درخت حوزه‌های تخصصی

Much has been done on assessment literacy (AL) components and job demand-resources (JD-R). However, an interdisciplinary look at AL components as the predictors of JD-R and its possible consequences in the engagement and burnout of teachers’ assessment performance has been neglected. To fill this gap, the present study explored this issue in the context of Iran. To this end, through convenience sampling, 146 Iranian EFL teachers were selected to answer questionnaires on AL, JD-R, burnout and engagement. A series of multiple regression analyses were run to analyze the collected data. The results showed that some components of AL such as test construction, administering, rating, and interpreting test, psychometric properties of a test, using and interpreting statistics and authenticity were significant predictors of job demand. Moreover, the results revealed that alternative and digital-based assessment, recognizing test type, distinction and function, and authenticity were significant predictors of job resource. Furthermore, test construction, administering, rating, and interpreting test, psychometric properties of a test, and using and interpreting statistics could significantly predict teachers’ burnout. In addition, alternative and digital-based assessment, giving feedback in assessment, and ethical and cultural considerations in assessment turned out to significantly predict teachers’ engagement. These findings can have theoretical and practical implications for stakeholders.

This study compared the Discussion section of theses written by M.A. English L1 and L2 students regarding lexical sophistication. Certain linguistic features were applied to investigate whether texts written in English L2 had any similarities or differences with native speakers’ (NSs) texts. To achieve this, 20 English L2 theses authored by Iranian M.A. students of Islamic Azad University of Isfahan (Khorasgan), were sampled. As such, 20 English L1 theses written by M.A. English NSs of the same major in the US and UK were randomly downloaded as a comparison corpus of English L1. The corpora were later uploaded to Coh-Matrix that processes text and discourse at different levels of language. Two main statistical procedures for data analysis were the MANOVA (Multivariate Analysis of Variance) and Discriminant Function Analysis. According to Coh-Metrix analysis, the results revealed certain similarities and differences between the corpora. Specifically, more CELEX content words were used in the NSs theses. However, the differences between the two corpora did not reach a statistical significance in terms of other indices of lexical sophistication (i.e., polysemy, concreteness, hypernymy, age-of-acquisition scores, and lexical diversity). Concerning these similarities and differences, academic writing pedagogy and theses writing abilities of Iranian English L2 learners can be improved by applying both word processing tools like Coh-Metrix, appropriate writing strategies and techniques according to the given results of current contrastive corpus study.