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Publication Details

Demonstration-based Solution Authoring for Skill Assessment

by Melinda Gervasio and Karen Myers and Michael Wessel

in Advances in Cognitive Systems, vol. 5,

2017.

Abstract

The high cost of developing content has been a major impediment to the widespread deployment of intelligent training systems. To enable automated skill assessment, traditional approaches have required significant time investment by highly trained individuals to encode first-principles domain models for the training task. In contrast, approaches grounded in example-based methods have been shown to significantly reduce authoring time. This paper reports on an approach to creating solution models for automated skill assessment using an example-based methodology, specifically targeting domains for which solution models must support robustness to learner mistakes. With this approach, a content author creates a baseline solution model by demonstrating a solution instance and then specifies a set of annotations to generalize from that instance to a comprehensive solution model. Results from a user study show that domain experts are comfortable with the approach and capable of applying it to generate quality solution models.

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Associated Projects

SAVE

Semantically Enabled Assessment in Virtual Environments (SAVE)

The SAVE framework supports training in virtual environments through automated assessment of learner performance and tools for content authoring.

AIC Personnel

Name Title E-mail
Gervasio, Melinda Principal Scientist
Myers, Karen Program Director & Principal Scientist
Wessel, Michael A Computer Scientist

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