臺灣博碩士論文加值系統

在網路學習中，網路學習評量為目前廣泛使用來了解每一個學習過程的方法。由於每一個人適合的學習方法不盡相同，現有單一回饋機制的教學評量方式，較難讓學習者達成適性化學習的目標。本論文提出以專家決策樹演算法來建立學習型態與教學策略間的規則性，並將此規則性融入電腦教學系統中，建構出新的多元教學評量系統。本系統先依據KLSI量表知識庫測量學習者的學習習慣與學習週期中各階段的強度，判斷出學生的學習型態；同時由多位資深教師（專家）以他們在教學上的經驗制定出學習策略的規則；之後使用專家決策樹演算法，依據學習者對教材的反應及評量作答結果，分析每種學習型態適用的學習策略模式。本研究結果不但可提供教師針對不同學習型態的學生給予適合的學習策略；也可以讓學習者藉由適性化的學習策略，提升學習效能。

Since different person has different learning method that suits him/her, the adaptive learning is hard to be reached through the existed single feedback assessment mechanism. This thesis integrated a set of expert decision tree algorithm to build up the regularity between learning type and teaching strategy. The regularity is embedded into the computer assisted system to set up new diversified teaching assessment system. Experts’ teaching experiments and KLSI Inventory are used to set up the rules of learning type knowledge database in the system. Based on the learning type knowledge database, the system will judge learners’ strength in each stage of the learning period, and divided the learners into four learning types. Then based on the response from teaching material and assessment result, the learning strategy suitable for different types of learners is analyzed. After revise the knowledge regularity correlation, the system will propose a suggestion of learning strategy for the learners to enhance learning effectiveness.

中文摘要..................................................ii
Abstract.................................................iii
致謝......................................................iv
論文目錄...................................................v
圖目錄...................................................vii
表目錄..................................................viii
第1章 緒論.................................................1
1.1 研究背景...............................................1
1.2 研究動機與目的.........................................2
1.3 論文架構...............................................3
第2章 文獻探討.............................................4
2.1 專家系統...............................................4
2.2 KLSI量表...............................................5
2.3 學習策略...............................................7
2.4 網路評量系統...........................................8
2.5 決策樹演算法介紹.......................................9
第3章 系統流程............................................12
3.1 系統原理..............................................12
3.2 系統架構..............................................13
3.2.1 知識庫的建立........................................14
3.2.2 線上學習系統架構....................................17
3.3 系統實作..............................................18
3.3.1 實驗對象和實施......................................18
3.3.2 平台製做和功能簡介..................................20
第4章 資料分析............................................23
4.1 知識庫分析............................................23
4.2 線上學習系統成效比較..................................25
第五章 研究結論和未來研究方向.............................29
5.1 研究結論..............................................29
5.2 未來研究方向..........................................29
參考文獻..................................................31
附錄一....................................................35

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