Solution Authoring via Demonstration and Annotation: An Empirical Study



Karen Myers and Melinda Gervasio. Solution Authoring via Demonstration and Annotation: An Empirical Study, in Proceedings of the 16th IEEE International Conference on Advanced Learning Technologies (ICALT), 2016.


A major impediment to the widespread deployment of intelligent training systems is the high cost of developing the content that drives their operation. Techniques grounded in end-user programming have shown great promise for reducing the burden of content creation. With these approaches, a domain expert demonstrates a solution to a task, which is then generalized to a broader model. This paper reports on a concept validation study that provides an empirical basis for the design of solution authoring frameworks based on end-user programming techniques. The study shows that non-expert users are comfortable with the approach and are capable of applying it to generate quality solution models. It also identifies constructs that, while important for accurate solution characterization, can lead to confusion and so warrant special care in tool design. Based on these results, we make recommendations for the design of solution-authoring tools in support of automated assessment for tutoring systems. Keywords: Artificial Intelligence, Artificial Intelligence Center, AIC, automated assessment, end user programming, programming by demonstration, intelligent training systems

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