Comparing factor mixture modeling and conditional Gaussian mixture variational autoencoders for cognitive profile clustering

Comparing factor mixture modeling and conditional Gaussian mixture variational autoencoders for cognitive profile clustering

IntroductionUnderstanding individual cognitive profiles is crucial for developing personalized educational interventions, as cognitive differences can significantly impact how students learn. While traditional methods like factor mixture modeling (FMM) have proven robust for identifying latent cogni...

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Bibliographic Details
Main Authors: Matteo Orsoni, Sara Giovagnoli, Sara Garofalo, Noemi Mazzoni, Matilde Spinoso, Mariagrazia Benassi
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-05-01
Series:Frontiers in Psychology
Subjects:
variational autoencoders
clustering
machine learning
cognitive profiles
factor mixture modeling
Online Access:https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1474292/full
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