2 resultados para average gains
em Bucknell University Digital Commons - Pensilvania - USA
Resumo:
Engineering students continue to develop and show misconceptions due to prior knowledge and experiences (Miller, Streveler, Olds, Chi, Nelson, & Geist, 2007). Misconceptions have been documented in students’ understanding of heat transfer(Krause, Decker, Niska, Alford, & Griffin, 2003) by concept inventories (e.g., Jacobi,Martin, Mitchell, & Newell, 2003; Nottis, Prince, Vigeant, Nelson, & Hartsock, 2009). Students’ conceptual understanding has also been shown to vary by grade point average (Nottis et al., 2009). Inquiry-based activities (Nottis, Prince, & Vigeant, 2010) haveshown some success over traditional instructional methods (Tasoglu & Bakac, 2010) in altering misconceptions. The purpose of the current study was to determine whether undergraduate engineering students’ understanding of heat transfer concepts significantly changed after instruction with eight inquiry-based activities (Prince & Felder, 2007) supplementing instruction and whether students’ self reported GPA and prior knowledge, as measured by completion of specific engineering courses, affected these changes. The Heat and Energy Concept Inventory (Prince, Vigeant, & Nottis, 2010) was used to assess conceptual understanding. It was found that conceptual understanding significantly increased from pre- to post-test. It was also found that GPA had an effect on conceptual understanding of heat transfer; significant differences were found in post-test scores onthe concept inventory between GPA groups. However, there were mixed results when courses previously taken were analyzed. Future research should strive to analyze how prior knowledge effects conceptual understanding and aim to reduce the limitations of the current study such as, sampling method and methods of measuring GPA and priorknowledge.
Resumo:
Recently, we have demonstrated that considerable inherent sensitivity gains are attained in MAS NMR spectra acquired by nonuniform sampling (NUS) and introduced maximum entropy interpolation (MINT) processing that assures the linearity of transformation between the time and frequency domains. In this report, we examine the utility of the NUS/MINT approach in multidimensional datasets possessing high dynamic range, such as homonuclear C-13-C-13 correlation spectra. We demonstrate on model compounds and on 1-73-(U-C-13,N-15)/74-108-(U-N-15) E. coli thioredoxin reassembly, that with appropriately constructed 50 % NUS schedules inherent sensitivity gains of 1.7-2.1-fold are readily reached in such datasets. We show that both linearity and line width are retained under these experimental conditions throughout the entire dynamic range of the signals. Furthermore, we demonstrate that the reproducibility of the peak intensities is excellent in the NUS/MINT approach when experiments are repeated multiple times and identical experimental and processing conditions are employed. Finally, we discuss the principles for design and implementation of random exponentially biased NUS sampling schedules for homonuclear C-13-C-13 MAS correlation experiments that yield high-quality artifact-free datasets.