本研究旨在探討基於基因演算法(genetic algorithm)在同時考量先備知識水平及學習角色異質互補，以及社會互動關係同質因素下，發展之最佳化問題導向網路合作學習分組策略，是否有助於提升問題導向網路合作學習之學習成效、互動關係、團體效能與團體凝聚力。 本研究採用準實驗研究法，以新北市某國小六年級三個不同班級合計83名學生為研究對象，並將三個班級學生隨機分派為採用基因演算法最佳化分組策略的實驗組，以及分別採用隨機分組及學生自行選擇分組策略的控制組一與控制組二，三組學習者皆在問題導向學習系統（Problem-based learning system,簡稱PBL）上進行不同分組策略之問題導向網路合作學習活動。藉由學習成效與團體效能與團體凝聚力評量，以及分析三組學習者在問題導向學習系統上的學習歷程與互動關係，最後再輔以半結構式訪談，以驗證三種不同分組策略在學習成效、互動關係、團體效能與團體凝聚力上的差異。 結果顯示本研究所提出之最佳化問題導向網路合作學習分組策略具有提升學習成效之效益；本研究所提出之最佳化分組策略對於促進問題導向網路合作學習之同儕互動具有正面效益；採用不同問題導向網路合作學習分組策略組別學習者在團體效能與團體凝聚力上具有顯著差異；採用最佳化分組策略組別學習者在問題導向網路合作學習的滿意度接近同意的水準。 / This study aims to explore whether the optimized group formation scheme based on genetic algorithm helps students enhance learning performance, interaction, collective efficacy, and group cohesion in collaborative problem-based learning environment. Factors associated with heterogeneous complementation of students’ prior knowledge levels and learning roles and the homogeneity of social interaction relationship were simultaneously considered in the genetic algorithm-based optimized group formation scheme. In this paper, a quasi-experimental research method is employed to assess the effects of three different group formation schemes on the learning performance, interaction, collective efficacy, and group cohesion in collaborative problem-based learning environment. Eighty-three students in three different sixth-grade classes in an elementary school in New Taipei City were invited to participant in the experiment and were randomly divided into three groups: the experimental group, which adopts genetic algorithm-based optimized group formation scheme, and two control groups, one is randomly grouped; while the other allows students group themselves. Learners in these three groups all use collaborative problem-based learning system (CPBL) to perform collaborative problem-based learning activities. Learning performance, interaction, group efficacy and group cohesion evaluation are applied to analyze the learning process and interaction among learners in these three groups. Finally, a semi-structured interview is supplemented to validate the variation of these three different group formation schemes in learning performance, interaction, group efficacy and cohesion. The result showed that the genetic algorithm-based optimized group formation scheme helps students promote learning performance and provides positive effects on peer interaction in collaborative problem-based learning. Three group learners adopting different group formation schemes for collaborative problem-based learning show significant difference on group effectiveness and group cohesion. The satisfaction of learners adopting genetic algorithm-based optimized group formation scheme for collaborative problem-based learning reached a nearly agreed standard.