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

TL;DR

This paper introduces a PSO-based Fuzzy Markup Language system for evaluating student performance, combining item response theory and novel learning mechanisms, with promising experimental results for educational applications.

Contribution

It presents a new PSO-based FML agent integrating IRT and a novel PFML learning mechanism for student assessment and optimization.

Findings

PFML learning mechanism performs favorably in experiments.

The system effectively estimates item parameters and student abilities.

The approach enhances educational assessment and human-machine learning integration.

Abstract

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.