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研究生:林侑勢
研究生(外文):Lin, Yu-Shih
論文名稱:強化結合理想解類似度偏好順序評估法之基因演算法以促進權衡多目標學習地圖規劃
論文名稱(外文):Enhancing genetic algorithm combined with TOPSIS to facilitate tradeoff multi-objective learning map scheme
指導教授:朱治平朱治平引用關係
指導教授(外文):Chu, Chih-Ping
口試委員:黃國禎謝孫源黃悅民陳宗禧張怡君
口試委員(外文):Gwo-Jen HwangSun-Yuan HsiehYueh-Min HuangTzung-Shi ChenYi-Chun Chang
口試日期:2015-07-29
學位類別:博士
校院名稱:國立成功大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:104
語文別:英文
論文頁數:82
中文關鍵詞:個人化學習學習地圖規劃權衡多目標基因演算法理想解類似度偏好順序評估法
外文關鍵詞:personalized learninglearning map schemetradeoff multiple objectivesgenetic algorithmTOPSIS
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數位學習領域中一個重要的議題為個人化學習,其考量學習者不同的特性給予學習者個人化的數位課程。科技的興盛使得數位學習環境進入網路學習及雲端學習,學習資源越趨豐富且呈現多種樣貌,往往相似的學習目標可以被達成,透過不同的學習方法或學習素材;當然,這當中存在著一些學習上的順序或關聯性。基於學習者天生不同的特性,對各種學習素材有著不同的學習績效與偏好,像是學習成就(利益目標)和學習效率(成本目標)等。在這樣多元的學習環境,學習地圖規劃可以引導學習者以促進個人化的學習旅程,但其是一個NP-hard的組合最佳化問題,並同時兼具權衡多目標的特性。更重要的是,這樣複雜的一個問題,不但教授者不可能為每位學習者規劃其合適的學習地圖,且對學習者而言是困難且費時對於自行規劃合適的學習地圖。
因此,為了解決上述問題,首先本論文提出一個方法其建立在強化之基因演算法(genetic algorithm)與結合理想解類似度偏好順序評估法(TOPSIS),以促進搜尋學習地圖規劃的近似最佳解;除此之外,本論文發展了一個學習管理網站系統其建立在所提的方法上,以幫助教授者提供客製化的學習旅程給學習者,使其可以平順地學習數位課程藉由所規劃之學習地圖的輔助。透過績效評估與比較的實驗,本論文所提的方法顯著地優越於比較的一個基因演算法和一個隨機法。實驗結果顯示,所提的方法在有效性和效率性比基因演算法好,表示所提的強化基因演算法可以改善與提升搜尋性能對於準確且可靠的方案給學習地圖規劃;換句話說,基於所提方法所發展的學習系統,其能夠有效率地規劃具可靠且高品質的學習地圖,因此,教授者與學習者可以專注在他們學習活動的任務本質上。
An important issue in personalized learning is to provide learners with personalized e-course under considering different learning characteristics. With the coming of the generation of non-sequential web-based learning, it has explosive learning materials appeared in various forms, the similar learning goal can be achieved through alternative learning materials, and some relationships are existed among learning goals and materials. Based on different learner characteristics, various learning materials have different effects on achieving the specific learning goal, such as desired learning performance (Benefit objective), required learning time (Cost objective), and so forth. Owing to the above problems, learning map scheme which guides learners to facilitate personalized learning itineraries, is NP-hard combination optimization with tradeoff multiple objectives. More importantly, it is not only impossible for instructors to scheme the fitting learning maps for learners, but also difficult and time-consuming for learners to scheme their fitting learning maps by themselves.
Hence, to cope with this problem, this dissertation first proposed an approach based on enhanced genetic algorithm with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), to facilitate the search for the near-optimal solution of learning map scheme. Moreover, a web-based learning management system based on the proposed approach was developed to help instructors facilitate the customized learning itineraries for learners, to provide learners with learning scheme on learning map to facilitate smoothly personalized e-course. By the extensive performance evaluations and comparisons, the proposed approach is significantly superior to a genetic algorithm and a Random approach. The experiment results not only verify the effectiveness and efficiency of the proposed approach, but validate the enhancement for the genetic algorithm, to improve and promote the search performance for a more accurate and reliable solution of learning map scheme. That is, the developed system with the proposed approach is able to efficiently scheme the learning map with reliable and high quality. Consequently, the instructors and learners can concentrate on their tasks.
List of Tables............viii
List of Figures.............ix
Chapter 1. Introduction...........1
1.1 Background............1
1.2 Motivation............2
1.3 Purpose.............4
1.4 Organization...........5
Chapter 2. Related Works...........6
2.1 Personalized e-course...........6
2.2 Genetic algorithms and its applications to e-learning.....8
2.3 TOPSIS and its applications to multi-objective decision making...10
Chapter 3. Problem Definition of Learning Map Scheme.....14
Chapter 4. Proposed Approach based on Enhanced GA with TOPSIS..16
4.1 Chromosome encoding.........18
4.2 Pre-grouping processing.........19
4.3 Initialization operation.........20
4.4 Evaluation operation..........22
4.4.1 Legality check..........23
4.4.2 Attribute value computation.......28
4.4.3 Fitness computation.........29
4.5 Selection operation...........33
4.6 Crossover operation...........35
4.7 Mutation operation...........37
4.8 System implementation..........41
Chapter 5. Experiments and Discussions........47
5.1 Data set and parameter settings.........47
5.2 Effectiveness comparison.........50
5.3 Comparison of solution quality and efficiency.....52
5.4 Comparison of population quality and efficiency.....59
5.5 Quality comparison of Initialization operation.....65
Chapter 6. Conclusions and Future works.......67
References.............71
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