裁判式學習: 建構式電腦輔助學習之研究

Abstract

主動學習的重要性已是教育工作者的共識, 如何提升學習者的主動性是近 年來許多研究的主題. 在本論文中, 我們提出一種以建構主義為基礎的新 教學策略,稱作[裁判式學習](Learning by Judging), 並且發展了一個電 腦輔助學習的環境來實現建構論中的幾個關鍵概念, 例如引導知識重組強 化.意識後設認知.促進社群共識等等.此教學系統是一個合作式學習環 境, 學生可於此工作導向的使用者介面中 , 呈現他們的先備知識. 同時 我們採用遺傳演算法(Genetic Algorithms)以提供演化樣本空間, 讓學生 可以反覆評估富有變化的樣本. 最重要的是 , 整個學習過程中, 學生只 須做判斷即可, 這個極為單純的條件可有效的降低學習障礙, 促使學生主 動投入學習過程. 透過演化式的判斷過程, 系 供學生建構與再建構其知 識的方法.為了分析學生的判斷行為, 系統記錄了學生所有學習過程中所 做的判斷.而所收集到的量化資料將利用人工智慧的方法,如階層式的分群 法( Hierarchical Clustering ).模糊分群法(Fuzzy Clustering). 及模 糊專家系統(Fuzzy Expert System) 等加以分析. 本方法的整體目的是要 鼓勵學生將認知的過程, 及社群互動的影響具體化. 換句話說, 學習過程 的分析結果可以來解釋知識建構過程, 及了解在一個網路化合作學習環境 中的社會學習型態. In this thesis, we propose a new,constructivism-based pedagogical strategy, called learning by judging, and develop a computer assisted learning environment to realize this concept. This tutoring system is a cooperative social learning environment in which students present their prior knowledge on a task-oriented user interface. The system employs Genetic Algorithm to provide an evolutionary judgment process, the system provides the students a way to constructing and re- constructing their know- ledge. In order to analyze a student's behavior of adjustment ,the system records his entire learning process in the form of judgment actions. The collected quantitative data will then be analyzed by using computational, intelligence methods such as hierarchical clustering , fuzzy C- means clustering , fuzzy set and fuzzy expert system. The overall purpose of this approach is to encourage students to realize own cognitive process and the impact form social interaction. In other words, the analytical result of the learning process becomes the basis of explaining constructive epistemological processes and understanding types of social learning processes in a network-based cooperative environment .