Instructional Design in Online Learning: Components of Quality

Although there are obvious differences between online instruction and face-to-face instruction, this paper focuses on their similarities. One of the challenges when designing a course that has been successfully taught in a face-to-face format is deciding what will stay the same versus what will be changed. How does one replace what happens in class with meaningful online content? In what ways can content be presented aside from reading text on one\u27s computer screen? With these questions in mind, an instructor began collaborating with an instructional designer to develop her first online course, a graduate level course in pupil assessment and evaluation. This paper describes the structure and components of that course. The instructor and instructional designer worked together to infuse three principles of instruction: a) developing a community of learners, (b) promoting critical thinking, and (c) defining clear expectations. Data from course evaluations indicated that overall, students perceived themselves as part of a community of learners, engaged in critical thinking, and found the course expectations to be clear. Applying the same principles of learning from a face-to-face course in an online course seems to have resulted in a successful course, at least from the students\u27 perspective. The major problem identified is common to both face-to-face and online formats -- balancing the demands of the student workload in this challenging course with the expectations and life realities of students who maintain full time jobs and active family commitments

An Intrinsic Description of the Nonlinear Aeroelasticity of Very Flexible Wings

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90662/1/AIAA-2011-1917-972.pd

Smartlocks

As multicore processors become increasingly prevalent, system complexity is skyrocketing. The advent of the asymmetric multicore compounds this - it is no longer practical for an average programmer to balance the system constraints associated with today's multicores and worry about new problems like asymmetric partitioning and thread interference. Adaptive, or self-aware, computing has been proposed as one method to help application and system programmers confront this complexity. These systems take some of the burden off of programmers by monitoring themselves and optimizing or adapting to meet their goals. This paper introduces a self-aware synchronization library for multicores and asymmetric multicores called Smartlocks. Smartlocks is a spin-lock library that adapts its internal implementation during execution using heuristics and machine learning to optimize toward a user-defined goal, which may relate to performance or problem-specific criteria. Smartlocks builds upon adaptation techniques from prior work like reactive locks [1], but introduces a novel form of adaptation that we term lock acquisition scheduling designed specifically to address asymmetries in multicores. Lock acquisition scheduling is optimizing which waiter will get the lock next for the best long-term effect when multiple threads (or processes) are spinning for a lock. This work demonstrates that lock scheduling is important for addressing asymmetries in multicores. We study scenarios where core speeds vary both dynamically and intrinsically under thermal throttling and manufacturing variability, respectively, and we show that Smartlocks significantly outperforms conventional spin-locks and reactive locks. Based on our findings, we provide guidelines for application scenarios where Smartlocks works best versus less optimally

Application Heartbeats. A Generic Interface for Specifying Program Performance and Goals in Autonomous Computing Environments

The rise of multicore computing has greatly increased sys-tem complexity and created an additional burden for soft-ware developers. This burden is especially troublesome when it comes to optimizing software on modern computing sys-tems. Autonomic or adaptive computing has been proposed as one method to help application programmers handle this complexity. In an autonomic computing environment, sys-tem services monitor applications and automatically adapt their behavior to increase the performance of the applica-tions they support. Unfortunately, applications often run as performance black-boxes and adaptive services must infer application performance from low-level information or rely on system-specific ad hoc methods. This paper proposes a standard framework, Application Heartbeats, which ap

Enabling technologies for self-aware adaptive systems

Self-aware computer systems will be capable of adapting their behavior and resources thousands of times a second to automatically find the best way to accomplish a given goal despite changing environmental conditions and demands. Such a capability benefits a broad spectrum of computer systems from embedded systems to supercomputers and is particularly useful for meeting power, performance, and resource-metering challenges in mobile computing, cloud computing, multicore computing, adaptive and dynamic compilation environments, and parallel operating systems. Some of the challenges in implementing self-aware systems are a) knowing within the system what the goals of applications are and if they are meeting them, b) deciding what actions to take to help applications meet their goals, and c) developing standard techniques that generalize and can be applied to a broad range of self-aware systems. This work presents our vision for self-aware adaptive systems and proposes enabling technologies to address these three challenges. We describe a framework called Application Heartbeats that provides a general, standardized way for applications to monitor their performance and make that information available to external observers. Then, through a study of a self-optimizing synchronization library called Smartlocks, we demonstrate a powerful technique that systems can use to determine which optimization actions to take. We show that Heartbeats can be applied naturally in the context of reinforcement learning optimization strategies as a reward signal and that, using such a strategy, Smartlocks are able to significantly improve performance of applications on an important emerging class of multicore systems called asymmetric multicores.Roberto Rocca Foundatio