基于PSO的模糊置标语言在学生学习性能评估中的应用
PSO-based Fuzzy Markup Language for Student Learning Performance Evaluation and Educational Application
摘要：
本文提出了一种基于模糊置标语言（FML）的粒子群优化（PSO）智能体，用于学生学习性能评估和教育应用，该智能体是根据常规测试的响应数据和项目响应理论设计的。首先，我们应用基于GS的参数估计机制根据响应数据估计项目参数，然后将其结果与基于IRT的贝叶斯参数估计机制的结果进行比较。此外，我们提出了一个静态IRT测试组装机制，组装一个传统的测试形式。提出了基于FML的动态评估机制，推断出具有不同能力的学生对该项目做出正确反应的概率。此外，本文还提出了一种新的PFML学习机制，优化项目和学生之间的参数。最后，我们采用K折交叉验证机制来评估所提出的代理的性能。实验结果表明，新的PFML学习机制的参数估计和学习优化性能良好。我们相信，PFML将成为教育研究和教学的参考，以及未来人机教育应用的重要协同学习机制。
英文摘要：
This paper proposes an agent with particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for students learning performance evaluation and educational applications, and the proposed agent is according to the response data from a conventional test and an item response theory. First, we apply a GS-based parameter estimation mechanism to estimate the items parameters according to the response data, and then to compare its results with those of an IRT-based Bayesian parameter estimation mechanism. In addition, we propose a static-IRT test assembly mechanism to assemble a form for the conventional test. The presented FML-based dynamic assessment mechanism infers the probability of making a correct response to the item for a student with various abilities. Moreover, this paper also proposes a novel PFML learning mechanism for optimizing the parameters between items and students. Finally, we adopt a K-fold cross validation mechanism to evaluate the performance of the proposed agent. Experimental results show that the novel PFML learning mechanism for the parameter estimation and learning optimization performs favorably. We believe the proposed PFML will be a reference for education research and pedagogy and an important co-learning mechanism for future human-machine educational applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
