隨著社群網站的興起，人際關係提升到另一種層面互動方式，也在人與人之間形成了一種新型態的虛擬社群。Facebook是當今社會中最受到歡迎的社群網站之一，提供了多類型的互動平台、動態即時訊息與個人檔案的分享，成功地打入全球網路市場中。近年來，社群網站也被廣泛地應用在多種數位學習與合作學習的情境中，透過電腦網路及多媒體技術的快速發展與普及間接提升了數位學習的便利性與可用性。在合作學習的環境中，必須考量到學習者的分組，而透過良好的分組，可以提升學習者的學習成效，但是要建構一個良好的分組機制，必須多面向來考量學習者學習特性與相關背景知識。本篇研究提出了在社群網站上發展一套具適應性的動態分組系統，在合作學習的環境下，以每位學習者的知識背景與專業領域為基礎自動完成分組，有別於傳統的隨機分組方法。此系統藉由自動記錄每位學習者在社群網站的使用行為，如訊息回覆內容，點選文章內容的偏好，或是對特定類型主題的瀏覽率等，再透過蜜蜂演算法的最佳化來形成分組條件，選出最適合完成任務的成員。經過實驗模擬結果發現，透過蜜蜂最佳化演算法的群聚智慧機制，提供了一個更高效率且更為準確的分組結果。

The building up of social networking site has promoted the relationships of interaction and organized virtual communities among people and its achievements have become a center of attraction. Recently Facebook has been applied widely in a variety of fields for education and the integration of social networking services (SNSs) and e-learning provide a novel and rich content platform for knowledge delivery and collaborative learning. But there are so many “friends” (learners) on the social network sites, and it will be difficult for teachers to divide the learners into small groups or select the appropriate people for specific tasks over social networking sites, it needs a better way to group the learners. The main purpose of the building a group composition system in a collaborative learning environment is to strengthen the effectiveness of learning. This study proposed an adaptive group composition scheme for collaborative learning on Social Network Sites using artificial bee colony (ABC) optimization and to provide the most adaptive collaborative learning group based on the individual knowledge background in college project designing course. The proposed system recollect the learners’ profile automatically through surfing behaviors, the preferred topic, and the posted messages; moreover, utilizing an Artificial Bee Colony algorithm to optimize the group composition. The experiment results indicated the proposed method promotes the accuracy of the group composition and achieve the better performance effectively in comparison with the other methods.

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