在現代科技的浪潮下，人類的生活有了許多重大的革新，網際網路的發展更帶動了更迅速的資訊傳遞，以學習方面來說，新的學習方式正在逐步的改變傳統學習的習慣，並提供學習者更多元的學習媒介。在中小學的教育體制，問題式學習是一種有效的學習方式，學生可以透過問題確認自我的學習狀態並理解問題所表達的知識與概念。而為了確切提供學習者所需的資料，好的學習媒介管理與分類就成為一個重要的工作，將問題按照其所涵蓋的知識點進行分類，使得使用者取得適合的題目可以更佳的便利，進而達成更好的學習效率。 本篇論文延續中小學學習教材的分類系統，除了針對學習教材規劃資料庫，並提出跨章節的分類系統。傳統的學習方式經常是針對每個不同的單一知識點進行學習，在原本的系統針對此類的問題已有不錯的分類效果，但在大型入學考試與進階的問題中，有些問題同時具備不同的章節的概念，因此我們延續原先的卷積神經網路(Convolutional Neural Network)與支持向量機器(Support Vector Machine)的分類器，設計了針對跨章節問題的分類架構，並與原本分類系統提出的跨章節分類策略進行比較，證明新提出的架構能達到更佳的分類效能。 ;In the tide of modern technology, there are many significant innovations in human life. The development of the Internet has led to the more rapid delivery of information. From the learning side, the new learning style is gradually changing the habit of traditional learning. In the K-12 system, the question-driven learning is an effective way of learning. The students can confirm their learning status through question exercises and understand the knowledge and concepts expressed by the problem. In order to provide the learning information for the learners, a good learning material management and classification has become an important task. To classify the question according to the knowledge points covered by them so that the user can get the appropriate questions convenient. And then achieve a better learning efficiency. In this thesis, we continue studies which the classification system of K-12 learning materials. In addition to planning database for learning materials, and proposed cross-topic classification system. The traditional way of learning is often for each different single point of knowledge to learn. In the original system for such problems have a good classification performance. Some question of the large entrance exam and advanced question have the different concept of cross-topic. Therefore, we extend the original Convolutional Neural Network (CNN) and support vector machine (SVM) hybrid classifier and proposed multi-label classification model for cross-topic questions. Finally, we compare the strategies proposed by classification system studies of K-12 learning materials with our multi-label classification model. The experiment shows that the multi-label classification model can outperform original strategies of classification.
