近幾年來，有許多學者專家提出以模糊推論為基礎以作學生學習成效評估及建立概念圖的方法。在作學習成效評估時，我們必須考慮到評分者之間的差異性及評量題目本身的難度、複雜度與重要性。在找概念之間的關聯性時，我們必須參考評量結果中各概念間學習成效的相似性。在本論文中我們提出了三個新方法，以模糊歸屬函數作學生學習成效評估及建立概念圖，在第一個方法中，我們提出一個自動建立寬鬆的分數、嚴謹的分數以及一般的分數之間對應的模糊歸屬函數以用於學生學習成效評估的新方法。在第二個方法中，我們提出一個利用模糊歸屬函數與模糊規則以用於學生學習成效評估的新方法，它考慮題目的難度、複雜度與重要性，並能分辨同分學生之間的排名先後。在第三個方法中，我們提出一個利用評量結果配合模糊推論方法以自動建構出概念圖的新方法，我們使用模糊規則與模糊推論方法以自動建立出概念圖並求得概念之間的相關度。

In recent years, some methods have been presented for applying the fuzzy set theory in educational grading systems and dealing with the concept maps construction for providing the adaptive learning guidance to students. For dealing with the students' learning achievement evaluation, we must solve the subjective judging problem of teachers and consider the difficulty, importance and complexity of questions. For dealing with the concept maps construction for providing the adaptive learning guidance to students, we have to consider the similarity of the learning achievement between concepts. In this thesis, we propose three methods to evaluate students' learning achievement and to construct concept maps based on fuzzy membership functions. In the first method, we present a new method to automatically construct the grade membership functions of lenient-type grades, strict-type grades and normal-type grades, given by teachers, for students' evaluation. In the second method, we present a new method for dealing with students' learning achievement evaluation using fuzzy membership functions and fuzzy rules. It considers the difficulty, importance and complexity of questions for students' answerscripts evaluation. It provides a useful way to distinguish the ranking order of students with the same score. In the third method, we present a new method to automatically construct concept maps based on fuzzy rules and students' testing records. We apply fuzzy rules and fuzzy reasoning techniques to automatically construct concept maps and evaluate the relevance degrees between concepts.

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