大學
圖書資訊與檔案學研究所
105155002資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105155002 資料類型 thesis dc.contributor.advisor 陳志銘 zh_TW dc.contributor.advisor Chen, Chih-Ming en_US dc.contributor.author (作者) 連英佑 zh_TW dc.contributor.author (作者) Lian, Ying-You en_US dc.creator (作者) 連英佑 zh_TW dc.creator (作者) Lian, Ying-You en_US dc.date (日期) 2018 en_US dc.date.accessioned 27-八月-2018 14:41:15 (UTC+8) - dc.date.available 27-八月-2018 14:41:15 (UTC+8) - dc.date.issued (上傳時間) 27-八月-2018 14:41:15 (UTC+8) - dc.identifier (其他 識別碼) G0105155002 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/119563 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 圖書資訊與檔案學研究所 zh_TW dc.description (描述) 105155002 zh_TW dc.description.abstract (摘要) 本研究旨在透過發展之學習行為微歷程記錄器蒐集網路合作式問題導向學習課程活動中學習者的學習行為微歷程，根據所蒐集資料歸納學習行為特徵分類架構，發展基於學習行為微歷程特徵建立電腦中介溝通能力預測模型的有效方法，以建立即時預測模型並檢驗預測模型正確率與穩定性。此外，也探討學習情境與學習者特質對於電腦中介溝通能力預測準確度之影響。研究結果顯示，運用線性迴歸、樹狀迴歸演算法、序列最小優化迴歸演算法、卷積類神經網路演算法以及多層感知機演算法建立之預測模型都具有良好的預測能力，其中序列最小優化迴歸演算法建模具有最高的預測準確率與穩定性，其預測平均絕對誤差低至0.2522分。而「溝通行為」與「溝通目的」為影響電腦中介溝通能力預測模型之關鍵特徵子群，並且發展電腦中介溝通能力預測模型必須考量學習情境及活動與學習者學習行為微歷程的關係。若學習者有更多的互動討論行為、較熟悉教學活動主題、具有較多的學習者案例，都有助於建立預測成效更準確的電腦中介溝通能力預測模型。 zh_TW dc.description.abstract (摘要) This study aims to develop a Computer-Mediated Communication (CMC) competence forecasting model based on several considered well-known machine learning schemes and learning behavior features collected by a micro-behavior recorder from the learners while using an online collaborative problem-based learning system to perform a problem-solving learning activity. To summarize the big data generated from a huge amount of micro behaviors into the useful behavior features for constructing a good CMC competence forecasting model, this study developed the learning micro-behavior classification structure according to the collected data features and the concept of CMC. An effective method for constructing a high correctness and stableness CMC competence forecasting model was proposed and examined. In addition, the effects of learning situations and learners’ personal trait on the accuracy of CMC competence forecasting model were also discussed.The results show that the CMC competence forecasting models developed by the linear regression algorithm, M5P algorithm, sequence minimum optimization regression algorithm, convolutional neural network algorithm and multi-layer perceptron algorithm all have good prediction performance. Among the five machine learning schemes, this study found that the sequence minimum optimization regression algorithm has the highest prediction accuracy and stability of prediction accuracy. The key features that influence the forecasting accuracy most are “communication behavior” and “communication purpose.” Moreover, the development of the forecasting model must consider the relationship between learning situations and learners’ personal trait such as discussion trait and familiar degree with the problem-solving subject because those would affect the accuracy of the developed forecasting model. en_US dc.description.tableofcontents 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 4 第三節 研究問題 4 第四節 研究範圍與限制 5 第五節 名詞解釋 6第二章 文獻探討 8 第一節 電腦中介溝通 8 第二節 學習行為評量技術與應用 13第三章 研究方法 19 第一節 電腦中介溝通能力預測模型開發流程 19 第二節 資料蒐集階段 20 第三節 模型建立階段 34 第四節 實驗設計 53第四章 實驗結果與分析 59 第一節 實驗對象敘述統計說明 59 第二節 電腦中介溝通能力量表結果 61 第三節 學習行為微歷程特徵資料分析 62 第四節 特徵選取與評估 66 第五節 電腦中介溝通能力預測模型評估與分析 69 第六節 學習情境與學習者背景對於電腦中介溝通能力預測準確度之影響 89第五章 結論與建議 99 第一節 結論 99 第二節 研究實施建議 104 第三節 未來研究方向 107參考文獻 109【附錄1】參與研究同意書 119【附錄2】學習者背景問卷 120【附錄3】電腦中介溝通能力問卷 121 zh_TW dc.format.extent 3201162 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105155002 en_US dc.subject (關鍵詞) 電腦中介溝通 zh_TW dc.subject (關鍵詞) 行為特徵 zh_TW dc.subject (關鍵詞) 微歷程 zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) 預測模型 zh_TW dc.subject (關鍵詞) Computer-Mediated Communication (CMC) en_US dc.subject (關鍵詞) Learning behavior feature en_US dc.subject (關鍵詞) Micro-behavior en_US dc.subject (關鍵詞) Machine learning en_US dc.subject (關鍵詞) Forecasting model en_US dc.title (題名) 基於學習行為特徵之學習者電腦中介溝通能力預測模型設計-以網路合作問題導向學習為例 zh_TW dc.title (題名) Developing a Computer-Mediated Communication Competence Forecasting Model Based on Learning Behavior Features: A Case Study of Online Collaborative Problem-Based Learning en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 王文芳（2016）。基於序列探勘之網路科學探究學習歷程分析（碩士論文）。國立政治大學，臺北市。王逸翔（2015）。基於腦波注意力之影音教材學習診斷系統（碩士論文）。國立臺灣師範大學，臺北市。吳清山、林天祐（2014）。教育U辭書 U dictionary of education。臺北市：智勝文化。宏侯政、萍崔夢（2013）。問題導向網路學習系統應用於國小五年級資訊素養與倫理之研究－著作權單元為例。教育傳播與科技研究，104，17–36。doi：10.6137/RECT.2013.104.02林上資（2011）。數位圖書館資訊架構對數位學習之學習成效影響研究－以自然與人文數位博物館為例（碩士論文）。國立政治大學，台北市。林佳慧（2011）。電腦化測驗系統使用效能評估之研究-以 [桃園縣國中生網路檢測系統] 為例（碩士論文）。淡江大學，新北市。林宓（2013）。基於腦波注意力發展數位筆結合紙本學習情境之英語診斷複習系統（碩士論文）。國立臺灣師範大學，台北市。唐士哲（2014）。重構媒介？「中介」與「媒介化」概念爬梳。新聞學研究，121，1–39。袁梅宇（2017）。王者歸來: WEKA機器學習與大數據聖經。臺北市：佳魁資訊。張家成（2007）。探勘合作式學習社會網路支援問題導向學習知學習伙伴推薦（碩士論文）。國立臺灣師範大學，台北市。陳志銘（2009）。創新數位學習模式與教學應用（初版.）。臺北市：文華圖書。陳勇汀（2017）。M5P：預測非線性連續資料的樹狀迴歸演算法。布丁布丁吃什麼？。取自：http://blog.pulipuli.info/2017/11/m5p-m5p-trees-with-linear-models-in-weka.html曾憲雄、蔡秀滿、蘇東興、曾秋蓉、王慶堯（2005）。資料探勘。臺北市：旗標出版社。鄭江宇、曾瀚平（2015）。指尖下的大數據：運用Google Analytics發掘行動裝置裡的無限商機。臺北市：遠見天下文化。Abdulkader, S. 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