School Principals' Attitudes Towards Performance Based Compensation

The main objective of this study is to determine the attitudes of school principals regarding a performance based compensation system. This study identifies the attitudes towards specific factors that should be considered in the implementation of a system of performance based compensation. The data have been analyzed to determine if a principal's demographic characteristics affect his/her level of agreement with performance based compensation and the factors for implementation. In addition, this study unveils areas of concern that principals have conveyed regarding the implementation of a performance based compensation system. Data was obtained from 444 public school principals representing 444 schools and 178 districts in the state of Colorado. Measures used in the treatment of the data include descriptive statistics and one-way ANOVA. The major findings of this study were: 1. 82.4% of respondents believe that teachers, principals and administrators should be included in performance based compensation (PBC). 2. The top two indicators that respondents believed should be included in a PBC system are student achievement (88.5%) and teacher evaluations (77.6%) 3. The 3 largest obstacles to PBC that respondents identified are: a. The capacity to link student achievement to teacher evaluations (82.9%) b. Teacher Union Resistance (67.1%) c. Cost (55.9%) 4. Principals in urban, rural and suburban geographic groups disagree about the effects of performance based compensation. 5. The top 5 overall concerns regarding Performance Based Compensation were: a. Concerns regarding effectively using assessment to measure performance of all teachers/equity between teachers b. Concerns regarding evaluation (time for principals to learn, consistency from school to school, time for principals to evaluate, quality of evaluation tool). c. Not in favor of PBC due to philosophical views or concerns about lack of research. d. Concerns regarding the equity between classrooms and districts across the state due to poverty levels and unequal resources. e. Concerns that performance based compensation will result in a decline in teacher collaboration and an increase in competition between teachers. Based upon these findings, the researcher concluded that there is not a strong general acceptance of performance based compensation systems. However, urban principals in Colorado tend to view PBC somewhat more favorably than do principals in suburban or rural areas. Most importantly, systems to link student achievement to teacher evaluation must be collaboratively created to ensure PBC systems are equitable, consistent and fair.

Towards Closed-loop Deep Brain Stimulation: Behavior Recognition from Human STN

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson’s disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient’s behavior. Thus behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. This thesis proposes novel detection and classification techniques for behavior recognition based on deep brain LFP. Behavior detection from such signals is the vital step in developing the next generation of closed-loop DBS devices. LFP recordings from 13 subjects are utilized in this study to design and evaluate our method. Recordings were performed during the surgery and the subjects were asked to perform various behavioral tasks. Various techniques are used understand how the behaviors modulate the STN. One method studies the time-frequency patterns in the STN LFP during the tasks. Another method measures the temporal inter-hemispheric connectivity of the STN as well as the connectivity between STN and Pre-frontal Cortex (PFC). Experimental results demonstrate that different behaviors create different m odulation patterns in STN and it’s connectivity. We use these patterns as features to classify behaviors. A method for single trial recognition of the patient’s current task is proposed. This method uses wavelet coefficients as features and support vector machine (SVM) as the classifier for recognition of a selection of behaviors: speech, motor, and random. The proposed method is 82.4% accurate for the binary classification and 73.2% for classifying three tasks. As the next step, a practical behavior detection method which asynchronously detects behaviors is proposed. This method does not use any priori knowledge of behavior onsets and is capable of asynchronously detect the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity and to detect the finger movements. Our experimental results using STN LFP recorded from eight patients with PD demonstrate this is a promising approach for behavior detection and developing novel closed-loop DBS systems.