隨著網際網路快速的發展，數位學習(E-Learning)的概念也隨之被提出；在此同時，為了提升數位學習環境的學習品質，許許多多的研究者，投入了大量的時間與人力，研究如何提升學習者的學習成效，如:適性化學習(adaptive learning)、推薦型數位學習系統(Recommendation e-learning system)…等；在這些研究中，往往都需要大量的學習者行為之資料，藉以分析與實驗之用，像是適性化學習環境中為了要分類學習者類型所需要的分析資料，或是在推薦型數位學習系統中所需要的測試資料以確保系統能精確的推薦每位學習者適當的學習內容。然而，建立一個數位學習網站，用來收集學生資料，是需要花費相當多的時間與人力的，並且要收集到足夠的資料也通常要花費很長的時間，因此，本篇文章基於彩色派翠網路(Coloured Petri Nets)提出Behavioral Coloured Petri Nets (BCPN)，用以塑模(model)真實環境下學習者的行為；再者，為了驗證BCPN之可用性(availability)，本研究提出一個以BCPN為基礎的Browsing Behavior Model(B2 Model)，除了可用來塑模並產生學生在數位學習環境中之瀏覽行為外，還可提供推薦數位學習系統中針對不同學習者推薦或預測學習內容之依據。

With rapid advance of the Internet, the notion of e-learning has been proposed. Meanwhile, to enhance the students’ learning performance, many researchers spend a great deal of time and effort to investigate how to promote learning quality in e-learning environment. Among such researches, the collection of the student’ behaviors is necessary for analysis and experimentation purpose. However, the building of an e-learning system with collecting the students’ behaviors needs to take a great deal of efforts and time. Besides, the collection of sufficient students’ behavior also consumes a period of long time. Hence, this study proposes a Behavioral Coloured Petri Nets (BCPN) to model the student’ behaviors. Furthermore, to verify the availability of the proposed BCPN, a Browsing Behavior model (B2 model) based on BCPN is presented to model and then generate the student’ browsing behaviors. In this study, we also compare the actual data with generated browsing behaviors. The compared result proves: (1) the generated browsing behaviors approach to the actual data; and (2) B2 model can be used to predict or recommend the learning content for student in an intelligent e-learning system.

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