Papers Published in Peer Review Journals

Measuring the impact of student success retention initiatives for engineering students at a private research university (Frontiers in Education Journal Dec2022)

Measuring the impact of student success retention initiatives for engineering students at a private research universityIntroductionStudent success in Science, Technology, Engineering, and Mathematics (STEM) is a national concern. To increase engineering retention and graduation rates at a small private institution, a university council developed a binary classifier to identify high-risk students and proposed interventions that included decoupling first-year Physics and Calculus courses, support in introductory Calculus, and Spatial Visualization (SV) training. This paper aims to validate the binary classifier used to identify the under-prepared students entering their first year and assess the impact of the interventions. We provide a comparative analysis of student success metrics for high-risk engineering students across a decade of cohorts, including 5 years before (2006–2010) and 5 years after (2011–2015) implementation of intentional strategies.MethodsWe validated the binary classifier using an accuracy measure and Matthews Correlation Coefficient (MCC). We used the 2-population proportion test to compare STEM retention and 4- and 6-year graduation rates of High-Risk engineering students before and after interventions and compare student performance in early foundation STEM courses across the same time frame.ResultsThe binary classification model identified High-Risk students with an accuracy of 63–70% and an MCC of +0.28 to +0.30. In addition, we found statistically significant improvement (p < 0.001) in the STEM retention rates, 6-year graduation rates, and first part of Physics, Ca...

Restoration of Noisy Orientation Maps from Electron Backscatter Diffraction Imaging

Restoration of Noisy Orientation Maps from Electron Backscatter Diffraction Imaging - Integrating Materials and Manufacturing InnovationCrystallographic orientations can be measured using scanning electron microscope-based techniques, such as electron backscatter diffraction (EBSD). The orientation data thus obtained may contain noise and misindexed data. There are several methods to restore the orientation data. The restorations from these methods may have varying levels of quality. Moreover, many such methods are parameter-dependent. Therefore, finding suitable parameter settings for optimal restorations can take time and effort for users of such methods. In this paper, we propose an algorithm to obtain high-quality restorations of noisy orientation data and to circumvent the parameter selection problem by automating it. We estimate the noise variance in the data to determine the amount of denoising to apply. We then use this information to determine the stopping criteria for a vector-valued weighted total variation flow, a nonlinear diffusion applied to the noisy orientation map. We compare the results obtained by our approach with the results from other commonly used denoising filters. As benchmarks, we used simulated EBSD maps with varying noise levels. Our proposed method outperformed denoising methods, such as mean, median, spline, half-quadratic, and Kuwahara filters. The denoising results were statistically significantly better for higher levels of noise.