Different Methods of Embodied Cognition in Pedagogy and its Effectiveness in Student Learning

The Mathematical Ideas Analysis hypothesizes that abstract mathematical reasoning is unconsciously organized and integrated with sensory-motor experience. Basic research testing movement, language, and perception during math problem solving supports this hypothesis. Applied research primarily measures students’ performance on math tests after they engage in analogous sensory-motor tasks, but findings show mixed results. Sensory-motor tasks are dependent on several moderators (e.g., instructional guidance, developmental stage) known to help students learn, and studies vary in how each moderator is implemented. There is little research on the effectiveness of sensory-motor tasks without these moderators. This study compares different approaches to working with an interactive application designed to emulate how people intrinsically solve algebraic equations. A total of 130 participants (84 females, 54 males) were drawn from a pool of Introductory Psychology students attending San Jose State University. Participants were placed in three different learning environments, and their performance was measured by comparing improvement between a pre-test and a post-test. We found no difference between participants who worked alone with the application, were instructed by the experimenter while using the application, or who instructed the experimenter on how to solve equations using the application. Further research is needed to examine how and whether analogous sensory-motor interfaces are a useful learning tool, and if so, what circumstances are ideal for sensory-motor interfaces to be used

Using dialogue to learn math in the LeActiveMath project

We describe a tutorial dialogue system under development that assists students in learning how to differentiate equations. The system uses deep natural language understanding and generation to both interpret students ’ utterances and automatically generate a response that is both mathematically correct and adapted pedagogically and linguistically to the local dialogue context. A domain reasoner provides the necessary knowledge about how students should approach math problems as well as their (in)correctness, while a dialogue manager directs pedagogical strategies and keeps track of what needs to be done to keep the dialogue moving along.

Cactus: Issues for Sustainable Simulation Software

The Cactus Framework is an open-source, modular, portable programming environment for the collaborative development and deployment of scientific applications using high-performance computing. Its roots reach back to 1996 at the National Center for Supercomputer Applications and the Albert Einstein Institute in Germany, where its development jumpstarted. Since then, the Cactus framework has witnessed major changes in hardware infrastructure as well as its own community. This paper describes its endurance through these past changes and, drawing upon lessons from its past, also discusses futureComment: submitted to the Workshop on Sustainable Software for Science: Practice and Experiences 201

Paraiso : An Automated Tuning Framework for Explicit Solvers of Partial Differential Equations

We propose Paraiso, a domain specific language embedded in functional programming language Haskell, for automated tuning of explicit solvers of partial differential equations (PDEs) on GPUs as well as multicore CPUs. In Paraiso, one can describe PDE solving algorithms succinctly using tensor equations notation. Hydrodynamic properties, interpolation methods and other building blocks are described in abstract, modular, re-usable and combinable forms, which lets us generate versatile solvers from little set of Paraiso source codes. We demonstrate Paraiso by implementing a compressive hydrodynamics solver. A single source code less than 500 lines can be used to generate solvers of arbitrary dimensions, for both multicore CPUs and GPUs. We demonstrate both manual annotation based tuning and evolutionary computing based automated tuning of the program.Comment: 52 pages, 14 figures, accepted for publications in Computational Science and Discover