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Dynamic Bayesian Network Modeling of Game-Based Diagnostic Assessments.

¹Arizona State University.

Multivariate Behavioral Research

|April 4, 2019

Summary

This study introduces a dynamic Bayesian network model to assess student skills and misconceptions using educational video game data. This approach enables real-time diagnostic inferences about student learning within digital game environments.

Keywords:

Dynamic Bayesian network diagnostic assessment game-based assessment

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Area of Science:

Educational Technology
Cognitive Science
Data Science

Background:

Digital games provide engaging platforms for evaluating student understanding and identifying learning gaps.
Traditional assessment methods may not fully capture the nuances of student performance in interactive environments.

Purpose of the Study:

To propose a dynamic Bayesian network (DBN) modeling approach for analyzing student performance data from educational video games.
To enable on-the-fly diagnostic inferences about student skills and misconceptions.

Main Methods:

Developed a Bayesian approach integrating dynamic Bayesian networks, cognitive diagnostic modeling, and process data analysis.
Implemented the model for observations of student performance within an educational video game context.

Main Results:

The proposed DBN model effectively facilitates real-time inferences about student proficiencies.
The approach allows for the identification of specific skills and misconceptions based on in-game actions.

Conclusions:

Dynamic Bayesian networks offer a powerful framework for real-time cognitive diagnostics in educational games.
This methodology enhances the diagnostic capabilities of digital learning environments for personalized feedback.