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Using latent variable models to make gaming-the-system detection robust to context variations.

Yun Huang1, Steven Dang2, J Elizabeth Richey3

  • 1Carnegie Mellon University, Pittsburgh, USA.

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Summary
This summary is machine-generated.

A new method, latent variable-based gaming detection (LV-GD), improves the accuracy of identifying students who exploit educational systems without learning. This approach accounts for context, offering a more reliable measure of gaming the system behavior.

Keywords:
Behavior modelingGaming detectorGaming the systemItem response theoryLatent variable models

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Area of Science:

  • Educational Technology
  • Learning Analytics
  • Artificial Intelligence in Education

Background:

  • Gaming the system, exploiting educational platforms for progress without learning, is typically linked to reduced academic outcomes.
  • Previous gaming detection methods showed questionable validity in diverse experimental contexts, failing to correlate gaming with learning.
  • Contextual variations were identified as a key factor influencing the association between detected gaming and learning.

Purpose of the Study:

  • To introduce and validate a novel approach, latent variable-based gaming detection (LV-GD), for more robustly estimating student gaming tendencies.
  • To address the limitations of existing gaming detectors by controlling for contextual factors.
  • To improve the reliability and validity of behavioral measures in educational research.

Main Methods:

  • Developed latent variable-based gaming detection (LV-GD) by applying a statistical model to an existing action-level gaming detector.
  • LV-GD estimates a student's gaming tendency relative to expected population levels within specific contexts.
  • Validated LV-GD across three datasets, comparing its performance against the original detector.

Main Results:

  • LV-GD demonstrated superior validity and reliability compared to the original gaming detector in assessing the association between gaming and learning.
  • The new method accurately identified intervention effects on gaming behavior.
  • LV-GD revealed correlations between gaming and perceived mathematical competence and illuminated productive gaming behaviors.

Conclusions:

  • Latent variable-based gaming detection (LV-GD) offers a more accurate and context-aware method for identifying students who game educational systems.
  • LV-GD enhances the practical utility of gaming detection, aiding in intervention analysis and understanding student behavior.
  • This approach provides a cost-effective and generalizable framework for developing robust behavioral measures in educational research.