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Related Experiment Video

Updated: Nov 6, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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First-Order Learning Models With the GDINA: Estimation With the EM Algorithm and Applications.

Hulya D Yigit1, Jeffrey A Douglas1

  • 1University of Illinois at Urbana-Champaign, USA.

Applied Psychological Measurement
|May 7, 2021
PubMed
Summary
This summary is machine-generated.

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This study introduces an efficient Expectation-Maximization (EM) algorithm for estimating student learning trajectories using cognitive diagnostic models (CDMs) and Markov transition models. The algorithm shows promising accuracy and efficiency for tracking knowledge profiles over time.

Area of Science:

  • Educational Measurement
  • Psychometrics
  • Learning Analytics

Background:

  • Understanding longitudinal learning paths is crucial in educational settings.
  • Cognitive Diagnostic Models (CDMs) combined with transition models offer detailed insights into student knowledge over time.
  • Efficient parameter estimation is key to the practical application of these models.

Purpose of the Study:

  • To present an efficient Expectation-Maximization (EM) algorithm for estimating student learning trajectories.
  • To evaluate the algorithm's performance in parameter estimation accuracy and computational efficiency.
  • To apply the model to real-world spatial reasoning data.

Main Methods:

  • Implemented the EM algorithm for GDINA (generalized deterministic inputs, noisy, "and" gate) and its submodels, coupled with a first-order Markov transition model.
Keywords:
Expectation–Maximization algorithmcognitive diagnosis modelsfirst-order hidden Markov modellearning trajectories

Related Experiment Videos

Last Updated: Nov 6, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

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Published on: March 13, 2021

9.6K
  • Conducted a simulation study varying sample size, number of attributes, time points, and measurement model complexity.
  • Assessed parameter recovery using attribute- and vector-level agreement rates and root mean square error, alongside computation times.

    • Main Results:

    • The EM algorithm demonstrated promising parameter estimation accuracy and relatively short computation times across most simulated conditions.
    • Increased computation time and reduced parameter recovery were observed only under conditions of low sample size and high attribute numbers.
    • Model fit analysis using BIC favored the DINA (deterministic inputs, noisy, "and" gate) model over GDINA for the spatial reasoning dataset.

    Conclusions:

    • The developed EM algorithm is a practical and efficient tool for estimating student learning trajectories using CDMs and transition models.
    • The algorithm's performance is robust, though sample size and attribute complexity should be considered for optimal results.
    • The findings support the utility of diagnostic models in understanding and tracking student learning progress over time.