درخت حوزه‌های تخصصی

PhD examination is a unique type of assessment with examiners playing a key role in it. Despite extensive research on various modes of academic genre, research on the examiners’ language representing oral review genre is yet underrepresented. One important way to identify this genre is to unpack the metadiscursive features that constitute this type of academic discourse. Using a metadiscourse framework, developed by Hyland (2005), this study investigates the metadiscursive markers that constitute Iranian and International examiners’ language of PhD dissertation evaluation. The data include the transcriptions of Iranian and International examiners’ evaluative discourse in eight PhD dissertation defenses, taken place in Iran and the US (MICASE corpus), representing English for Academic Purposes. The results indicate that the examiners use a variety of interactional as well as interactive metadiscourse markers to convey the effective and appropriate evaluation of the PhD dissertations at hand. Further breakdown of the metadiscourse devices and the comparison of the two sets of data are provided. The results unpack an aspect of oral academic review as a specific genre with implications for both EAP-user examiners and PhD candidates to become aware of the discursive features of the examiners language.

The present study investigates the efficacy of preparation time in four speaking tasks of TOEFL iBT. As the current pre-task planning time offered by ETS is very short, 15 to 30 seconds, we intended to explore how the test-takers’ speaking quality would change if the preparation time was added to the response time, giving the respondents a relatively longer online planning opportunity. To this aim, two groups of TOEFL iBT candidates were studied under pre-task and online planning conditions. Totally, 384 elicited speaking samples were first transcribed and then measured in terms of complexity, accuracy, and fluency (CAF). The results yielded by a series of One-way MANOVA revealed the online planning group significantly outperformed the pre-task planning group in terms of accuracy and fluency across all four speaking tasks. Although with less robustness, the online planners had significantly higher speech complexity represented by lexical diversity and left-embeddedness. The results obtained through this study may challenge the efficacy of the currently provided preparation time in TOEFL iBT speaking subsection.

A bulk of research has revealed that emotions play a crucial role in different aspects of second/foreign language (L2) education. However, the emotions that English as a foreign language (EFL) students experience during assessment have been neglected, so far. Against this backdrop, this qualitative study intended to unveil the typologies, antecedents, and outcomes of assessment-related emotions of 24 Iranian EFL undergraduate students studying applied linguistics. To this end, a semi-structured interview and a narrative frame were employed. The results of the thematic analysis demonstrated that undergraduate EFL students experienced different positive and negative emotions before, during, and after assessment practices at the university. Furthermore, it was found that “teacher”, “tasks”, and “textbook” were the most frequent causes of the participants’ perceived emotions in assessment. Concerning the outcomes of assessment-induced emotions, the results demonstrated that “burnout”, “frustration”, “demotivation”, “stress”, and “self-doubt” were the most frequent negative outcomes, while “motivation”, “learning”, “pride”, and “excitement” were repeatedly posited as positive outcomes. Finally, the study enumerates some implications for L2 educators and researchers concerning the role of emotions in assessment processes and practices.

This paper discusses the use of Persian in comparison with Dari, a dialect of Persian language used in Kerman, one of the cities of Kerman Province located in southeast Iran. It is based on a random selection of 47 subjects from different sex, age, and occupational groups of Zoroastrian people with different levels of education in seven domains, i.e. family, friendship, neighborhood, transaction, education, government, and employment. In this study, the subjects were asked to determine which language they use in different domains on Likert’s 4-point scale. After collecting the data by the use of a questionnaire with 30 situations in the seven domains and analyzing them, the researcher concluded that there is a Persian-Dari diglossia in Zoroastrian speech community in which Dari functions as the Low language whereas Persian is the High one. As a matter of fact, Dari has been restricted to family domain and the young generation has lost it to a large extent.

This study explores how professional and student writers manifest their authorial identities and project their gender voices in their academic texts. To this end, 38 male-authored and 38 female-authored articles published in seven leading international journals were selected. Moreover, 38 articles written by male students and 38 articles authored by female students were collected from two universities in Iran. Taking an academic writing course, these students handed in their papers as term projects. These academic writings were analyzed based on Hyland’s metadiscourse framework comprising two major resources: interactional and interactive metadiscourse resources. The findings indicated the male authors mainly attended to discourse organization using more interactive resources while the female authors mostly solicited solidarity by employing more interactional resources. The professional authors engaged in a more critical stance using self-mentions and attitude markers and the students focused on discourse organization. Attitude markers and self-mentions, as markers of stance-taking, were absent in the students’ writing. The professional authors made use of their gender identity to promote their authorial identity instead of suppressing it. These results suggest that EAP programs should inform students how to employ both metadiscourse resources and their gender-based discourse choices to express their authorial identity more effectively.

L2 Motivational Self System (Dörnyei, 2005, 2009) holds a remarkable potency in explaining the long-term routes to L2 acquisition. Meanwhile, due to the prevalence of dynamic systems perspective, the theory has been awaited to account for the affective and cognitive aspects of L2 learning. The present study attempted to test an integrated model to examine the correlations between components of the revised L2MSS, L2 emotions of Enjoyment and Boredom, Intended Learning Effort, Vigilant and Eager L2 Use, and L2 Proficiency utilizing structural equation modeling. The data was obtained from 350 TOEFL test takers in ETS official centers in Iran. The results demonstrated the strongest prediction of Intended Learning Effort by Ought L2 Self/Own, coming next to the Ideal L2 Self/Own and Ideal L2 Self/Other. Ideal L2 Self/Own and L2 Learning Experience were positive predictors of L2 Enjoyment, whereas Ought L2 Self/Other and L2 Learning Experience had a positive and negative impact on L2 Boredom, respectively; L2 Enjoyment predicted Intended Learning Effort and Eager L2 Use positively, whereas L2 Intended Learning Effort Vigilant L2 Use were predicted negatively and positively by Boredom, respectively. Moreover, Eager L2 Use and Intended Learning Effort were positive predictors of L2 Proficiency; however, Vigilant L2 Use was conversely related to L2 Proficiency. Overall, the findings illustrate the superiority of a promotion orientation to L2 learning and the equal importance of quality and quantity of the Motivated Learning Behavior, which has significant pedagogical implications in terms of motivational and self-regulatory learning strategies in designing and managing L2 classroom tasks.

 Juxtaposing the ubiquitous facets of mobile technology as everyware (Greenfield, 2010) and vocabulary as one of the most axial aspects of language learning (Bowles & Cogo, 2016; Schmitt & Schmitt, 2020) , besides the importance of considering learners’ attributes and needs (Taghizadeh, 2019) while developing a mobile vocabulary application (app), necessitates analyzing the relationship and impact of all these elements in a single structural model. To tackle the issue, first, via a mixed-methods design, the researcher developed a bespoke mobile application using the task model and the principles of agile methodology. and then investigated the impact of using the app on a sample of 62 Iranian EFL university students’ vocabulary recognition and recall, the results of which were published in two other articles. In this study, the researcher integrated the obtained data within a proposed structural model and assessed the model's fitness to investigate the interaction and interrelationship among the latent variables mentioned above. The results obtained from SEM-PLS analyses revealed that within the unified structural model, the learners’ preferences and needs were favorably influenced by their learning style orientation and technology savviness. Similarly, the findings verified the positive impact of considering learners’ preferences and needs during the agile app development lifecycle on the target participants’ vocabulary knowledge, encompassing vocabulary recognition and recall. Finally, the fitness of the proposed structural model was verified based on the criteria for model assessment mentioned by Sparks and Alamer (2022) . The SEM-PLS data analyses and the implications of the study are presented and discussed.

Cognitive diagnostic models (CDMs) have received much interest within the field of language testing over the last decade due to their great potential to provide diagnostic feedback to all stakeholders and ultimately improve language teaching and learning. A large number of studies have demonstrated the application of CDMs on advanced large-scale English proficiency exams, such as IELTS, TOEFL, MELAB, and ECPE. However, too little attention has been paid to the utility of CDMs on elementary and intermediate high-stakes English exams. The current study aims to diagnose the reading ability of test takers in the B1 Preliminary test, previously known as the Preliminary English Test (PET), using the generalized deterministic input, noisy, “and” gate (G-DINA; de la Torre, 2011) model. The G-DINA is a general and saturated model which allows attributes to combine in both compensatory and non-compensatory relationships and each item to select the best model. To achieve the purpose of the study, an initial Q-matrix based on the theory of reading comprehension and the consensus of content experts was constructed and validated. Item responses of 435 test takers to the reading comprehension section of the PET were analyzed using the “G-DINA” package in R. The results of attribute profiles suggested that lexico-grammatical knowledge is the most difficult attribute, and making an inference is the easiest one.

Cognitive diagnostic models (CDMs) have received sustained attention in educational settings because they can be used to operationalize formative assessment to provide diagnostic feedback and inform instruction. A large number of CDMs have been developed over the past few years. An important component of all CDMs is a Q-matrix that specifies a particular hypothesis about the relationship between each test item and its required attributes. The purpose of this study was to construct and validate a Q-matrix for the reading comprehension section of the Islamic Azad University English Proficiency Test (IAUEPT), as an advanced English placement test designed to measure language ability of Ph.D. candidates who tend to pursue their studies in the IAU. To achieve this, using item responses of 1152 candidates to twenty items of the reading section of the test, an initial Q-matrix was constructed based on theories and models of second/foreign language (L2) reading comprehension, previous applications of CDMs on L2 reading comprehension, and brainstorming and consensus of five content experts. Then, the initial Q-matrix was empirically validated using the method proposed by de la Torre and Chiu (2016) and checking mesa plots, and heatmap plot. Five attributes were derived for the reading comprehension section: vocabulary, grammar, making an inference, understanding specific information, and identifying explicit information. Finally, the analysis of the Generalized Deterministic Inputs, Noisy “and” Gate (GDINA) regarding absolute fit at item- and test-level as well as three residual-based statistics showed the accuracy of the Q-matrix and a perfect model-data fit.