She is involved with engineering education innovations from K-12 up to the collegiate level. She received her Ph.D. in Mechanical Engineering from Georgia Tech in 2012.

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Typical approaches to assessing students' understanding of the engineering design process (EDP) include performance assessments that are time-consuming to score. It is also possible to use multiple-choice (MC) items to assess the EDP, but researchers and practitioners often view the diagnostic value of this assessment format as limited. However, through the use of distractor analysis, it is possible to glean additional insights into student conceptualizations of complex concepts. Using an EDP assessment based on MC items, this study illustrates the value of distractor analysis for exploring students' understanding of the EDP. Specifically, we analyzed 128 seventh grade students' responses to 20 MC items using a distractor analysis technique based on Rasch measurement theory. Our results indicated that students with different levels of achievement have substantively different conceptualizations of the EDP, where there were different probabilities for selecting various answer choices among students with low, medium, and high relative achievement. We also observed statistically significant differences (p < 0.05) in student achievement on several items when we analyzed the data separately by teacher. For these items, we observed different patterns of answer choice probabilities in each classroom. Qualitative results from student interviews corroborated many of the results from the distractor analyses. Together, these results indicated that distractor analysis is a useful approach to explore students' conceptualization of the EDP, and that this technique provides insight into differences in student achievement across subgroups. We discuss the results in terms of their implications for research and practice.

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In this paper, a value-based global optimization (VGO) algorithm is introduced. The algorithm uses kriging-like surrogate models and a sequential sampling strategy based on value of information (VoI) to optimize an objective characterized by multiple analysis models with different accuracies. VGO builds on two main contributions. The first contribution is a novel surrogate modeling method that accommodates data from any number of different analysis models with varying accuracy and cost. Rather than interpolating, it fits a model to the data, giving more weight to more accurate data. The second contribution is the use of VoI as a new metric for guiding the sequential sampling process for global optimization. Based on information about the cost and accuracy of each available model, predictions from the current surrogate model are used to determine where to sample next and with what level of accuracy. The cost of further analysis is explicitly taken into account during the optimization process, and no further analysis occurs if the expected value of the new information is negative. In this paper, we present the details of the VGO algorithm and, using a suite of randomly generated test cases, compare its performance with the performance of the efficient global optimization (EGO) algorithm (Jones, D. R., Matthias, S., and Welch, W. J., 1998, “Efficient Global Optimization of Expensive Black-Box Functions,” J. Global Optim., 13(4), pp. 455–492). Results indicate that the VGO algorithm performs better than EGO in terms of overall expected utility—on average, the same quality solution is achieved at a lower cost, or a better solution is achieved at the same cost.