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