Understanding Student Computational Thinking with Computational Modeling

Recently, the National Research Council's framework for next generation science standards highlighted "computational thinking" as one of its "fundamental practices". 9th Grade students taking a physics course that employed the Modeling Instruction curriculum were taught to construct computational models of physical systems. Student computational thinking was assessed using a proctored programming assignment, written essay, and a series of think-aloud interviews, where the students produced and discussed a computational model of a baseball in motion via a high-level programming environment (VPython). Roughly a third of the students in the study were successful in completing the programming assignment. Student success on this assessment was tied to how students synthesized their knowledge of physics and computation. On the essay and interview assessments, students displayed unique views of the relationship between force and motion; those who spoke of this relationship in causal (rather than observational) terms tended to have more success in the programming exercise.Comment: preprint to submit to PERC proceedings 201

Computational modeling of microstructure

Many materials such as martensitic or ferromagnetic crystals are observed to be in metastable states exhibiting a fine-scale, structured spatial oscillation called microstructure; and hysteresis is observed as the temperature, boundary forces, or external magnetic field changes. We have developed a numerical analysis of microstructure and used this theory to construct numerical methods that have been used to compute approximations to the deformation of crystals with microstructure

A Neural Model for Compositional Word Embeddings and Sentence Processing

We propose a new neural model for word embeddings, which uses Unitary Matrices as the primary device for encoding lexical information. It uses simple matrix multiplication to derive matrices for large units, yielding a sentence processing model that is strictly compositional, does not lose information over time steps, and is transparent, in the sense that word embed- dings can be analysed regardless of context. This model does not employ activation functions, and so the network is fully accessible to analysis by the methods of linear algebra at each point in its operation on an input sequence. We test it in two NLP agreement tasks and obtain rule like perfect accuracy, with greater stability than current state-of-the-art systems. Our proposed model goes some way towards offer- ing a class of computationally powerful deep learning systems that can be fully understood and compared to human cognitive processes for natural language learning and representation

FNAS computational modeling

Numerical calculations of the electronic properties of liquid II-VI semiconductors, particularly CdTe and ZnTe were performed. The measured conductivity of these liquid alloys were modeled by assuming that the dominant temperature effect is the increase in the number of dangling bonds with increasing temperature. For low to moderate values of electron correlation, the calculated conductivity as a function of dangling bond concentration closely follows the measured conductivity as a function of temperature. Both the temperature dependence of the chemical potential and the thermal smearing in region of the Fermi surface have a large effect on calculated values of conductivity

Computational modeling of hydrogel cross‐linking based on reaction‐diffusion theory

Alginate-based hydrogel is widely used as bio-ink in 3D bioprinting. For producing the bio-ink and stabilizing the polymer network, the hydrogel shall undergo a gelation process which can be obtained by adding an ionic cross-linker agent, such as Calcium ions for alginate. The diffusion of the crosslinker in the alginate stabilizes the polymeric network thanks to the reaction of Calcium ions with alginate monomers. This work presents a reaction-diffusion computational model of the gelation mechanism in alginate hydrogels. The coupled chemical system is solved using finite element discretizations considering the inhomogeneous evolution of the gelation process in time and space