現今社會由於資訊發達，網際網路的普及化，3C時代已經是完全的來臨了。
人們的生活已經跟網路脫離不了關連，就連教育模式也漸漸的跟網路結合，
許多網路學習平台因此竄出，本篇論文探討的是，如何讓選取網路課程的使用者，
可由網路學習平台過去的選課資料記錄，以及使用者同儕之間的關係影響力，提供未來的選課建議。此篇論文會從二個方向來探討，第一種，課程探勘，可探勘出每種課程之間的關聯性，且會加入權重的探討的，讓每筆資料參考的大小有所差異。第二種，搜尋相似好友，可以在網路上找出跟使用者本身有關連性的朋友前者探勘出來的結果，可以代表使用者未來可能有興趣的線上課程，且會有學習該門課程可能的學習成績作為參考用。後者則是可以搜尋哪些人的資料是跟使用者本身有關連的資料，可以減少探勘資料數量，且增加可信度。實驗顯示此論文提出的方法有實用性，彈性跟效率，利用本論文方法可讓使用線上學習課程的使用者，得到有用處的未來課程選取建議。

E-learning technology is being applied to organizing business process in many large –scale enterprises. Lots of studying system , therefore, has become an active research area. In this paper , we propose two methodologies for mining users’ course from learning system’s database . First , course mining , it’s used to find the frequently course logs from E-learning system’s database. Additionally, it can extract weighted among each E-learning system’s data. Second , finding similar friends. The method might get user’s friends , and then , we can use these related friend’s data for course mining .By using this method, we can avoid wasting time on mining unnecessary course data, and get more useful mining result for users. The empirical result shows the proposed methodologies are fast, flexible, and efficient. It’s a good solution for users who want to get course mining suggestion .

[1] Bekim Fetaji1, Majlinda Fetaji2,”E-learning Indicators Approach to Developing E-learning Software Solutions”, 1,2 South East European University/Computer Sciences, Tetovo, Macedonia, e-mail:
[2] Qianyi Gu,” Support Personalization in Distributed E-Learning Systems through Learner Modeling”, amara Sumner Department ofComputer Science, University ofColorado at Boulder Campus Box 430, 80309-0430, Boulder, Colorado, USA
[3] “The Using of E-Learning Techniques to Improve the Medical Education” , Haider Kadhem Muhsin Faculty of Computer Science and Information Technology
Delmon University for Science and Technology Manama- Bahrain
[4] E-Learning Indicators Methodology Approach in Designing Successful
e-Learning Bekim Fetaji 1, Majlinda Fetaji 2 1South East European University, CST Faculty, 1200 Ilindenska bb, Tetovo, Macedonia 2South East European University, CST Faculty, 1200 Ilindenska bb, Tetovo, Macedonia
[5] http://www.scorm.com/
[6] Jiawei Han and Micheline Kamber,” Data Mining: Concepts and Techniques”, Simon Fraser University
[7] http://warm.stu.edu.tw
[8] Meng-Feng Tsai and Yi-Ming Lee, “Mining Self-derivable Multilevel FP-tree From a Transactional Database”, National Central University, Taiwan, Master Thesis, 2006
[9] Meng-Feng Tsai and Gia-Ying Hu, “Service Mining for Composite Service Discovery”, National Central University, Taiwan, Master Thesis, 2007
[10] Sven Graupner, Vadim Kotov , Holger Tricks Hewlett-Packard Laboratories ,”Resource-Sharing and Service Deployment in Virtual Data Centers” 1501 Page Mill Road , Palo Alto, CA 94304, USA
[11] Sven Graupner, Vadim Kotov, Holger Trinks Hewlett-Packard Laboratories” Resource-Sharing and Service Deployment in Virtual Data Centers”, 1501 Page Mill Road, Palo Alto, CA 94304, USA
[12] George Zheng＋ and Athman Bouguettaya＋ “A Web Service Mining Framework”, Virginia Tech, Blacksburg, Virginia USA ‡CSIRO ICT Centre, Canberra, ACT, Australia
[13] Jiawei H., Yongjian F.,”Discovery of multiple-Level Association Rules from Large Database,” “by Proc. Of 1995 Int’l Conf. on Very Large Data Bases (VLDB’95)”.