近年來，開放教育資源(Open Educational Resources)逐漸的普及，使用者透過網路可以針對自身得需要輕易的取得和分享教學資源，開放教育資源也開始對教師、學習者選取教學、學習資源產生影響。 然而龐大的資源數量也導致使用者需花費大量時間找尋所需資源，僅依賴搜尋引擎仍不足以滿足使用者的需求，多數的研究也顯示如何幫助使用者找到適合的資源是一個具挑戰的問題。因此本研究提出了一個使用關聯規則建構的推薦系統，透過使用教育專家建立的教育詞彙庫分析教育資源所包含的教育詞彙，利用巨量資料分析框架針對教育資源隱含之教育辭彙建立關聯規則，利用關聯規則建立每一筆教學資源的推薦，當使用者瀏覽教育資源時可以一併得知有關聯性的開放教學資源和與教材相關的時事新聞，利用推薦系統幫助使用者找到適合的開放教育資源以提升開放資源的使用率。 ;In recent years, Open Educational Resources (OER) has gradually become widespread. That OER users can easily access, make, and share educational resources according to their own needs through Internet influences on the selection of educational resources. However, a huge amount of resources has led to OER users spend a lot of time sourcing the necessary resources only with a simple search engine and lots of research point out that help users to find “right” OER is a challenge. This paper develops an OER and OER-related news recommendation system through educational corpus, association rules and Big Data analytics technique. When educators or learners browse the OER web pages, they can be aware of the relevance of OER and OER-related news easily. This study compares the recommendation system with two platforms that OER users often choose to look for OER and results show that using recommendation system can help users find resources.
