The study has a twofold purpose. The first is to compare the priority order of the seven fundamental areas of skill among the three sample groups, The second is to compare the differences between actual and ideal teacher performance in the seven fundamental skill areas. The conclusions are generalizable only to the population of Texas teacher educators, members of the Texas Science Supervisors Association and members of the Science Teachers Association of Texas, All of the groups had basic agreement as to the priority order of the science teacher competencies being demonstrated by teachers, The profession's success in educating teachers in the content areas is reflected by the priority of the rankings. The position of the science supervisors' ratings of teacher performance between teachers and teacher educators indicated that supervisors have the most accurate view of teacher performance, The least discrepancy among groups occurred in the ideal rating of teacher performance, indicating general agreement as to the level at which teachers should be demonstrating skills, The greatest discrepancy occurred in the identification of actual level of teacher performance.

Advances in machine condition monitoring technologies are driven by the rise in complexity of modern machines and the increased demand for product reliability. Condition monitoring research tends to focus on the development of signal processing algorithms that are sensitive to machine faults, robust under time-varying operating conditions, and informative regarding the nature and extent of machine faults. A significant challenge remains for monitoring the condition of machines that are subject to time-varying operating conditions. The here presented work is concerned with the development of cost effective condition monitoring algorithms. It is investigated how empirical models (including probability density distributions and regression functions) may be used to extract diagnostic information from machine response signals that have been generated under fluctuating operating conditions. The proposed methodology is investigated on a number of case studies, including gearboxes, alternator end windings, and haul roads. It is shown how empirical models for machine condition monitoring may generally be implemented according to one of two basic approaches. The two approaches are referred to as discrepancy analysis and waveform reconstruction. Discrepancy analysis is concerned with the comparison of a novel signal to a reference model. The reference model is sufficiently expressive to represent vibration response as measured on a healthy machine over a range of operating conditions. The novel signal is compared to the reference model in such a manner that a discrepancy signal transform is obtained. A discrepancy signal is sensitive to faults, robust to time-varying operating conditions, and inherently simple. As such it may further beWaveform reconstruction implements a regression function to model machine response as a function of different state space variables. The regression function may subsequently be exploited to extract diagnostic information. The machine response may for instance be reconstructed at a specified steady state operating condition. This renders the signal wide-sense stationary so that Fourier analysis may be applied. analysed in order to extract periodicities and magnitudes as diagnostic markers. / Dissertation (MEng)--University of Pretoria, 2013. / gm2014 / Electrical, Electronic and Computer Engineering / unrestricted