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Open Access Publications from the University of California

Scholarly Works (7 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2019)

Improvement science is a promising framework for school efforts at improving classroom instruction. However, there is scant documentation on actual attempts to apply improvement science principles to better K-12 teaching practices. This research attempts to fill that gap by reporting the results of a two-year improvement science professional development effort undertaken by a five-school network, explicitly focusing on secondary math teaching practices. Through interviews with nine of 29 participating teachers, as well as the facilitators, there was clear consensus about which improvement science principles did and did not support the network’s learning efforts. Observations were conducted over the two-year period. Document analysis included the Plan-Do-Study-Act (PDSA) logs of the participating teachers, as well the network’s measures for improvement. Teachers reported positively on several improvement science principles. These include keeping their work problem-specific and user-centered and engaging disciplined inquiry. Additionally, teachers reported value in the distinct improvement science principle of framing their efforts in terms of systems thinking and the concept of using measures for improvement to collect data on the ability of their efforts to impact important drivers of effective classroom instruction. However, both facilitators and teachers expressed concern about the ability to create accurate, responsive, and common measures for improvement in order to inform their decision-making. Additionally, the teachers reported intense time/bandwidth concerns about gathering and using measures for improvement to inform changes in their work processes. Finally, facilitators allowed teachers to change their driver of focus and to use qualitative, rather than quantitative, data to inform their PDSA cycles. Teachers appreciated this autonomy, but this decision hindered the network’s ability to test a common hypothesis informed by data on a shared measure. These findings have important implications for any educational organization attempting to use improvement science principles, particularly measures for improvement, in an effort to reform classroom instructional practices.

    Cover page: Improving Instruction: An Examination of a Network Improvement Science Effort to Support Instructional Change
    • Article
    • Peer Reviewed
    UC Berkeley Previously Published Works (2016)
    The emergence of "big data" offers unprecedented opportunities for not only accelerating scientific advances but also enabling new modes of discovery. Scientific progress in many disciplines is increasingly enabled by our ability to examine natural phenomena through the computational lens, i.e., using algorithmic or information processing abstractions of the underlying processes; and our ability to acquire, share, integrate and analyze disparate types of data. However, there is a huge gap between our ability to acquire, store, and process data and our ability to make effective use of the data to advance discovery. Despite successful automation of routine aspects of data management and analytics, most elements of the scientific process currently require considerable human expertise and effort. Accelerating science to keep pace with the rate of data acquisition and data processing calls for the development of algorithmic or information processing abstractions, coupled with formal methods and tools for modeling and simulation of natural processes as well as major innovations in cognitive tools for scientists, i.e., computational tools that leverage and extend the reach of human intellect, and partner with humans on a broad range of tasks in scientific discovery (e.g., identifying, prioritizing formulating questions, designing, prioritizing and executing experiments designed to answer a chosen question, drawing inferences and evaluating the results, and formulating new questions, in a closed-loop fashion). This calls for concerted research agenda aimed at: Development, analysis, integration, sharing, and simulation of algorithmic or information processing abstractions of natural processes, coupled with formal methods and tools for their analyses and simulation; Innovations in cognitive tools that augment and extend human intellect and partner with humans in all aspects of science.
      Cover page: Accelerating Science: A Computing Research Agenda
      • Article
      • Peer Reviewed
      UC Davis Previously Published Works (2015)
        Cover page: gem5-gpu: A Heterogeneous CPU-GPU Simulator
        • Article
        • Peer Reviewed
        UC Davis Previously Published Works (2015)
        Analytic database workloads are growing in data size and query complexity. At the same time, computer architects are struggling to continue the meteoric increase in performance enabled by Moore's Law. We explore the impact of two emerging architectural trends which may help continue the Moore's Law performance trend for analytic database workloads, namely 3D die-stacking and tight accelerator-CPU integration, specifically GPUs. GPUs have evolved from fixed-function units, to programmable discrete chips, and now are integrated with CPUs in most manufactured chips. Past efforts to use GPUs for analytic query processing have not had widespread practical impact, but it is time to re-examine and re-optimize database algorithms for massively data-parallel architectures. We argue that high-throughput data-parallel accelerators are likely to play a big role in future systems as they can be easily exploited by database systems and are becoming ubiquitous. Using the simple scan primitive as an example, we create a starting point for this discussion. We project the performance of both CPUs and GPUs in emerging 3D systems and show that the high-throughput data-parallel architecture of GPUs is more efficient in these future systems. We show that if database designers embrace emerging 3D architectures, there is possibly an order of magnitude performance and energy efficiency gain.
          Cover page: Implications of Emerging 3D GPU Architecture on the Scan Primitive
          • Article
          • Peer Reviewed
          UC Berkeley Previously Published Works (2017)
            • Article
            • Peer Reviewed
            UC Berkeley Previously Published Works (2016)
            Because most technology and computer architecture innovations were (intentionally) invisible to higher layers, application and other software developers could reap the benefits of this progress without engaging in it. Higher performance has both made more computationally demanding applications feasible (e.g., virtual assistants, computer vision) and made less demanding applications easier to develop by enabling higher-level programming abstractions (e.g., scripting languages and reusable components). Improvements in computer system cost-effectiveness enabled value creation that could never have been imagined by the field's founders (e.g., distributed web search sufficiently inexpensive so as to be covered by advertising links). The wide benefits of computer performance growth are clear. Recently, Danowitz et al. apportioned computer performance growth roughly equally between technology and architecture, with architecture credited with ~80x improvement since 1985. As semiconductor technology approaches its "end-of-the-road" (see below), computer architecture will need to play an increasing role in enabling future ICT innovation. But instead of asking, "How can I make my chip run faster?," architects must now ask, "How can I enable the 21st century infrastructure, from sensors to clouds, adding value from performance to privacy, but without the benefit of near-perfect technology scaling?". The challenges are many, but with appropriate investment, opportunities abound. Underlying these opportunities is a common theme that future architecture innovations will require the engagement of and investments from innovators in other ICT layers.
              Cover page: 21st Century Computer Architecture
              • Article
              • Peer Reviewed
              UC Davis Previously Published Works (2019)
              The hardware and software foundations laid in the first half of the 20th Century enabled the computing technologies that have transformed the world, but these foundations are now under siege. The current computing paradigm, which is the foundation of much of the current standards of living that we now enjoy, faces fundamental limitations that are evident from several perspectives. In terms of hardware, devices have become so small that we are struggling to eliminate the effects of thermodynamic fluctuations, which are unavoidable at the nanometer scale. In terms of software, our ability to imagine and program effective computational abstractions and implementations are clearly challenged in complex domains. In terms of systems, currently five percent of the power generated in the US is used to run computing systems - this astonishing figure is neither ecologically sustainable nor economically scalable. Economically, the cost of building next-generation semiconductor fabrication plants has soared past $10 billion. All of these difficulties - device scaling, software complexity, adaptability, energy consumption, and fabrication economics - indicate that the current computing paradigm has matured and that continued improvements along this path will be limited. If technological progress is to continue and corresponding social and economic benefits are to continue to accrue, computing must become much more capable, energy efficient, and affordable. We propose that progress in computing can continue under a united, physically grounded, computational paradigm centered on thermodynamics. Herein we propose a research agenda to extend these thermodynamic foundations into complex, non-equilibrium, self-organizing systems and apply them holistically to future computing systems that will harness nature's innate computational capacity. We call this type of computing "Thermodynamic Computing" or TC.
                Cover page: Thermodynamic Computing
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