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Longitudinal Cognitive Diagnostic Assessment Based on the HMM/ANN Model.

Hongbo Wen¹, Yaping Liu¹, Ningning Zhao²

¹National Assessment Center of Education Quality, Beijing Normal University, Beijing, China.

Frontiers in Psychology

|October 5, 2020

Summary

This summary is machine-generated.

This study compares traditional cognitive diagnosis models with a novel longitudinal hidden Markov model (HMM)/artificial neural network (ANN) approach. The HMM/ANN model effectively tracks student cognitive skills, showing high accuracy, especially with fewer attributes.

Keywords:

SSOM neural network cognitive diagnostic assessment hidden Markov model longitudinal assessment reading comprehension

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Area of Science:

Educational Measurement
Psychometric Modeling
Artificial Intelligence in Education

Background:

Cognitive diagnostic assessment (CDA) utilizes psychometric models to evaluate student cognitive and knowledge development.
Traditional cognitive diagnosis models (CDMs) have limitations in longitudinal tracking of skill progression.
The integration of advanced computational models offers potential for more dynamic student assessment.

Purpose of the Study:

To compare the performance of traditional CDMs with a longitudinal CDM based on the hidden Markov model (HMM) and artificial neural network (ANN).
To evaluate the efficacy of the HMM/ANN model in longitudinally tracking students' cognitive skills.
To investigate factors influencing the classification accuracy and transition probabilities of the HMM/ANN model.

Main Methods:

Development and application of a longitudinal cognitive diagnosis model integrating HMM with ANN as the measurement model.
Utilized simulation studies to assess model performance under various conditions.
Employed empirical data analysis to validate the model in real educational settings.

Main Results:

The HMM/ANN model demonstrated high classification accuracy and correct conversion rates, particularly when the number of attributes was small.
The model effectively tracked the development of students' cognitive skills in practical educational scenarios.
Classification accuracy was influenced by item quality, item count, and attribute count, but not sample size.

Conclusions:

The HMM/ANN model provides a robust framework for longitudinal cognitive diagnosis, outperforming traditional methods in dynamic skill tracking.
Optimizing item quality and quantity, while managing the number of attributes, enhances the HMM/ANN model's predictive power.
This approach offers significant advancements for understanding and supporting student learning trajectories in educational contexts.