過去研究指出在合作學習中良好的平衡分組有助於提升學生的學習效能。然而考慮大量的學生人數與學習特徵數去建立良好的平衡分組，對於授課者而言會花費相當可觀的時間與心力。因此如何自動化地建立良好的平衡分組，在合作學習中已成為一個重要的議題。
本論文提出了一個基於基因演算法結合近似最佳解績效排序法的分組策略，當分組需考慮多個學習特徵時，幫助授課者建立組內異質與組間同質的合作式學習分組。本論文所提出的方法不僅可解決單一方向的多屬性分組問題，且可解決不同方向多特徵屬性間具有衝突性或關聯性時權衡分組問題。
論文亦利用不同大小問題的資料，如：不同學生人數資料、不同特徵個數資料以及真實學生資料，當作實驗素材進行實驗。實驗結果說明所提出的分組方法在解的品質、執行時間、搜尋速度以及穩定度上都優於隨機法與遺傳基因演算法。綜言之，論文所提出的分組策略具有更佳的效能、效率、強健度以及較佳的分組結果與品質。

In collaborative learning, previous researches indicated that well-balanced groups could enhance students’ learning performance. However, to construct well-balanced groups for large number of students with multiple characteristics will cost considerable efforts and time to instructors. Hence, how to automatically construct well-balanced learning groups has been an important issue in collaborative learning.
This thesis proposes a new grouping strategy based on genetic algorithm (GA) with Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) to assist instructors in constructing inter-homogeneous and intra-heterogeneous collaborative learning groups. The grouping strategy proposed solves the multi-characteristic grouping problem, where the characteristics are in the same direction or in different directions. Further, the constituent members of groups can be balanced to achieve the inter-groups homogeneity and intra-groups heterogeneity.
Meanwhile, several datasets with different problem sizes including an actual case were employed as experimental materials to conduct a series of experiments. Experimental results demonstrated that the proposed grouping method outperforms the random and genetic algorithm methods in terms of solution quality, executing time, search speed and stability. To sum, the proposed grouping method is more effective, efficient, robust, and has better grouping result and quality.

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