مطالب

Materials evaluation plays an important role in language teaching as it helps practitioners to develop an awareness of the coverage and quality of the existing materials and as such, guides most instruction and revision decisions. Therefore, we sought to evaluate three English Vision textbooks exclusively written for Iranian secondary high school students through a comparison to the identical Interchange book series, the fifth edition. Following a model of nominal groups outlined here, the evaluation targeted the quantity and quality of the groups appearing in the two series as they are assumed to be an important part of authentic language. For the quantitative part, the results indicated a significantly higher use of nominal groups in the Interchange series. Similarly, in the qualitative analyses, we found higher quality nominal groups appearing in the Interchange books. The findings revealed inadequate inclusion of nominal groups' potentially useful structures in the Vision textbooks, supposed to help teachers to raise an awareness so as to come up with appropriate instructional policies, and the textbook developers to consider the findings when any revision is due.

In recent years, technology-mediated collaborative writing has received great attention in the research literature. The purpose of the present study is to compare the effects of online collaborative writing using Google Docs and individual writing in a face-to-face conventional classroom on the writing performance of Iranian EFL learners. To this aim, a sample of 32 homogeneous intermediate learners was selected as the study's participants. They were then randomly divided into an experimental group (N = 16) and a control group (N = 16). Then, the experimental group participants performed online collaborative writing using Google Docs while the control group fulfilled individual papers in the face-to-face classroom. Two writing tasks by the students were employed to gather the data. Later, data were analyzed through paired-sample t-tests to investigate differences in writing in each group. Finally, ANCOVA was used to investigate the difference in writing performance between the two groups. This study added further insights concerning the contribution of Google Docs in technological research in technology-mediated collaborative writing and provided some pertinent pedagogical implications

The studies on L2 argumentative writing have surveyed different dimensions of learners’ argumentative behavior and performance. However, less attention has been given to the strategies and techniques enhancing students’ argumentative repertoire. As such, the current study, taking a pedagogy-based perspective, examined the argumentative writing behavior of 30 Iranian IELTS candidates before and after the instruction in both English and Persian languages. The sample questions were of the previous IELTS Writing part 2 essays from a real test by Cambridge University Press, chosen by 3 IELTS instructors in the Institute to meet the research objectives. To this end, 180 IELTS Task 2 argumentative essays were analyzed by 30 volunteers, each having 6 essays, at least 250 words, (Persian and English). As for the students’ English and Persian argumentative writing performance, the results indicated that there were significant differences between the EFL learners’ overall means on English argumentative tests. Furthermore, the candidates chiefly applied ‘data’ and ‘claim’ most frequently in both languages with secondary elements of argument (i.e., counterargument claim, counterargument data, rebuttal claim, and rebuttal data) as the least produced elements detected in the essays. The results demonstrate that a sound pedagogy in argumentation can both influence the use of argumentation elements in both English and Persian written texts. The pedagogical implications for writing instruction and argumentative writing are discussed.

This study investigates the effect of strategy training on EFL learners’ attitudes regarding the concept and applicability of critical thinking in an educational context. To achieve this goal,80 EFL learners were selected using convenience sampling and homogenized based on the Oxford Placement Test.They completed a questionnaire adapted from Stupple et al.(2017) to assess their attitudes toward critical thinking,which served as the pre-test. Subsequently,the participants were divided into experimental and control groups.The experimental group underwent strategy training over10 sessions,during which various strategies were explained, and participants practiced using them to solve problems and exchange feedback.Additionally,they discussed the applicability and challenges associated with critical thinking in an educational context.The control group received mainstream instruction.After the treatment,both groups completed the same critical thinking questionnaire as the posttest.Furthermore,qualitative interviews were conducted with participants from the experimental group to gain an in-depth understanding. The data analysis revealed significant changes in the attitudes of the experimental group.Overall,this study follows a quasi-experimental explanatory sequential mixed-method design “In nature,the data analysis showed that,in general,the attitudes of the experimental group had changed significantly in the post-test compared to the control group.However,there was no significant difference in the attitude of the experimental and control groups regarding the applicability of strategy training for improving critical thinking and their ability to deploy critical thinking effectively.The results of the interviews and the comment section showed that teachers’ theoretical knowledge and willingness,EFL learners’ preparedness, and educational factors play important roles in the successful implementation of strategy training for developing critical thinking among EFL learners.”

This research proposes a systematic approach to provide insight on how to quantify the similarity/dependency between different architectural design styles. The study utilizes the College of Fine Arts of University of Tehran as its primary focus and examines to what extent the architecture at the College of Fine Arts at University of Tehran was affected by the French École des Beaux-Arts and the German Bauhaus school through examining more than hundred architectural works from these schools. In the first half of this study, the teaching method and educational system of these schools are reviewed and in the second half of the study the main characteristics of architectural works of each school are identified. If we consider an architectural work as the outcome of an architectural education, then the features of the created work can be attributed to the associated educational system. Based on this, the characteristic of the prominent architectural works of the two schools were investigated and used for the analysis through the proposed approach. This approach can be used for quantitatively investigating complex qualitative problems in different disciplines. On average, the results show that the college of Fine Arts is influenced by thirty-three percent from these two approaches, while the Bauhaus approach shows a greater impact.

Stakeholders of an organization avoid undesirable outcomes caused by ignoring the risks. Various models and tools can be used to predict future outcomes, aiming to avoid the undesirable ones. Early warning models are one of the approaches that could help them in doing so. This study focuses on developing an early warning system using machine learning algorithms for predicting solvency in the insurance industry. This study analyses 23 financial ratios from Iranian general insurance companies listed on the Tehran Stock Exchange between 2015 and 2020. The model uses Decision Tree, Random Forest, Artificial Neural Networks, Gradient Boosting Machine and XGBoost algorithms, with Boruta as a feature selection method. The dependent variable is the solvency margin ratio, and the other 22 ratios are the independent variables, which Boruta reduces to 7 variables. Firstly, the performance of the machine learning models on two datasets, one with 22 independent variables and one with 7, is compared based on RMSE values. The XGBoost algorithm performs the best on both data sets. Additionally, the study predicts the 2020 values for 19 insurance companies, performs stage classifications, and compares actual stages to predicted stages. In this analysis, Random Forest has the best estimate accuracy on both data sets, while Gradient Boosting Machine has the best estimate accuracy on the Boruta data set. Finally, the study compares the machine learning models' results in terms of capital adequacy classification, where Random Forest performs the best on both data sets, and Gradient Boosting Machine on the Boruta data set.