PSO-Based Fuzzy Markup Language for Student Learning Performance Evaluation and Educational Application

Fuzzy relationships exist between students' learning performance with various abilities and a test item. However, the challenges in implementing adaptive assessment agents are obtaining sufficient items, efficient and accurate computerized estimation, and a substantial feedback agent. Additionally, the agent must immediately estimate students' ability item by item, which places a considerable burden on the server, especially for a group test. Hence, the implementation of an adaptive assessment agent is more difficult in practice. 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 (IRT)-based three-parameter logistic model. First, we apply a Gauss-Seidel 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 PSO-based FML (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 colearning mechanism for future human-machine educational applications.