This proposal seeks funds to validate a Dynamic Evaluation of Enhanced Problem-solving (DEEP) methodology that has shown promise in limited studies. The overall aim is to advance knowledge in assessment methods for complex domains in SMET disciplines. The DEEP methodology is based on a view of learning as becoming more expert-like and more skilled in higher-order causal reasoning and problem solving. In this view learning is treated as a continuous process of growth and assessment involves tapping that process.

Intellectual Merit The panel felt that this was a very strong proposal. The approach was seen as novel. It was judged to be addressing an important type of student learning:-- complex problem solving.) characterized as Dynamic Evaluation of Enhanced Problem Solving (DEEP). The DEEP process itself is grounded in literature on solving ill structured problems and involves (briefly stated): 1) identifying characteristic complex problems in appropriate modules of a problem-centered curriculum; 2) eliciting expert patterns of problem solving in that curriculum; 3) representing expert patterns in both textual and graphic formats; 4) determining salient features of these representations; 5) establishing measures of similarity in salient features of expert representations; 6) eliciting novice patterns for the same problem-solving activities; 7) representing novice patterns in the same formats as used for experts; 8) identifying the presence or absence of salient features (from experts responses) in novice representations; 9) establishing measures of distance from expert patterns for each salient feature; and 10) tracking and analyzing changes in learner responses over time and through instructional interventions. Broader Impacts

The DEEP project has the potential for broad applicability of the approach to assessment of complex domains of problem solving. By development in multiple subject matter areas and educational levels generalizability is enhanced. By developing formal frameworks for analysis of expert and novice problem solving and a technological base for representations, the potential for tracking student progress is enhanced. By building on research with less complex problems the potential for tackling the complex domains is enhanced. The project thus has the potential to provide the foundation for further research and development of tools for assessment of progress of student learning in important complex domains.

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Syracuse University
United States
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