基于EPN的学习资源组合研究

EPN-based Approach for E-learning Resource Composition

提出基本Petri网(EPN)的资源组合方法,用于对学习资源和组合流程进行建模和分析。首先对基于Petri网的组合方法进行综述,这些方法推动了资源组合的发展,但其所描述的资源及变迁规则不适合学习资源。为此提出了基于EPN的资源组合,以解决这一问题。改进EPN的变迁规则,把学习资源封装为Web服务,使用改进后的模型对服务进行建模,然后提出组合算法根据已知条件和变迁规则组合学习资源。最后,给出学习资源组合的实例验证方法的可行性。

In e-learning domain, learning resource composition is essential to improve the efficiency of courseware development. This paper presented an EPN-based approach to e-leaming resource composition flows modeling and analysis. Till now, most methods are based on high-level Petri nets （HPN）, but the resources HPN described are not suitable for learning resource composition. That leads to the inefficiency in modeling and analyzing composition flows. We proposed a EPN-based approach to the issue. We described technique for transition rules representing, algorithm, and workflow, so that resource composition can be automatically carried out. Finally, an example shows the method is suitable to the resource composition.