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Identifying Disengaged Responding in Multiple-Choice Items: Extending a Latent Class Item Response Model With Novel

Jana Welling¹, Timo Gnambs¹, Claus H Carstensen²

¹Leibniz Institute for Educational Trajectories, Bamberg, Germany.

Educational and Psychological Measurement

|June 20, 2024

Summary

Disengaged responding in educational assessments can be identified using process data like text rereading. While this improved model fit, it offered only marginal gains in detecting unmotivated test-takers.

Keywords:

computer-based assessments disengaged responding item response theory multiple-choice items process data rapid guessing

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Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

Area of Science:

Educational Measurement
Psychometrics
Cognitive Psychology

Background:

Disengaged responding threatens the validity of educational assessments.
Current methods using response times risk misclassification.
Process data offers richer insights into test-taking behavior.

Purpose of the Study:

To investigate the utility of process data (text reread, item revisit, answer change) in identifying disengaged responding.
To develop an extended latent class item response model incorporating these indicators.
To compare the extended model with a baseline model using only response time.

Main Methods:

Developed an extended latent class item response model.
Included text reread, item revisit, and answer change as predictors of engagement.
Applied the model to a sample of 1,932 German university students.

Main Results:

The extended model showed a better fit than the baseline model.
Item response time and text reread were significant predictors of engagement.
No systematic differences were found in parameter estimation or classification.

Conclusions:

Process data, specifically text rereading, offers a marginal improvement in identifying disengaged responding.
Further research is needed to fully leverage process data for detecting unmotivated test-takers.