數位學習環境持續改善，磨課師平台也蓬勃發展，平台上產生大量的學習紀錄。為了藉由這些紀錄改善老師的教學品質，讓學生了解自我的學習情況，學習分析這個研究領域因此而生。
學習分析的趨勢包含辨識、診斷、預測、評估、決策等面向，可以使用的方法包含社群網絡分析、機率模型、深度學習等等方式。學習分析的其中一項挑戰就是資料格式不一致，在各個學習平台上，學習歷程的格式不盡相同，因此資料不方便互相轉移，分析的結果也無法適用於其他平台。ADL (Advanced Distributed Learning )因此發展了xAPI(Experience API)，這是一種新的學習歷程規範，不僅可以用來記錄學習平台上的學習行為，甚至在行動載具上的學習行為，也可依照這個規範，紀錄於LRS中，學習歷程因此可以跨平台整合。
本研究實作了一個學習分析系統，系統中包含一個長短期記憶類神經網路，簡稱LSTM(Long Short Term Memory Neural Network)，用來分析高雄市教育局Dr.Go這個中小學磨課師平台上的學習歷程，預測學生答題狀況與學習成效表現，再藉由預測模型去了解學習資源彼此之間的關係。將預測結果用圖表呈現，顯示各個學習資源節點的關聯，幫助老師做出教學上的決策。

With the development of technology, the learning methods are different from the past. MOOCs(Massive Open Online Courses) platform like Khan Academy, Cousera and edX are more and more popular from 2012. These platforms contain amount of user’s data and researchers want to know what kind of knowledge we could know from these data. However, it is difficult to analyze data from different learning system together. Moreover, current learning analytic service on platform is not smart enough.
Therefore, we develop a system architecture to collect learning records from different systems and implement a LSTM neural network to predict students’ performance. And the accuracy of prediction is higher than 75% normally and the error rate is lower than 10%. Also, the system could transform the relation between learning material to relation graph. It will be helpful for teacher to adjust their teaching strategy.

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