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研究生:郭金鈤
研究生(外文):Chin-Jin Guo
論文名稱:大規模開放式線上課程學習行為研究-以社交媒體資料探勘為例
論文名稱(外文):Learning in Massive Open Online Courses:Evidence from Social Media Mining
指導教授:沈建文沈建文引用關係
指導教授(外文):Chien-wen Shen
學位類別:博士
校院名稱:國立中央大學
系所名稱:企業管理學系
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:78
中文關鍵詞:MOOC學習資料探勘社交媒體情緒分析社交網絡
外文關鍵詞:MOOClearningsocial mediadata miningsentiment analysissocial networks
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由於許多大規模開放式線上課程(Massive Open Online Courses, MOOCs)已經運用社交媒體工具來支援大批學生,針對Twitter社交媒體進行共同知識創造及集體學習,本研究採用各種社交媒體探勘方式,分析MOOCs的推文訊息,並進一步調查相關「MOOCs學習」的各種訊息。在Twitter社交媒體上,MOOCs推文的描述性統計和趨勢分析表明,MOOCs推文中平日發佈的訊息較為活躍,是週末的5倍。在Twitter社交媒體上每月分析表明,10月份是一年中最活躍的月份,而8月是最不活躍的月份。因此,MOOCs從業人員應著重於MOOCs高峰時段訊息發佈中討論的重點,並立即對學生的回饋作出反應。此外,Twitter每月訊息活動涉及的情緒分析結果表明,輿論對整體MOOCs推文的情緒是略偏向負面,然而「MOOCs學習」推文普遍呈現較正面的信息。因此,MOOCs從業人員應調查對於MOOCs推文的負面訊息,並瞭解其背後的原因。如果當MOOCs社群中,用戶產生具體的新聞討論話題時,MOOCs從業者可以針對當日發佈的訊息進行調查,計算正面和負面推文數之間的差異,以了解公眾對新聞的看法。此外,我們針對MOOCs轉推的影響力研究表明,具影響力的用戶中前5%-10%,通常約占MOOCs正面及負面轉推訊息的50%。隨後繪製的社交網路圖可顯示出,在Twitter社交媒體訊息中MOOCs推文中,擁有最多MOOCs正面及負面推文的關鍵影響用戶是如何傳播訊息的。此外,Twitter社交網絡中MOOCs訊息通常最多可傳播到第二層的用戶。我們針對社交媒體進行訊息探勘的研究結果,認為Twitter社交媒體能夠幫助MOOCs從業者提高對MOOCs的見解,且有效地提高學生的學習。
Many massive open online courses(MOOCs)have adopted social media tools for large student audiences to co-create knowledge and engage in collective learning processes. To further understand the public opinion toward MOOCs learning, this study adopted various social media mining approaches to investigate Twitter messages. An analysis of the descriptive statistics and trends of MOOC-related Twitter messages revealed that MOOC-related discussions on Twitter were 5-fold more active on weekdays than at the weekend. A monthly analysis on Twitter showed that October was the most active month of the year, whereas August was the least active month. Therefore, MOOC practitioners should focus on MOOC discussions during peak periods to respond immediately to student feedback. In addition, the results of a sentiment analysis involving observations of monthly activities on Twitter indicated that public opinion toward MOOCs was slightly negative, although there were generally more positive messages about learning through MOOCs than there were negative ones. Therefore, MOOC practitioners should investigate negative Twitter messages related to MOOCs to understand the underlying reasons for them. When MOOCs communities discuss specific news topics, MOOC practitioners can investigate the difference between the number of positive and negative tweets on a given day to understand public opinion toward the news. Furthermore, our findings regarding the influencers of MOOCs retweets indicate that the top 5%–10% of influencers typically account for 50% of sentimental retweets about MOOCs. Social network diagrams were also developed to reveal how sentimental messages about MOOCs on Twitter were disseminated from the top influencers with the highest number of positive/negative retweets about MOOCs. The MOOCs were generally disseminated to a maximum of 2 layers of users in Twitter social networks. Our findings of social media mining show that Twitter can assist MOOC practitioners in improving their understanding of the insights of MOOCs to effectively improve student learning.
第一章 緒論 ……………………………….……………………………………… 01
1-1 研究動機 ………………………………………………………………… 01
1-2 研究目的 ………………………………………………………………… 04
第二章 文獻探討 ………………………………………………………………… 07
2-1 MOOCs發展 …………………...………..……………………………… 07
2-2 社交媒體在 MOOCs的研究 ………………….………..……………… 09
2-3 社交媒體中MOOCs的學習行為研究 …………………….….… 11
第三章 研究方法 …………………….…….……………………………………… 17
3.1資料收集 ………………….....…………………………………………… 17
3.2數據分析 ………………….....…………………………………………… 17
3.3關鍵影響用戶分析 ……………………………………………………… 18
3.4情緒分析 ……………………………………………………………… 20
3.5正負面推文轉推用戶分析 …………….………..……………………….. 25
第四章 研究結果與討論 ……………………..……………………………………27
4-1 MOOCs推文統計 ………………….…………...…………………..…… 27
4.1.1 MOOCs推文「數量」統計 ………………......…………27
4.1.2 MOOCs推文「Hashtag」統計 …………………………...…….32
4.1.3 MOOCs推文「用戶」統計 ………..………..…………..36
4-2 MOOCs轉推用戶分析 …………………...………..…..………..…43
4-3 MOOCs推文情緒分析 …………………...………..…..………..…46
4-4 MOOCs正負面推文轉推用戶分析 …………………...…….…….58
第五章 結論與建議 ……………………..………………………………………… 70

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