概念圖建構方法之研究

Abstract

隨著網路學習(e-Learning)的蓬勃與發展，如何在「適性化」理念下發展出智慧型網路學習系統，並依照不同學習能力與背景的學習者來提供適性化的學習路徑已是當今網路學習不可忽視的重要課題。為了更容易達到適性化教學的目的，大部分的網路學習系統會將學生的學習成績加以分析，並依據事先建構好的「課程概念圖」來調整適合學生的學習路徑。 然而，雖然概念圖在適性化的網路學習相關策略設計上非常有用，可是，每當學習一新課程，教育設計者或是相關領域專家就必須進行一段冗長、費時且艱鉅的知識擷取過程才能將此概念圖建立起來。

為了解決這個問題，本文研究提出了「二階段概念圖建構法」，希望達到自動化建立概念圖的目的。在第一階段裡，結合了泛析理論、教育理論、資料探勘等相關技術，藉此找出學生成績間的相關法則 ;在第二階段裡，深入分析成績相關法則所代表的意義，並判斷試題所包含概念之間的先備關係及其可能的相對應的情境解釋。最後，根據之前所做的相關分析，量身訂作了一個「概念圖建構演算法」，藉由此演算法來自動建構出課程概念圖，以利教師或專家進一步分析及應用。

In recent years, e-learning system has become more popular and many adaptive learning environments have been proposed to offer learners customized courses in accordance with their aptitudes and learning results. For achieving the adaptive learning, a predefined concept map of a course is often used to provide adaptive learning guidance for learners. However, it is difficult and time consuming to create the concept map of a course. Thus, how to automatically create a concept map of a course becomes an interesting issue. In this thesis, we propose a Two-Phase Concept Map Construction (TP-CMC) approach to automatically construct the concept map by learners’ historical testing records. Phase 1 is used to preprocess the testing records; i.e., transform the numeric grade data, refine the testing records, and mine the association rules from input data. Phase 2 is used to transform the mined association rules into prerequisite relationships among learning concepts for creating the concept map. Therefore, in Phase 1, we apply Fuzzy Set Theory to transform the numeric testing records of learners into symbolic form, apply Education Theory to further refine it, and apply Data Mining approach to find its grade fuzzy association rules. Then, in Phase 2, based upon our observation in real learning situation, we use multiple rule types to further analyze the mined rules and then propose a heuristic algorithm to automatically construct the concept map. Finally, the Redundancy and Circularity of the concept map constructed are also discussed. Moreover, we also develop a prototype system of TP-CMC and then use the real testing records of students in junior high school to evaluate the results. The experimental results show that our proposed approach is feasible.