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Inferring expertise in knowledge and prediction ranking tasks.
Michael D Lee1, Mark Steyvers, Mindy de Young
1Department of Cognitive Sciences, University of California, Irvine, CA 92697-5100, USA. mdlee@uci.edu
Topics in Cognitive Science
|January 19, 2012
Summary
This study introduces a cognitive model to measure expertise in ranking tasks. The model-based expertise measure is more accurate than self-reports for general knowledge and prediction tasks.
Area of Science:
- Cognitive Science
- Psychometrics
- Human-Computer Interaction
Background:
- Assessing expertise is crucial in various fields.
- Traditional methods like self-reports have limitations in accuracy.
- Rank ordering tasks provide a unique window into an individual's knowledge structure.
Purpose of the Study:
- To develop and validate a cognitive modeling approach for measuring expertise in rank ordering problems.
- To compare the efficacy of model-based expertise measures against self-report measures.
- To explore the applicability of this approach across diverse knowledge domains.
Main Methods:
- Developed a cognitive model to capture individual differences in knowledge for rank ordering tasks.
- Collected ranking data from participants on general knowledge and prediction tasks.
- Inferred expertise using the cognitive model and compared it with self-reported expertise and actual task accuracy.
Main Results:
- The cognitive model-based expertise measure significantly outperformed self-report measures.
- This improved accuracy was consistent across general knowledge tasks (e.g., ordering holidays) and prediction tasks (e.g., sports, TV).
- The model effectively infers expertise directly from provided rankings.
Conclusions:
- Cognitive modeling offers a robust and objective method for assessing expertise in rank ordering tasks.
- This approach surpasses traditional self-assessment, providing more reliable insights into knowledge.
- Future research can explore the broader applications and limitations of cognitive models in expertise assessment.
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