In this work we take advantage of eleven different sunspot group, sunspot, and active region databases to characterize the area and flux distributions of photospheric magnetic structures. We find that, when taken separately, different databases are better fitted by different distributions (as has been reported previously in the literature). However, we find that all our databases can be reconciled by the simple application of a proportionality constant, and that, in reality, different databases are sampling different parts of a composite distribution. This composite distribution is made up by linear combination of Weibull and log-normal distributions -where a pure Weibull (log-normal) characterizes the distribution of structures with fluxes below (above) 10 21 Mx (10 22 Mx). We propose that this is evidence of two separate mechanisms giving rise to visible structures on the photosphere: one directly connected to the global component of the dynamo (and the generation of bipolar active regions), and the other with the small-scale component of the dynamo (and the fragmentation of magnetic structures due to their interaction with turbulent convection). Additionally, we demonstrate that the Weibull distribution shows the expected linear behaviour of a power-law distribution (when extended into smaller fluxes), making our results compatible with the results of Parnell et al. (2009).

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Her research focuses what factors influence diverse students to choose engineering and stay in engineering through their careers and how different experiences within the practice and culture of engineering foster or hinder belongingness and identity development. Dr. Godwin graduated from Clemson University with a B.S. in Chemical Engineering and Ph.D. in Engineering and Science Education. Her research earned her a National Science Foundation CAREER Award focused on characterizing latent diversity, which includes diverse attitudes, mindsets, and approaches to learning, to understand engineering students' identity development. She is the recipient of a 2014 American Society for Engineering Education (ASEE) Educational Research and Methods Division Apprentice Faculty Grant. She has also been recognized for the synergy of research and teaching as an invited participant of the 2016 National Academy of Engineering Frontiers of Engineering Education Symposium and 2016 New Faculty Fellow for the Frontiers in Engineering Education Annual Conference. She also was an NSF Graduate Research Fellow for her work on female empowerment in engineering which won the National Association for Research in Science Teaching 2015 Outstanding Doctoral Research Award.

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In this work we introduce a new way of binning sunspot group data with the purpose of better understanding the impact of the solar cycle on sunspot properties and how this defined the characteristics of the extended minimum of cycle 23. Our approach assumes that the statistical properties of sunspots are completely determined by the strength of the underlying large-scale field and have no additional time dependencies. We use the amplitude of the cycle at any given moment (something we refer to as activity level) as a proxy for the strength of this deep-seated magnetic field.We find that the sunspot size distribution is composed of two populations: one population of groups and active regions and a second population of pores and ephemeral regions. When fits are performed at periods of different activity level, only the statistical properties of the former population, the active regions, is found to vary.Finally, we study the relative contribution of each component (small-scale versus large-scale) to solar magnetism. We find that when hemispheres are treated separately, almost every one of the past 12 solar minima reaches a point where the main contribution to magnetism comes from the small-scale component. However, due to asymmetries in cycle phase, this state is very rarely reached by both hemispheres at the same time. From this we infer that even though each hemisphere did reach the magnetic baseline, from a heliospheric point of view the minimum of cycle 23 was not as deep as it could have been.

This research paper describes the study of non-cognitive factors and their impact on student academic outcomes, above and beyond the impact from previous academic performance. The connection between prior academic performance factors, such as high school GPA and standardized test scores, and the performance of first year students (as measured by GPA) has been well established. While it has been shown that typically 20%-25% of the variation in first year student performance can be explained by a combination of high school GPA and standardized test scores, this still leaves over half of the variation unaccounted for. Some of this variation may be accounted for by a collection of non-cognitive factors.A non-cognitive inventory was created using the 10-Item Big Five Survey, the Short Grit Survey, and two subscales from the Motivated Strategies for Learning Questionnaire (Test Anxiety and Time and Study Environment). Data was collected using this survey from freshman through senior engineering students at a large, public research-intensive university in the Midwest. Using a hierarchical multiple regression, students' first year grades were regressed onto their previous academic performance as well as their scores in the non-cognitive inventory. Initial results indicate that the inclusion of non-cognitive factors alongside previous academic performance improved the predictability of students' first year GPA by an additional 7 percentage points compared to a model that only included previous performance. This paper also explores the variations in impact of non-cognitive factors on performance for different classroom settings. A series of multiple regressions illuminates distinct differences in the non-cognitive factors that most strongly affect academic performance in technical lecture, technical team, and liberal arts courses. Implications for student support in those different classroom contexts are described.

of the ASEE Virtual Community of Practice (VCP) for mechanics educators across the country. His current research focuses on student problem-solving processes and use of worked examples, change models and evidence-based teaching practices in engineering curricula, and the role of non-cognitive and affective factors in student academic outcomes and overall success.Mr. Gireesh Guruprasad, Purdue University, West Lafayette (College of Engineering)Gireesh Guruprasad is a graduate student at Purdue University. As part of his research, he explores factors that affect the Professional Formation of Engineers, based on students beliefs and preferences and the beliefs of the faculty who teach them. Gireesh obtained his Bachelors degree in Mechanical Engineering and is currently pursuing his Masters degree in Aeronautics and Astronautics Engineering.Mr. Ryan R. Senkpeil, Purdue University, West Lafayette (College of Engineering) Ryan Senkpeil is a Ph.D. student in Engineering Education at Purdue University who's research is focused on non-cognitive factors that impact engineering student performance and developing interventions to improve students' non-cognitive factors.c American Society for Engineering Education, 2017 Characterizing the alignment in faculty and student beliefs Abstract This research paper investigates faculty members' actions in a classroom setting in light of their personal beliefs about teaching and learning, and their relationships to student beliefs. The research question is: to what extent is alignment between faculty and student beliefs about teaching and learning related to faculty pedagogical activities and actions? Very little prior work integrates student-side and instructor-side preferences and actions, and this paper extends our understanding of this alignment. We expect that a clearer understanding of the alignment between faculty and students may help explain student academic performance. This paper focuses on characterizing the alignment, while our future research explores its relationship to student outcomes.Our data analysis reveals the following key insights about our research question. Faculty-student learning styles misalignment is largest along the active-reflective dimension of the ILS. In turn, faculty who are more misaligned with their students (in the ILS sense) tend to lecture more. In our data, faculty learning preferences and teaching preferences do not appear to be strongly correlated. Results suggest that faculty who are more instructor focused than average tend to use active and collaborative learning activities, and formative evaluation to a lesser extent. Conversely, faculty who are more student focused than average use lecture as a teaching tool to a lesser extent. IntroductionFaculty choices about how they teach in undergraduate engineering courses have important impacts on student learning. Past research has found that faculty's implicit beliefs and thoughts influence their behavior in class [1]-[3] . The strategies and actions faculty adopt to teach in class, it ...