隨著網路科技的快速發展，互動式的遠距教學方式漸漸成為數位學習的一個新趨勢。相信在不久的將來，網路上將會有許多相容於SCORM標準的數位課程存放於課程儲存庫中供學習者使用。因此，當學習者在面對如此大量的課程資料時，將產生不易去選擇適當地且符合其個人喜好的課程來學習的問題。 本論文提出一個基於SCORM標準的混合式課程推薦系統。利用本研究所提出的三個課程推薦模組，來提高學習者查詢課程的準確率和加強學習者的學習效果。藉由此系統，學習者可以獲得最適合他的推薦課程，不僅能夠節省學習者找尋合適課程的時間，並且可以針對不同的學習者給予適性且個人化的推薦課程。 With the development of internet technology, interactive distance learning is becoming a new trend of E-learning. In the future, we believe that there will be a large number of digital SCORM-compliant courses in repository for the user. Consequently, when a learner confronts a large amount of learning materials, it is hard to select appropriate and preferred courses to learn. This thesis proposed a hybrid recommendation system for SCORM-compliant courses. By using three proposed recommendation modules, it can raise the accuracy of searching course and increase the performance of learning. With the proposed system, learner can get the most appropriate courses. It not only can save the time for searching courses, but also recommends the adaptive and personal courses for different learners.
