電腦化測驗在數位學習領域是重要的部分。用以幫助教學者通過測驗了解學生的學習狀況。傳統一般測驗只能提供考試的分數等不完整的資訊，若能透過萃取測驗中涵蓋的隱藏資訊如概念與課程之間的關係度，並加以應用於診斷分析以幫助學生達到更好的學習成就。本研究提出一個創新的方法去分析和截取測驗中隱藏資訊，再將之以知識結構的方式如概念構圖來呈現以幫助學生能夠了解其學習狀態。本研究發展了一個名為概念構圖-種子（CM-SEED）的學習系統以幫助學生診斷其學習障礙與提供對應的補救學建議與教材。為評估本研究系統是否有效,本研究將其應用於大學課程，實驗結果顯示使用本研究學習系統學生之學習成效顯著優於未使用系統的學生。另外，本研究亦對實驗組學生進一步探討不同學習風格是否影響學習成效，結果顯示反思的(reflective)和感覺的(sensing)的學生有更好的學習成效。

For e-learning environments, computerized test and diagnosis is an important issue from which instructors can know learners’ learning statues by assessments. In general, test result only provides a single score which is insufficient information for assessment. Since the test sheet consist implicit information of both concepts, concept r relationships of a subject may incredibly help learners for better learning achievement. This study proposed an approach to analyze test results by using implicit information extraction process and, visualizes knowledge structure with concept map for assisting learners understanding their learning status in detail. Furthermore, a Concept Map-Smart Extraction and Explicit Diagnosis (CM-SEED) learning system was developed to diagnose learning barriers and misconceptions whilst providing suggestions and guidance as remedial learning. An experimental study was conducted with 90 participants in a university, and they were randomly assigned into an experimental group and a control group. The result indicates students of the experimental group performed significantly achievement and obtained better learning perception than those students in control group. Additionally, the students in experiment group belong to the learning styles of reflective and sensing learners have better learning achievement.

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