درخت حوزه‌های تخصصی

The studies conducted so far on the effectiveness of resolution methods including the discussion method in resolving discrepancies in rating have yielded mixed results. What is left unnoticed in the literature is the potential of discussion to be used as a training tool rather than a resolution method. The present study addresses this research gap by exploring the data coming from rating behaviors of 5 Iranian raters of English. Qualitative analysis of the data indicated that the discussion method can serve the function of training raters. It helped raters rate more easily, quickly and confidently. Furthermore, it helped them improve their understanding and application of the rating criteria and enabled them justify their scoring decisions. Many-faceted Rasch analysis also supported the beneficial effects of discussion in terms of improvement in raters’ severity, consistency in scoring, and the use of scale categories. The findings provide insight into the potential of discussion to be used as a training tool especially in EFL contexts in which lack of access to expert raters can be an obstacle to holding training programs. The author argues for future studies to focus on how discussion may function depending on the rating scale used.

The present study was conducted to further improve the practice of written corrective feedback by integrating the two known feedback types (i.e. direct corrective feedback and metalinguistic explanation). With this aim, a sample of sixty-nine high-intermediate Iranian EFL learners was assigned into different feedback groups. While the first and second groups received direct and metalinguistic explanation feedback, respectively, the third group received an integration of both. Results showed that all three types of feedback improved the learners’ accuracy, though with different degrees of success. However, the results of the delayed post-tests revealed that only the integration of Direct Corrective Feedback (DCF) with Metalinguistic Explanation (ME) had the most sustainable effect on the learners' accurate use of the articles in the long run. It is discussed that the integration of DCF and ME can enhance the corrective effect of the feedback and reduce the defects which have been associated with the practice.

Purpose: The main purpose of this study was to identify the components of SMEs' knowledge rewards, and based on other components, learning and development strategies were developed based on the obtained components. Methodology: The present study was a descriptive survey in terms of purpose and method of data gathering and was mixed in terms of research method. Therefore, in the qualitative part, using semi-structured interviewing tool with 15 human resources experts and stakeholders Interviews were conducted and the data were analyzed using qualitative content analysis and a model was designed by combining them. Then, the model was tested by a researcher-made questionnaire and sample of 162 SMEs in Tehran, using quantitative structural equation research method with LISREL software and then to formulate strategies and strategies related to learning and learning. The paired t-test and SPSS software were used for development. Reliability of qualitative part of Holstein method was 0.83% and Cronbach's alpha coefficient was 0.81. Findings: The findings showed that 22 sub-components of comprehensive reward factors were identified, all of which had appropriate factor loadings and the model was confirmed. Among the non-material dimensions of learning and development, it was the most important factor and in explaining learning and development strategies, the highest gap was observed in education, which was the first and foremost strategies in this field. Conclusion: The findings of the study confirm the relationship of each of the material and non-material components to the concept of comprehensive reward, but the most important is the importance of the material components to the comprehensive reward. In the payment component, cash awards and in benefits, the benefits of flexibility had the highest impact, but among the non-material components that were secondary to the overall reward, the highest impact was on the learning and development component, which included performance evaluation. It matters the most. Non-material factors were expected to be of high priority for knowledge workers, but this was not favorable to the statistical population.