了解用戶在社交媒體上的電子學習經驗，對於幫助電子學習者，如學生、教育工作者、管理者、決策者和設計師等具有可操作性的知識作出更明智的教育促進決策至關重要。今天，數以百萬計的人，包括學生和教師，都使用社交網站因為各種各樣的理由，在本文中，我們使用FP-Growth進行關聯規則挖掘，以探索社會媒體數據中的關系，以了解用戶的電子學習體驗。本研究遵循的方法從文本內容是兼併詞語從一家專注於教育相關的subreddits，從Reddit評論中提取出數據集。分析了兼併詞語在subreddits以及生成內容作者。結果表明，不同主題的並發詞語的最高支持度為95.2%和100%。在諸如“you want to”，“you think a”，“you know i”，“i and studying”，“the exam my”，“so the test”，“calculus for”，“was difficult”，“are easy”，“the helpful“，”a i good“和”thank for“等詞之間存在著顯著的關係。得出的結論是，從社群媒體中的關聯規則得到使用者(可能是學生或者老師)他們之間在考試或者課程的互動和合作性，來表達他們消極或積極的學習經驗。

An understanding of users’ e-learning experiences in social media is crucial in helping e-learning stakeholders such as students, educators, administrators, policymakers and designers with actionable knowledge to make more informed decisions for educational enhancement. Today, millions of people including students and teachers use social networking sites for assorted reasons. In this paper, association rule mining using the FP-Growth algorithm has been applied to explore relationships within social media data to understand users’ e-learning experiences. The study follows a methodology to extract the associations from textual content which are concurrent words generated from comments of a Reddit dataset focusing on education related subreddits. Analysis was conducted on concurrent words within subreddits as well as the authors that generated the content. The results show strong relationships with the highest support of 95.2% and 100% confidence between concurrent words with various themes. Significant relationships were found between concurrent words such as “you want to”, “you think a”, “you know i”, “i and studying”, “the exam my”, “so the test”, “calculus for”, “was difficult”, “are easy”, “the helpful”, “a i good” and “thank for”. It was concluded that the association rules within social media data suggest that there is interactivity and collaboration primarily regarding assessments as well as courses where users, presumably students and teachers, express their negative and positive learning experiences.

Abstract ii
摘要 iii
Acknowledgements iv
List of Figures vii
List of Tables viii
Chapter 1 Introduction 1
1.1 Objective 2
1.2 Motivation 3
1.3 Contribution 3
Chapter 2 Related Works 5
2.1 Data Mining in Education 5
2.2 E-Learning and Social Networks 6
2.3 Social Networks and Data Mining 7
2.3.1 Reddit 8
Chapter 3 Methodology 10
3.1 Reddit Dataset 10
3.2 Extraction of Subreddit Comments 11
3.3 Preprocessing the Reddit Comments 12
3.4 Extracting Keywords 13
3.5 Feature Selection 15
3.6 Data Transformation 16
3.7 Association Rule Mining 18
3.6.1 FP-Growth Algorithm 19
Chapter 4 Results and Discussion 20
4.1 Interactivity and Collaboration Relationships 21
4.2 Assessment Relationships 26
4.3 Difficult and Easy Relationships 32
4.4 Negative Relationships 33
4.5 Positive Relationships 35
4.6 Evaluation 37
Chapter 5 Conclusion 40
References 41

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