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15/05/2020 - Machine learning for estimation in IRT models - Palestrante: Mariana Curi (ICMC-USP)

Quando 15/05/2020
das 14h00 até 16h00
Onde Google Meet (à distância)
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Seminário Conjunto UFSCAR/USP

Data e Horário:
15/05/2020 às 14h

Local:
Google Meet - Link da apresentação: https://drive.google.com/open?id=1AgJRftK93CL2ab_DPiU2AkLFd1xB2eA0

Título:
Machine learning for estimation in IRT models

Palestrante:
Mariana Curi (ICMC - USP)

Arquivo com apresentação: IMPS2019.pdf

Resumo:

High dimensional latent space is still a challenge for usual estimation methods in Item Response Theory (IRT) models, like MCMC or maximum likelihood. In this work, we propose a Variational Autoencoder (VAE) architecture, a kind of unsupervised deep neural network, for a multidimensional IRT model parameter estimation. Our approach allows us to model high latent trait dimensions, overcoming some of the limitations concerned to “big data” analysis. The simulation studies show that, given enough data, the proposed method is competitive with the state-of-the-art ones with respect to predictive power and is much faster in runtime performance. The new approach is applied to a real data set to illustrate the usefulness of the proposed method in the context of educational assessment.

 

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