  This study discusses an alternative tool for modeling student assessment
data. The model constructs networks from a matrix item responses and attempts
to represent these data in low dimensional Euclidean space. This procedure has
advantages over common methods used for modeling student assessment data such
as Item Response Theory because it relaxes the highly restrictive
local-independence assumption. This article provides a deep discussion of the
model and the steps one must take to estimate it. To enable extending a present
model by adding data, two methods for estimating the positions of new
individuals in the network are discussed. Then, a real data analysis is then
provided as a case study on using the model and how to interpret the results.
Finally, the model is compared and contrasted to other popular models in
psychological and educational measurement: Item response theory (IRT) and
network psychometric Ising model for binary data.
