Student activity levels, lesson context, and teacher behavior during middle school physical education

There is little research on students' engagement in physical activity in middle school physical education (PE). We observed student activity, lesson context, and teacher behavior in 430 PE lessons taught by 126 teachers in 24 schools. Variables were analyzed by mixed-model nested analyses of covariance. Boys were more active than girls overall and during skill drills, game play, and free play. Student activity varied by lesson context, with fitness activities producing the most activity. Class size was negatively associated with student activity. Daily PE contributed a weekly total of 25 min of vigorous activity and 83 min of moderate-to-vigorous activity--much less than national objectives. Results suggest that numerous opportunities exist for increasing student physical activity during middle school PE.     

Exploring the ethical implications of MOOCs

Massive open online courses (MOOCs) represent a potentially exciting opportunity to use technology to realise many of the long-promised benefits of universal higher education. While there are many positive aspects to the MOOCs on offer and in development, there are also significant ethical concerns arising from various initiatives. These include the academic duties of care and integrity, commercial exploitation of learners, as well as research ethics concerns arising from the analytical and other work being done by academics and institutions. These issues are explored and a set of heuristics for individuals and institutions provided as a starting point for ethical analysis of MOOCs and associated activities.     

Novel model reference adaptive control laws for improved transient dynamics and guaranteed saturation constraints

In classical model reference adaptive control (MRAC), the adaptive rates must be tuned to meet multiple competing objectives. Large adaptive rates guarantee rapid convergence of the trajectory tracking error to zero. However, large adaptive rates may also induce saturation of the actuators and excessive overshoots of the closed-loop system’s trajectory tracking error. Conversely, low adaptive rates may produce unsatisfactory trajectory tracking performances. To overcome these limitations, in the classical MRAC framework, the adaptive rates must be tuned through an iterative process. Alternative approaches require to modify the plant’s reference model or the reference command input. This paper presents the first MRAC laws for nonlinear dynamical systems affected by matched and parametric uncertainties that constrain both the closed-loop system’s trajectory tracking error and the control input at all times within user-defined bounds, and enforce a user-defined rate of convergence on the trajectory tracking error. By applying the proposed MRAC laws, the adaptive rates can be set arbitrarily large and both the plant’s reference model and the reference command input can be chosen arbitrarily. The user-defined rate of convergence of the closed-loop plant’s trajectory is enforced by introducing a user-defined auxiliary reference model, which converges to the trajectory tracking error obtained by applying the classical MRAC laws before its transient dynamics has decayed, and steering the trajectory tracking error to the auxiliary reference model at a rate of convergence that is higher than the rate of convergence of the plant’s reference model. The ability of the proposed MRAC laws to prescribe the performance of the closed-loop system’s trajectory tracking error and control input is guaranteed by barrier Lyapunov functions. Numerical simulations illustrate both the applicability of our theoretical results and their effectiveness compared to other techniques such as prescribed performance control, which allows to constrain both the rate of convergence and the maximum overshoot on the trajectory tracking error of uncertain systems.