responses

<p>Simulated response sets for 5000 students on an exam with 30 items.</p>

datasets

Based on the work of Curi, Converse, Hajewski, and Oliveira (2019) <doi:10.1109/IJCNN.2019.8852333>. This package provides easy-to-use functions which create a variational autoencoder (VAE) to be used for parameter estimation in Item Response Theory (IRT) - namely the Multidimensional Logistic 2-Parameter (ML2P) model. To use a neural network as such, nontrivial modifications to the architecture must be made, such as restricting the nonzero weights in the decoder according to some binary matrix Q. The functions in this package allow for straight-forward construction, training, and evaluation so that minimal knowledge of 'tensorflow' or 'keras' is required.

Geoffrey Converse

ML2Pvae

Variational Autoencoder Models for IRT Parameter Estimation

Suely Oliveira

Mariana Curi

responses function

<p>A data frame with 30 columns and 5000 rows.
Entry <code>[j,i]</code> is 1 if student <code>j</code> answers item <code>i</code> correctly, and 0 otherwise.</p>

responses: Response data

Description

Usage

Arguments

Format