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Computational thinking can be deemed as thinking in algorithmic way, with which one can transpose given problems into computer algorithms. Since computational thinking requires abstract reasoning, it should not depend on particular programming languages. Unfortunately, introductory programming courses (CS1) often give students false impression that their goals are to teach a particular programming language. This study shares the design of new pedagogy for CS1 that removes dependency on a particular language and promotes computational thinking by teaching multiple programming languages simultaneously. Specifically, chosen programming languages range from low-level to high-level to expose students to different levels of abstraction from the details of computer architecture. Initial student survey responses from both trial and control groups show that there are significant improvements for the trial groups. 

In uncertainty quantification, it is commonly required to solve a forward model consisting of a partial differential equation (PDE) with a spatially varying uncertain coefficient that is represented as an affine function of a set of random variables, or parameters. Discretizing such models using stochastic Galerkin finite element methods (SGFEMs) leads to very high-dimensional discrete problems that can be cast as linear multi-term matrix equations (LMTMEs). We develop efficient computational methods for approximating solutions of such matrix equations in low rank. To do this, we follow an alternating energy minimization (AEM) framework, wherein the solution is represented as a product of two matrices, and approximations to each component are sought by solving certain minimization problems repeatedly. Inspired by proper generalized decomposition methods, the iterative solution algorithms we present are based on a rank-adaptive variant of AEM methods that successively computes a rank-one solution component at each step. We introduce and evaluate new enhancement procedures to improve the accuracy of the approximations these algorithms deliver. The efficiency and accuracy of the enhanced AEM methods is demonstrated through numerical experiments with LMTMEs associated with SGFEM discretizations of parameterized linear elliptic PDEs.