User manual for the Earth observations Division R and D to OLPARS dot data conversion

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Skill set profile clustering based on student capability vectors computed from online tutoring data

In educational research, a fundamental goal is identifying which skills students have mastered, which skills they have not, and which skills they are in the process of mastering. As the number of examinees, items, and skills increases, the estimation of even simple cognitive diagnosis models becomes difficult. To address this, we introduce a capability matrix showing for each skill the proportion correct on all items tried by each student involving that skill. We apply variations of common clustering methods to this matrix and discuss conditioning on sparse subspaces. We demonstrate the feasibility and scalability of our method on several simulated datasets and illustrate the difficulties inherent in real data using a subset of online mathematics tutor data. We also comment on the interpretability and application of the results for teachers

Skill set profile clustering: the empty K-means algorithm with automatic specification of starting cluster centers

While students’ skill set profiles can be estimated with formal cognitive diagnosis models [8], their computational complexity makes simpler proxy skill estimates attractive [1, 4, 6]. These estimates can be clustered to generate groups of similar students. Often hierarchical agglomerative clustering or k-means clustering is utilized, requiring, for K skills, the specification of 2^K clusters. The number of skill set profiles/clusters can quickly become computationally intractable. Moreover, not all profiles may be present in the population. We present a flexible version of k-means that allows for empty clusters. We also specify a method to determine efficient starting centers based on the Q-matrix. Combining the two substantially improves the clustering results and allows for analysis of data sets previously thought impossible

Resolution Study

Resolution effects on cartographic data using conventional stereoplotters with photographs taken at orbital height

Could There Be A Hole In Type Ia Supernovae?

In the favored progenitor scenario, Type Ia supernovae arise from a white dwarf accreting material from a non-degenerate companion star. Soon after the white dwarf explodes, the ejected supernova material engulfs the companion star; two-dimensional hydrodynamical simulations by Marietta et. al. show that, in the interaction, the companion star carves out a conical hole of opening angle 30-40 degrees in the supernova ejecta. In this paper we use multi-dimensional Monte Carlo radiative transfer calculations to explore the observable consequences of an ejecta-hole asymmetry. We calculate the variation of the spectrum, luminosity, and polarization with viewing angle for the aspherical supernova near maximum light. We find that the supernova looks normal from almost all viewing angles except when one looks almost directly down the hole. In the latter case, one sees into the deeper, hotter layers of ejecta. The supernova is relatively brighter and has a peculiar spectrum characterized by more highly ionized species, weaker absorption features, and lower absorption velocities. The spectrum viewed down the hole is comparable to the class of SN 1991T-like supernovae. We consider how the ejecta-hole asymmetry may explain the current spectropolarimetric observations of SNe Ia, and suggest a few observational signatures of the geometry. Finally, we discuss the variety currently seen in observed SNe Ia and how an ejecta-hole asymmetry may fit in as one of several possible sources of diversity.Comment: 11 pages, 9 figures, submitted to Ap

Occupational expectations, future aspirations, and adaptation to formal education at an off-reservation boarding school for Indian high school students of the Northern Plains region

Little research has been done on the attitudes of American Indian groups toward occupation, education, or occupational mobility. Although anthropologists have studied the American Indian since the early 1940\u27s, they have tended to focus upon tribal groupings studying each as an isolated people bearing the survivals of an aboriginal culture

Asteroid family identification using the Hierarchical Clustering Method and WISE/NEOWISE physical properties

Using albedos from WISE/NEOWISE to separate distinct albedo groups within the Main Belt asteroids, we apply the Hierarchical Clustering Method to these subpopulations and identify dynamically associated clusters of asteroids. While this survey is limited to the ~35% of known Main Belt asteroids that were detected by NEOWISE, we present the families linked from these objects as higher confidence associations than can be obtained from dynamical linking alone. We find that over one-third of the observed population of the Main Belt is represented in the high-confidence cores of dynamical families. The albedo distribution of family members differs significantly from the albedo distribution of background objects in the same region of the Main Belt, however interpretation of this effect is complicated by the incomplete identification of lower-confidence family members. In total we link 38,298 asteroids into 76 distinct families. This work represents a critical step necessary to debias the albedo and size distributions of asteroids in the Main Belt and understand the formation and history of small bodies in our Solar system.Comment: Accepted to ApJ. Full version of Table 3 to be published electronically in Ap