網狀結構的內容編排是網路式教學的重要特性，學員未必一定得依照章節循序學習，使用網路式教學系統，學員可以依照自己的興趣與需求，任意地穿梭在各個主題之間的關聯與參考，大幅增加了學習過程的彈性。然而，這樣的學習方式在增加了靈活度的同時，也增加了複雜度、降低了結構性。依照學習者的認知型態，場所獨立類型的學生，分析能力較強，喜歡獨立思考與目標導向的學習方式；而場所依賴類型的學生，則偏好格式工整的教材內容，同時也較依賴來自同儕之間的互動與訊息。由此可提出以下合理假設：同樣的教學模式未必能在不同認知型態的學員之間收到相同的效果，讓學習過程自動順應不同的認知型態，才能提升整體的教學成效。

本研究試圖將認知型態概念納入網路式教學平台建構的考量，在低結構化的課程內容中，達到網路學習個人化的目的。以主修科學類課程的大學生作為實驗樣本，使用認知型態分析工具對其進行測驗後實際參與網路式教學。伺服器的存取記錄檔，儲存了這些受測者在學習過程的行為，從記錄檔內得以萃取出不同認知型態的行為模式與關聯式法則，使用這些法則能夠回饋日後的網路學習者，為個別學員提供推薦與引導連結，以及切合需求的學習工具，形成一套具備認知型態個人化的網路式教學系統。

One of the main features of hypermedia learning is that learners can explore through multiple topics at their own paces and preferences. However, not all types of learners feel comfortable with such learning approach. This is the result of learners having different cognitive styles which reflects learners' information processing habits. Therefore, students' cognitive styles should be a key factor in the development of a hypermedia learning system. In this research, we use Web Usage Mining techniques to analyze two different styles of user, behaviors, field dependent and field independent, from a hypermedia learning system. We present and experimentally examine two techniques, based on association rules and cluster centroids, to discover historical navigation patterns. These patterns enable the learning system to provide real-time recommendations for personalized hypermedia learning.

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