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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65590

    Title: 基於互補度與社群網路分析於基因演算法之分組機制;Grouping based on Complementary Degree and Social Network Analysis in Genetic Algorithm
    Authors: 蘇皇名;Su,Huang-Ming
    Contributors: 資訊工程學系
    Keywords: 合作式學習;分組;基因演算法;社群關係;Cooperative Learning;Grouping;Genetic Algorithm;Social Network
    Date: 2014-07-10
    Issue Date: 2014-10-15 17:05:37 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 在過去幾年中,合作式學習已成為最重要的教學策略之一。在合作學習裡,如何將學習者們適當地分組成為了一個重要的議題。為了解決這個問題,許多學者提出了各式各樣的方法。
    ;In the past years, Cooperative Learning has become one of the most important teaching strategies. Helping learners grouping appropriately is now becoming more and more important. To solve the problem, a lot of methods have been proposed.
    In this study, we employ a novel grouping approach that considers the complementary degree of learner’s learning state and social networks to enhance interaction and teamwork between learners. This study also used genetic algorithm (GA) to generate better grouping results. Moreover, a set of systems have been developed. The e-learning system was developed for learners and tutors that they can view the grouping result and academic information conveniently. In grouping system, it will record learners’ learning statuses and interaction between team members to adjust grouping result from each assignment dynamically.
    In the end, the results show that the proposed approach can optimize the grouping well. The proposed method of grouping can generate high heterogeneous grouping results and the learners are satisfied with the grouping results at the same time. Meanwhile, the learners’ learning effects are improved by using the proposed method of grouping in cooperative learning. The mean score of post-test in the experimental group was higher than the control group. Moreover, the experimental group learners’ academic level reaching more consistent than the learners of the control groups. Finally, the learners said that they liked the grouping function of the system in their feedback. The grouping method of the system really helped them to learn efficiently.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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