Linking engagement and performance: The social network analysis perspective

Theories developed by Tinto and Nora identify academic performance, learning gains, and involvement in learning communities as significant facets of student engagement that, in turn, support student persistence. Collaborative learning environments, such as those employed in the Modeling Instruction introductory physics course, provide structure for student engagement by encouraging peer-to-peer interactions. Because of the inherently social nature of collaborative learning, we examine student interactions in the classroom using network analysis. We use centrality---a family of measures that quantify how connected or "central" a particular student is within the classroom network---to study student engagement longitudinally. Bootstrapped linear regression modeling shows that students' centrality predicts future academic performance over and above prior GPA for three out of four centrality measures tested. In particular, we find that closeness centrality explains 28 % more of the variance than prior GPA alone. These results confirm that student engagement in the classroom is critical to supporting academic performance. Furthermore, we find that this relationship for social interactions does not emerge until the second half of the semester, suggesting that classroom community develops over time in a meaningful way

What\u27s Love Got to Do with It? An Exploration of the Symposium and Plato\u27s Love

To many people love is special, sacred even. Love plays a countless number of roles for a countless number of people. Contemporary ideas about love, however, are more in alignment with the philosophies of Aristotle, and not of Plato. Aristotle held that love could exist as many people see it today – wishing well for others purely for their own sake. But Plato disagreed. Plato claimed that love was a way by which one could better themselves and become wiser. In this thesis, I explain Plato’s theory of love put forth in the Symposium. I also explore the textual evidence for the selfish nature of Plato’s love

Interpreting Spectral Energy Distributions from Young Stellar Objects. I. A grid of 200,000 YSO model SEDs

We present a grid of radiation transfer models of axisymmetric young stellar objects (YSOs), covering a wide range of stellar masses (from 0.1Msun to 50Msun) and evolutionary stages (from the early envelope infall stage to the late disk-only stage). The grid consists of 20,000 YSO models, with spectral energy distributions (SEDs) and polarization spectra computed at ten viewing angles for each model, resulting in a total of 200,000 SEDs. [...]. These models are publicly available on a dedicated WWW server: http://www.astro.wisc.edu/protostars/ . In this paper we summarize the main features of our models, as well as the range of parameters explored. [...]. We examine the dependence of the spectral indices of the model SEDs on envelope accretion rate and disk mass. In addition, we show variations of spectral indices with stellar temperature, disk inner radius, and disk flaring power for a subset of disk-only models. We also examine how changing the wavelength range of data used to calculate spectral indices affects their values. We show sample color-color plots of the entire grid as well as simulated clusters at various distances with typical {\it Spitzer Space Telescope} sensitivities. We find that young embedded sources generally occupy a large region of color-color space due to inclination and stellar temperature effects. Disk sources occupy a smaller region of color-color space, but overlap substantially with the region occupied by embedded sources, especially in the near- and mid-IR. We identify regions in color-color space where our models indicate that only sources at a given evolutionary stage should lie. [...].Comment: 69 pages, 28 figures, Accepted for publication in ApJS. Preprint with full resolution figures available at http://www.astro.wisc.edu/protostars

Practitioner’s guide to social network analysis: Examining physics anxiety in an active-learning setting

The application of social network analysis (SNA) has recently grown prevalent in science, technology, engineering, and mathematics education research. Research on classroom networks has led to greater understandings of student persistence in physics majors, changes in their career-related beliefs (e.g., physics interest), and their academic success. In this paper, we aim to provide a practitioner’s guide to carrying out research using SNA, including how to develop data collection instruments, setup protocols for gathering data, as well as identify network methodologies relevant to a wide range of research questions beyond what one might find in a typical primer. We illustrate these techniques using student anxiety data from active-learning physics classrooms. We explore the relationship between students’ physics anxiety and the social networks they participate in throughout the course of a semester. We find that students’ with greater numbers of outgoing interactions are more likely to experience decrease in anxiety even while we control for pre-anxiety, gender, and final course grade. We also explore the evolution of student networks and find that the second half of the semester is a critical period for participating in interactions associated with decreased physics anxiety. Our study further supports the benefits of dynamic group formation strategies that give students an opportunity to interact with as many peers as possible throughout a semester. To complement our guide to SNA in education research, we also provide a set of tools for other researchers to use this approach in their work—the SNA toolbox—that can be accessed on GitHub

2-D and 3-D Radiation Transfer Models of High-Mass Star Formation

2-D and 3-D radiation transfer models of forming stars generally produce bluer 1-10 micron colors than 1-D models of the same evolutionary state and envelope mass. Therefore, 1-D models of the shortwave radiation will generally estimate a lower envelope mass and later evolutionary state than multidimensional models. 1-D models are probably reasonable for very young sources, or longwave analysis (wavelengths > 100 microns). In our 3-D models of high-mass stars in clumpy molecular clouds, we find no correlation between the depth of the 10 micron silicate feature and the longwave (> 100 micron) SED (which sets the envelope mass), even when the average optical extinction of the envelope is >100 magnitudes. This is in agreement with the observations of Faison et al. (1998) of several UltraCompact HII (UCHII) regions, suggesting that many of these sources are more evolved than embedded protostars. We have calculated a large grid of 2-D models and find substantial overlap between different evolutionary states in the mid-IR color-color diagrams. We have developed a model fitter to work in conjunction with the grid to analyze large datasets. This grid and fitter will be expanded and tested in 2005 and released to the public in 2006.Comment: 10 pages, 8 figures, to appear in the proceedings of IAU Symp 227, Massive Star Birth: A Crossroads of Astrophysics, (Cesaroni R., Churchwell E., Felli M., Walmsley C. editors

Three photometric methods tested on ground-based data of Q 2237+0305

The Einstein Cross, Q~2237+0305, has been photometrically observed in four bands on two successive nights at NOT (La Palma, Spain) in October 1995. Three independent algorithms have been used to analyse the data: an automatic image decomposition technique, a CLEAN algorithm and the new MCS deconvolution code. The photometric and astrometric results obtained with the three methods are presented. No photometric variations were found in the four quasar images. Comparison of the photometry from the three techniques shows that both systematic and random errors affect each method. When the seeing is worse than 1.0", the errors from the automatic image decomposition technique and the Clean algorithm tend to be large (0.04-0.1 magnitudes) while the deconvolution code still gives accurate results (1{sigma} error below 0.04) even for frames with seeing as bad as 1.7". Reddening is observed in the quasar images and is found to be compatible with either extinction from the lensing galaxy or colour dependent microlensing. The photometric accuracy depends on the light distribution used to model the lensing galaxy. In particular, using a numerical galaxy model, as done with the MCS algorithm, makes the method less seeing dependent. Another advantage of using a numerical model is that eventual non-homogeneous structures in the galaxy can be modeled. Finally, we propose an observational strategy for a future photometric monitoring of the Einstein Cross.Comment: 9 pages, accepted for publication in A&

Rhapso : automatic stitching of mass segments from fourier transform ion cyclotron resonance mass spectra

Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) provides the resolution and mass accuracy needed to analyze complex mixtures such as crude oil. When mixtures contain many different components, a competitive effect within the ICR cell takes place that hampers the detection of a potentially large fraction of the components. Recently, a new data collection technique, which consists of acquiring several spectra of small mass ranges and assembling a complete spectrum afterward, enabled the observation of a record number of peaks with greater accuracy compared to broadband methods. There is a need for statistical methods to combine and preprocess segmented acquisition data. A particular challenge of quadrupole isolation is that near the window edges there is a drop in intensity, hampering the stitching of consecutive windows. We developed an algorithm called Rhapso to stitch peak lists corresponding to multiple different m/z regions from crude oil samples. Rhapso corrects potential edge effects to enable the use of smaller windows and reduce the required overlap between windows, corrects mass shifts between windows, and generates a single peak list for the full spectrum. Relative to a stitching performed manually, Rhapso increased the data processing speed and avoided potential human errors, simplifying the subsequent chemical analysis of the sample. Relative to a broadband spectrum, the stitched output showed an over 2-fold increase in assigned peaks and reduced mass error by a factor of 2. Rhapso is expected to enable routine use of this spectral stitching method for ultracomplex samples, giving a more detailed characterization of existing samples and enabling the characterization of samples that were previously too complex to analyze