درخت حوزه‌های تخصصی

Adverse Conditions in the Professional Lives of English Instructors: Recommendations to Ameliorate Teacher Resilience Abstract The present qualitative study strived to examine Iranian English instructors’ experiences of adverse conditions in their teaching environments. To yield this purpose, 30 novice language institute English instructors in Tabriz (Iran) were selected as the participants. To triangulate the data, we utilized interview and narrative data collection techniques. Adapting inductive, bottom-up approach to analyzing the themes, three main themes were generated: teacher factors, contextual factors, and student factors. The findings of the study were ascribed to the participants’ resilience in their academic settings. The findings can provide the teacher educators and educational psychologists with certain guiding principles regarding teacher resilience. The results of the study has a number of implications for pedagogy and teaching practice as well as the educational psychology and teacher education . Keywords: adverse conditions, emotional regulation, psychological wellbeing, teacher education, teacher resilience

The desirability of the financial reporting can greatly help the users of finan-cial information in making investment decisions. The purpose of this re-search is to measure the readability of financial reporting using a multi-indicator decision-making model and the artificial neural network method and the role of information presentation time in its improvement. In this research, various indicators have been used to measure the readability of financial reporting, and the quality of reporting is obtained through the rank-ing of companies by the stock exchange. In this research, the number of 149 companies admitted to the Tehran Stock Exchange in the period of 2010-2020 was examined, and to measure the financial readability through struc-tural equations and Stata software, and to test the hypothesis of the research, the regression model and Eviews econometrics software were used. In this study, we have tried to Use machine learning techniques and optimization tools as a way to derive adaptive-robust nonlinear models that can reduce the risk of model error as much as possible. The findings of the research show that the time of providing information has an impact on the readability of financial reporting. The obtained outputs from the estimation of the artificial neural networks and results obtained from estimation, using of this method with evaluation scales concerning random amount and comparing it with adjusted R, we found that there is meaningful relation between the associated variables and return. However, such network has the least error than other networks. The results show an overall improvement in forecasting using the neural network as compared to linear regression method. In other words, our proposed system displays an extremely higher profitability potential. The obtained result can be argued that the more the company's information is provided by the managers to the company's shareholders and investors on time and at the right time, the more readable and understandable the financial reports will be.