Teacher Allocation Policies and the Unbalanced Distribution of Novice and Senior Teachers across Regions in Turkey

Policies toward fostering a more balanced distribution of teacher quality have garnered considerable attention from researchers and policymakers around the world. This attention has been motivated largely by the widely acknowledged educational goal of providing quality education for all children. Equipped with similar policy concerns, this study examines the initial assignment of novice teachers and voluntary transfer of senior teachers to determine whether there is any kind of sorting pattern in the allocation of novice and experienced teachers to schools across regions, particularly across provinces, in Turkey. Using the entire initial teacher assignment and voluntary teacher transfer data in between 2010 to 2014, the descriptive and correlational analyses in this study provide clear evidence that both novice and senior teachers are unevenly allocated across regions. The findings suggest that already-disadvantaged students in the less-developed eastern regions of the country are far more likely to be exposed to novice and/or less-experienced teachers. Possible explanations of this observed teacher sorting pattern and its policy implications are discussed

A Case Study of Learner Support Services in the Turkish Open Education System

The purpose of this study was to examine and identify support service needs and preferences of distance learners studying at the Turkish Open Education System (OES). In order to fulfill this purpose, views and perceptions of OES students on importance and accessibility of student support services at the OES were investigated through a mixed-method approach that uses both qualitative and quantitative data collection and analysis methods. Data collection took place in three distinct phases. In the first phase, available learner support services were identified through review of the literature, investigation of institutional artifacts, and interviews with the institutional representatives. In the second phase, a questionnaire was administered to OES students in order to collect data about demographic information, students\u27 goals and motivations for participating in the distance education program, their perceptions about the importance and accessibility of support services, and types of support services they needed at different stages of their study. It also included open-ended questions to allow participants to comment on factors that are most assistive and most impeding in their distance learning experience, and also to allow them to offer suggestions for improving and/or expanding the existing learner support services. Out of 450 questionnaires distributed, 311 usable questionnaires were returned. In the third phase, individual and group follow-up interviews were performed with OES students to gain an in-depth understanding of participants\u27 distance learning experience and to triangulate questionnaire data. The results of this study revealed that affective support needs of OES students are largely unmet. A large needs gap was identified for five of the six affective support services included in the questionnaire. The largest needs gap was for the counseling services to promote student motivation. Moreover, a large needs gap was identified for two of the ten cognitive support services included in the questionnaire. These were face-to-face academic counseling and communication with course instructor. In addition to affective and cognitive support services, a greater needs gap was identified for one of the six systemic support services, which is orientation to the course media/delivery format. Statistical tests (t test and ANOVA) revealed significant differences (p \u3c 0.05) in importance and accessibility ratings of several support services based on gender, employment status, and study time

Genetic Algorithm and Fuzzy Based on The Taguchi Optimization to Improve The Torque Behavior of An Outer-Rotor Permanent-Magnet Machine

The torque behavior of an outer-rotor surface-mounted permanent-magnet machine is improved by identifying seven pertinent design variables, including rotor height. The optimal design variables are revealed by analyzing 18 experiments determined by the Taguchi method for the minimum torque ripple, minimum total harmonic distortion of the induced voltage, and maximum average torque. In addition, the optimal design variables are obtained very quickly by using fuzzy inference mechanism and genetic algorithm (GA) based on the Taguchi method with the single response of the multi-response performance index instead of multiple responses. A considerable amount of multi-response improvement is achieved according to the results of the two optimizations

New magnet shape for reducing torque ripple in an outer-rotor permanent-magnet machine

Torque ripple is a major problem for permanent-magnet (PM) machines. It is examined by focusing on the magnetic circuit of the PM machine. Because there is a relationship between the torque ripple and the magnetic energy that is stored in the magnetic field along the air gap of the PM machine, a variation in the magnetic energy was revealed initially. A new magnet geometry is obtained by forming ledges and notches in the permanent magnets to modify the variation of the magnetic energy and the fluctuation in torque. Thus, a new PM design is proposed in this study to minimize the torque ripple of an outer-rotor surface-mounted PM machine. An improvement of 44.69 in the torque ripple is achieved thanks to the new magnet design. In addition, improvements are made in the average torque and the total harmonic distortion of the back electromotive force

Multiresponse optimization to improve the torque behavior of an outer-rotor permanent-magnet machine using gray relational analysis based on the Taguchi method

The torque behavior of an outer-rotor surface-mounted permanent-magnet machine is improved by identifying seven pertinent design variables, including rotor height. The optimal design variables are revealed by analyzing 18 experiments determined by the Taguchi method for the minimum torque ripple, minimum total harmonic distortion of the induced voltage, and maximum average torque. In addition, the optimal design variables are obtained very quickly by using gray relational analysis based on the Taguchi method with the single response of the gray relational grade instead of multiple responses. A considerable amount of multiresponse improvement is achieved according to the results of the two optimizations. Performance improvements of 20.6%, 32.0%, and 24.5% are obtained for the average torque, the torque ripple, and the total harmonic distortion of the back-EMF, respectively

New Stator Tooth for Reducing Torque Ripple in Outer Rotor Permanent Magnet Machine

Torque ripple has been a major problem for the permanent magnet (PM) machine. It is discussed focusing on the magnetic circuit of the PM machine. Since it is known the relationship between the torque ripple and the magnetic energy that is stored in the magnetic field along the air gap of the PM machine, fluctuation in the magnetic energy was initially revealed. New tooth geometry was obtained by drilling holes into stator tooth to modify this variation in the magnetic energy and the fluctuation in torque. Thus, a new stator tooth design in outer rotor surface-mounted permanent magnet (OR-SPM) machine was proposed to minimizing the torque ripple in this study. Improvement in torque ripple value was performed in excess of 50% thanks to new stator tooth design. In addition, improvements have been carried out at the average torque and total harmonic distortions (THD) of back EMF (electromotive force)

A Comparison of Occupational Choices of Achieving and Underachieving Highschool Juniors

98 p.Thesis (Educat.D.)--University of Illinois at Urbana-Champaign, 1964.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Detection of Road Potholes by Applying Convolutional Neural Network Method Based on Road Vibration Data

In the context of road transportation, detecting road surface irregularities, particularly potholes, is of paramount importance due to their implications for driving comfort, transportation costs, and potential accidents. This study presents the development of a system for pothole detection using vibration sensors and the Global Positioning System (GPS) integrated within smartphones, without the need for additional onboard devices in vehicles incurring extra costs. In the realm of vibration-based road anomaly detection, a novel approach employing convolutional neural networks (CNNs) is introduced, breaking new ground in this field. An iOS-based application was designed for the acquisition and transmission of road vibration data using the built-in three-axis accelerometer and gyroscope of smartphones. Analog road data were transformed into pixel-based visuals, and various CNN models with different layer configurations were developed. The CNN models achieved a commendable accuracy rate of 93.24% and a low loss value of 0.2948 during validation, demonstrating their effectiveness in pothole detection. To evaluate the performance further, a two-stage validation process was conducted. In the first stage, the potholes along predefined routes were classified based on the labeled results generated by the CNN model. In the second stage, observations and detections during the field study were used to identify road potholes along the same routes. Supported by the field study results, the proposed method successfully detected road potholes with an accuracy ranging from 80% to 87%, depending on the specific route

Inhibition of enzymic browning in cloudy apple juice with selected antibrowning agents

Golden Delicious apple juice was subjected to enzymic browning in the presence of the selected antibrowning agents: ascorbic acid, isoascorbic acid, L-cysteine, sorbic acid, benzoic acid, cinnamic acid and beta-cyclodextrin. The relative effectiveness of these antibrowning agents for inhibition of enzymic browning in apple juice was determined in terms of colour and enzyme activity measurements with respect to time for approximately one day storage period at 25 +/- 1 degreesC. The most effective agents were determined as L-cysteine, cinnamic acid and ascorbic acid. Response surf ace methodology was Used to evaluate the Potency of the L-cysteine, ascorbic acid and cinnamic acid combination for the control of enzymic browning. The ascorbic acid, L-cysteine and cinnamic acid combination provided better results than the individual compounds. The Optimum combination was determined as 0.49 mM ascorbic acid, 0.42 mM L-cysteine and 0.05 mM cinnamic acid in the cloudy apple juice stored for 2 h at 25 +/- 1 degreesC