درخت حوزه‌های تخصصی

The present study is part of a Ph.D. program that explores the possible effect of critical thinking-oriented dynamic assessment (CT-DA) on learners' learning potential in reading comprehension skills. 21 Iranian language learners who were homogenized in terms of their language proficiency, reading comprehension, and critical thinking abilities participated in this study. Learners were divided into three groups of CT-DA, dynamic assessment (DA), and Control group. While learners in CT-DA received mediation loaded with critical thinking techniques, learners in DA group received dynamic assessment mediation, and learners in the Control group did not receive any mediation. The analysis of the results revealed that DA and CT-DA significantly improve learners' reading potential scores. Moreover, significant differences between the Learning Potential Score (LPS) of DA and CT-DA groups were found, denoting the better performance of participants who received a critical thinking-oriented dynamic assessment. Finally, the qualitative analyses led to the detection of eleven mediational strategies which nurtured the development of the reading comprehension ability of L2 learners during CT-DA. The article concludes with suggestions for further research on dynamic assessment and critical thinking in second/foreign language development.

One of the most important risks that the banks and financial institutes face, is credit risk which is related to not-paid instalments or the instalments paid with delay by borrowers. Banks use credit scoring models In order to prevent this type of risk. The goal of this research is to investigate the joint effect of economic time cycles and the industry sector on credit scoring models we are seeking to answer the key question: “when should bank change their credit scoring models based on economic time cycles and for which industry sectors?”. The dataset of the research involves all companies that were applied for a loan in one of the Iranian major banks during the years 2008-2011. The companies have been divided into four industry sectors including “Industry and Mine”, “Oil and chemical”, “Service and Infrastructure” and finally “Agriculture”. Based on the sector of the industry and year, 54 explanatory variables, both financial and non-financial, 12 distinct industry sectors and time-specific data sets are built then classification methods were used to classify customers into two groups of defaults and non-defaults. Finally, we compared the results by Wilcoxon Test. The results show that the companies that are in the groups of Industry and Mine and Agriculture, need their own special credit scoring based on industry type model and time but two other groups don’t need of course in the studies dataset duration. Finally, the study concluded by introducing the credit scoring strategies for different four-cycle of economies