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Assessment methodology for computer-based instructional simulations
Alan Koenig1, Markus Iseli, Richard Wainess
1Graduate School of Education & Information Studies, Center for the Study of Evaluation (CSE), National Center for Research on Evaluation, Standards, & Student Testing (CRESST), University of California, Los Angeles, 300 Charles E. Young Drive North, GSE&IS Building, Box 951522, Los Angeles, CA 90095-1522.
This study explores pedagogical approaches for computer-based instructional simulations. It presents a methodology for automated assessment using ontologies and Bayesian networks in medical simulations.
Area of Science:
- Educational Technology
- Medical Simulation
- Artificial Intelligence in Education
Background:
- Computer-based instructional simulations are increasingly prevalent in military and medical fields.
- Advancements in simulation technology necessitate evolving pedagogical models for instruction, assessment, and feedback.
- Existing educational approaches in medical simulations require review and enhancement.
Purpose of the Study:
- To review current educational approaches for medical simulations.
- To present pedagogical methodologies for designing and developing simulations, particularly from UCLA's Center for Research on Evaluation, Standards, and Student Testing.
- To introduce a methodology for automated assessment in computer-based simulations using ontologies and Bayesian networks.
Main Methods:
- Review of existing literature on educational approaches in medical simulations.
- Presentation of pedagogical methodologies developed at UCLA's CRESST for games and simulations.
- Development of a methodology for automated assessment utilizing ontologies and Bayesian networks.
Main Results:
- A framework for implementing automated assessments in computer-based simulations was presented.
- The advantages and design considerations for using ontologies and Bayesian networks in pedagogical contexts were discussed.
- Methodologies for enhancing instruction, assessment, and feedback within sophisticated simulations were explored.
Conclusions:
- Automated assessment using ontologies and Bayesian networks offers a viable approach for computer-based simulations.
- The proposed methodologies can inform the design and development of more effective educational simulations.
- Evolving pedagogical models are crucial for leveraging the full potential of advanced instructional simulations.
