Identification of human emotion involving electroencephalogram (EEG) signals has become an emerging field in health monitoring application as EEG signals can give us a more diverse insight on emotional states. The aim of this study is to develop an efficient framework based on deep learning concept for automatic identification of human emotion from EEG signals. In the proposed framework, the signals are pre‐processing for removing noises by low‐pass filtering and then delta rhythm is extracted. After that, the extracted rhythm signals are converted into the EEG rhythm images by employing the continuous wavelet transform and then deep features are discovered by using a pre‐trained convolutional neural networks model. Afterwards, MobileNetv2 is used for deep feature selection to obtain the most efficient features and finally, long short term memory method is employed for classification of selected features. The proposed methodology is tested on ‘DEAP EEG data set’ (publicly available). This study considers two emotions namely ‘Valence’ and ‘Arousal’ for classification. The experimental results demonstrate that the proposed approach produced accuracies of 96.1% for low/high valence and 99.6% for low/high arousal classification. A further comparison of the proposed method is also carried out and it is seen that the proposed method outperforms other compared methods.

Araştırmanın amacı, MEB'e bağlı okullarda görevli olan öğretmenlerin çok kültürlülük ve çok kültürlü eğitime ilişkin görüşlerini ortaya koymaktır. Nitel araştırma yöntemlerinden olgubilim deseni ile yürütülen çalışma, 2014-2015 eğitim-öğretim yılında Elazığ ilinde görev yapmakta olan 16 öğretmen üzerinde gerçekleştirilmiştir. Verilerin toplanması için açık uçlu görüşme formu kullanılmıştır. Araştırma sonunda, katılımcıların çokkültürlülük ve çokkültürlü eğitim ile ilgili çoğunlukla olumlu görüşlere sahip oldukları fakat bu kavramları yalnızca ırk ve dil bağlamında ele aldıkları görülmüştür. Ayrıca, katılımcıların yarısına yakını, eğitimde resmi dilden yana olduklarını bildirmişlerdir.

In the recent years, school administrators often come across various problems while teaching, counseling, and promoting and providing other services which engender disagreements and interpersonal conflicts between students, the administrative staff, and others. Action learning is an effective way to train school administrators in order to improve their conflict-handling styles. In this paper, a novel approach is used to determine the effectiveness of training in school administrators who attended an action learning course based on their conflict-handling styles. To this end, a Rahim Organization Conflict Inventory II (ROCI-II) instrument is used that consists of both the demographic information and the conflict-handling styles of the school administrators. The proposed method uses the Neutrosophic Set (NS) and Support Vector Machines (SVMs) to construct an efficient classification scheme neutrosophic support vector machine (NS-SVM). The neutrosophic c-means (NCM) clustering algorithm is used to determine the neutrosophic memberships and then a weighting parameter is calculated from the neutrosophic memberships. The calculated weight value is then used in SVM as handled in the Fuzzy SVM (FSVM) approach. Various experimental works are carried in a computer environment out to validate the proposed idea. All experimental works are simulated in a MATLAB environment with a five-fold cross-validation technique. The classification performance is measured by accuracy criteria. The prediction experiments are conducted based on two scenarios. In the first one, all statements are used to predict if a school administrator is trained or not after attending an action learning program. In the second scenario, five independent dimensions are used individually to predict if a school administrator is trained or not after attending an action learning program. According to the obtained results, the proposed NS-SVM outperforms for all experimental works.

The methods of school administrators training also affect the professional qualification of administrators. However, any method of agreed of each country regarding the training of effective school administrators has not been presented. From this lack of education, the use of simulations in the training of school administrators was discussed in this study. For this purpose, firstly the problems experienced during the training of the school administrators, later reflections of simulations to the training of school administrators, and the preparation and limitations of managerial problem scenarios were discussed. The research was conducted as a qualitative study based on literature review. However, survey was been limited to abroad literature as there have been no studies on the use of simulations in the training of school administrators in domestic literature. As a result of the literature search, it was seen that most of the studies demonstrating the effectiveness of simulations have been conducted with formal education (primary, secondary and high school) or with university students. In these studies, it was seen that important results were obtained regarding the effectiveness of simulations in training children and young adults and pointed out that simulations are based on adult learning theory. Some studies have also suggested that the use of problem scenarios in the training of school administrators as simulations may be more effective. At the end of the study, various suggestions were made about the use of simulations in Turkish Education System.

The purpose of this study was to determine the relationship between school administrators' levels of managerial resourcefulness and the levels of stress and depression they experience. For this reason, a relational research model was used throughout the study. The study sample consisted of 704 school administrators who served in Elazığ city center and its districts. Data was collected from 205 school administrators using the method of disproportionate stratified sampling. The Managerial Resourcefulness Scale, Stress Scale, and Depression Scale were used to collect the data. According to the findings, the level of managerial resourcefulness has a high impact on the experience of stress and depression. When managerial resourcefulness increased, the level of stress and depression decreased. When the significance levels of the regression coefficients were examined, the variables of cautiousness and decisiveness were discovered to be significant predictors of both depression and stress scores.