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Computerized Adaptive Testing System of Functional Assessment of Stroke

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Assessing multiple abilities through process data in computer-based assessments: The multidimensional sequential

Yuting Han^1,2,3, Feng Ji⁴, Pujue Wang⁵

¹Cognitive Science and Allied Health School, Beijing Language and Culture University, Beijing, China.

Behavior Research Methods

|April 22, 2025

Summary

This summary is machine-generated.

This study introduces a new Multidimensional Sequential Response Model (MSRM) for computer-based assessments. The MSRM accurately estimates multiple abilities using process data, enhancing tailored educational interventions.

Keywords:

Bayesian estimation Computer-based assessments Multidimensional sequential response model (MSRM)Process data

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Area of Science:

Educational Measurement and Assessment
Psychometrics
Cognitive Psychology

Background:

Computer-based assessment (CBA) increasingly utilizes process data to understand examinee response patterns.
Traditional methods often focus on single ability estimation, limiting comprehensive assessment.
There is a need for advanced models to leverage detailed process data for multidimensional ability assessment.

Purpose of the Study:

To introduce the Multidimensional Sequential Response Model (MSRM) for analyzing process data in CBA.
To develop and evaluate a Bayesian estimation method for the MSRM.
To demonstrate the practical application and utility of the MSRM in educational and psychological contexts.

Main Methods:

Development of the Multidimensional Sequential Response Model (MSRM).
Application of a Bayesian estimation technique for MSRM parameters.
Validation through a Monte Carlo simulation study with varying conditions (sample size, sequence length).
Empirical testing using two real-world case studies.

Main Results:

The MSRM demonstrated adequate accuracy and computational efficiency in parameter estimation.
Estimation quality improved with larger sample sizes and longer response sequences.
The model proved effective in practical application across different assessment contexts.
The MSRM provided detailed insights into multidimensional ability mastery.

Conclusions:

The MSRM is a viable and effective tool for assessing multiple latent abilities using process data from CBA.
The proposed Bayesian estimation method is accurate and computationally efficient.
The MSRM offers a pathway to more precise ability profiling, supporting tailored instruction and targeted interventions.