合作學習為目前廣為被應用於教學中的策略之一，其目的為提升所有參與者之學習成效，為了達成此目標，首先便是考量如何協助教師進行合作學習之分組以營造好的學習情境。然而，當教師面對大量的學生時，特別是在電腦輔助合作學習的環境中，要求教師同時考量多個分組準則來將學生分配在適合的合作學習群組之中，這會對教師造成很大的負擔及工作量，基於此，本研究將此合作學習分組問題數學化，並且以學生之先備知識為分組之準則來建構此問題。
先備知識為學習者學習中不可或缺之基礎，其能夠影響學習者如何去解釋、組織、同化與吸收新的教學與學習，一般而言，測驗為常被教師使用於評估學習者之先備知識，然而，傳統的測驗方式往往只會於測驗結果提供一分數來讓教師與學習者瞭解其測驗結果，如此的方式可能會造成教師與學習者無法清楚的瞭解問題之所在，基於此，本研究提出一先備知識測量模組來協助教師診斷學習者之先備知識能力，透過診斷的結果，本研究應用粒子群聚最佳化演算法提出一合作學習分組模組，此模組能協助教師以學習者之先備能知識的能力來為學習者進行合作學習分組。
為了評估本研究所提出之合作學習分組模組的實用性與強健性，本研究首先建置了一系列系統化的評估實驗來分析此模組之效能與強健性，此分析結果顯示此模組能夠有效的以學習者之先備知識來進行合作學習之分組，此外，本研究應用所提出之系統於實際教學情境中來探討其對於教學成效之影響，實驗結果顯示出本研究所提出之系統能夠改善學習者之學習動機與提升學習成效，最後，本研究針對先備知識之診斷結果來進行分析，其結果顯示所提出之診斷機制能夠準確的診斷出學習者之先備知識的能力。

One goal of collaborative learning is to maximize the learning performance of all participating students. In order to achieve this aim, the first step is to consider how to assist instructors in forming well-structured collaborative learning groups with a good work atmosphere to promote successful outcomes for all members. Nevertheless, when the instructors face a large number of students, especially in computer-supported collaborative learning environments, simultaneously considering several grouping criteria to form the students in an appropriate collaborative learning context is almost impossible. To address this issue, this study develops a group formation problem to model the formation of collaborative learning groups based on students’ prior knowledge.
Prior knowledge is an essential foundation for learning, as it affects the interpretation, organization, assimilation, and absorption of new instructions. Generally, tests are used to help instructors evaluate students’ prior knowledge. Nevertheless, conventional testing approaches usually assign only a score to students, and this may mean that both students and instructors are unable to accurately identify misunderstandings in the formers’ prior knowledge. Therefore, this study proposes a prior knowledge measurement model to assist instructors in diagnosing student’s prior knowledge. Moreover, this study is based on a novel approach called particle swarm optimization (PSO) to propose a group formation model for forming well-structured collaborative learning groups based on students’ prior knowledge.
To demonstrate that the group formation model is an applicable and robust approach, a series of systematical evaluations were conducted to analyze the performance of the group formation model. The results show that the model can efficiently and effectively form collaborative learning groups based on students’ prior knowledge. In addition, an experiment was conducted to evaluate the efficacy of the proposed approach with regard to improving teaching and learning performance. The results show that the system is able to improve students’ learning motivation and enhance teaching and learning performance. In addition, the diagnosis evaluation results show that the proposed system can accurately diagnose the students’ prior knowledge.

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