近年來MOOC(Massive Open Online Courses)在全世界快速發展，為大眾提供不同於以往的教學平台。然而，線上教學平台的教師常形成「大者恆大，小者恆小」的現象(意即馬太效應)發生，造成有些表現良好的教師雖然評價良好，但仍因個人資訊的不完整等因素，造成期處於成長的弱勢。

不同於以往使用單一理論配合問卷法分析，本研究採用資料科學法，配合資料爬取、群集分析(Cluster Anlaysis)、主成分分析(Principal Component Analysis)與區別分析(Discriminant Analysis)，針對線上教學平台馬太效應形成之因素進行分析，試圖找出不同類型的文化資本對於績效變化之影響，並描繪出不同型態的教師文化資本與成長型態間的關係。

透過區別分析，本研究發現影響馬太效應形成的變數重要性由大到小分別為內部訊號、評論、專業技術、語言與價格，自我介紹與外部訊號對於馬太效應形成不顯著。教師與平台可以針對以上變數進行後續改善方案。

Massive Open Online Courses (MOOCs) rapidly develop around the world, providing a distinct educational platform for people. However, the "Matthew Effect" often occurs among instructors on online teaching platforms, where successful instructors tend to grow further while struggling ones remain disadvantaged, despite positive evaluations.

Unlike other research mainly using single theories combined with a questionnaire method, this study integrates data scraping, cluster analysis, principal component analysis (PCA), and discriminant analysis to analyze the factors contributing to the Matthew Effect on online teaching platforms.

With discriminant analysis, we find out that the variables influencing the formation of the Matthew effect are internal signals, reviews, professional skills, language, and price. While introduction and external signals are insignificant. Both teachers and platforms can develop improvement strategies based on these results.

目錄
論文審定書 i
致謝 ii
中文摘要 iii
Abstract iv
圖目錄 vii
表目錄 x
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究問題與目的 7
第三節 研究流程 9
第二章 文獻探討 10
第一節 資訊不對稱與訊號理論 10
第二節 文化資本 11
第三節 馬太效應 16
第四節 資料科學 17
第三章 研究架構與方法 18
第一節 研究模型 18
第二節 研究假說 19
第三節 資料蒐集與分析 21
第四節 分析統計方法 28
第四章 研究分析與結果 29
第一節 資料清洗 29
第二節 資料預處理 30
第三節 代理變數 33
第四節 主成份分析 34
第五節 區別分析 38
第六節 其他群集比較 46
第七節 模型及假說驗證 77
第五章 結論與建議 79
第一節 研究結論 79
第二節 學術貢獻 80
第三節 實務貢獻 81
第四節 研究限制與未來建議 82
參考文獻 83

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