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The current study was performed to investigate the impact of metalinguistic oral and written corrective feedback on learners’ process writing ability through virtual learning environment. To this aim, a total of 66 Iranian EFL students in Shahrekord University participated in the study. To conduct the study, a sample of IELTS expository writing (Writing Task 1) was administered to all participants for homogeneity purposes. Then, each of the two classes was divided into two parts, and each was randomly assigned to one of the four comparison groups (oral metalinguistic feedback, written metalinguistic feedback, oral metalinguistic + error logs, and written metalinguistic + error logs). Next, the writing pretest (a process writing task) was given to participants prior to instruction. The treatment lasted for eight weeks, and then process writing posttest was administered. The results revealed that all groups made progress from pretest to posttest. However, no significant difference was found among the four types of metalinguistic corrective feedback. The implications of the findings are discussed throughout the paper.

This study sought to determine the impact of self-regulation and emotional labor on reflective action among Iranian EFL teachers in online EFL classes using structural equation modeling. To achieve this, a combination of cluster sampling and stratified random sampling of 500 people was recruited. The participants were administered with the English version of Dutch questionnaire of emotional labor, Teacher Self-Regulation Scale and Teaching Reflection Inventory. After collecting data using three distinct questionnaires pertaining to latent variables, SEM analysis was conducted using IMB SPSS version 28.0 and Amos version 24.0 to analyze the path model relationships. To this end, a hypnotical model was proposed, evaluated, and specified to fit the data. The confirmatory factor analysis was employed to assess the hypothetical model by employing goodness of fit indices and analyzing all the convergences between the latent variables and the related sub-scales. According to the criteria for a good model's fitness, all of the model's values are above 0.90, indicating that the model has good fitness. And it was concluded that there was statistically significant relationships between the variables of the study.

Thanks to the significant achievements in Artificial Intelligence (AI), Machine Translation (MT), in general, and Google Translate (GT), in particular, have been extensively used in all facets of life, including language learning. However, faced with a plethora of research evidence on GT’s educational contributions, erroneous translations create disparity regarding its use in language learning. To address this lacuna, the present study systematically reviewed 10 databases, namely, Web of Science, Scopus, ERIC, ScienceDirect, Taylor & Francis Online, Wiley Online Library, SAGE Journals, Springer Link, Springer Open, and DOAJ. Additionally, it hand searched the reference lists of 44 studies selected to be included in the synthesis from database search along with references cited in three previous systematic reviews on similar topics to capture a comprehensive view of the literature related to the use of GT in language learning between 2010-2021. It reviewed 50 studies witnessing a rise in the number of studies in this area. Studies reported that although significant improvements in the quality of GT led to pedagogical gains and more tendency to implement it in language learning, instructors still distrust it. Accordingly, this research provides pedagogical implications and suggests avenues for future research on the use of GT in language learning.     

This study is aimed at investigating the effect of reading-based vs. discussion-based pre-writing activities on the writing ability of Iranian EFL learners. To this end, a quasi-experimental study was conducted with 40 Iranian intermediate EFL learners within16 to 20 age range who were selected based on their performance on an Oxford Solution Proficiency Test. They were divided into two experimental groups: Reading Group and Discussion Group. The former group was made to involve in some reading activity prior to doing the main writing task, and the latter group was made to participate in a discussion activity before the main writing task. After the treatment for 10 sessions, both groups were post-tested. The findings indicated that both groups’ writing ability improved over the course of the study, but the difference between the performances of the groups on the post-test was not statistically significant, although the Reading group’s mean score was greater than the Discussion group. The results of this study have some implications for students, language teachers, syllabus designers and material developers.

Predicting students’ performance in a course is one of the major aims of educational data mining systems. In the present study, two and three-layer artificial neural networks (ANN) and neuro-fuzzy systems (NFS) were used to predict Iranian EFL learners’ final scores and compare them with scores given by their instructor. Sixty-six students’ scores in an English reading comprehension course comprising of five sub-scores of midterm (out of 40), quiz (out of 60), final (out of 50), class participation (out of 5) and bonus (out of 2) were used for training the systems. Two and three-layer ANNs and an NFS were trained to predict students’ final scores using training data. Researchers compared the students’ final scores given by their instructor and those achieved through the ANNs and NFS. The results showed that the NFS could predict and deliver scores that were closer to the linear sum of students’ scores. Moreover, three-layer ANN had a better performance than the two-layer ANN. According to these results, data mining techniques could deliver an accurate estimate of students' abilities in a particular course.

The current study primarily explored the link between EAP students’ L2 situational interest (SI) and academic reading transformative engagement (TE). Moreover, it explored SI and its subcomponents, triggered SI and maintained SI as predictors of TE. We also sought the role of different disciplines as moderators of the relationship between SI and TE. Finally, we investigated the big five personality traits as predictors of TE. The participants of this study were 160 undergraduate EAP students recruited through quota sampling. A quantitative research method with a correlational design was employed to conduct the study. Two questionnaires were adopted to measure academic reading TE and personality traits, and a questionnaire was adapted from math SI to English learning SI to assess the students’ L2 SI. The results revealed a significant and positive relationship between students’ L2 SI and their academic reading TE. SI significantly predicted TE, and between its subcomponents, only maintained SI strongly and significantly predicted TE. Neither the difference between the disciplinary groups in terms of students’ TE nor the interaction of the comparative disciplinary groups by SI was significant. The result indicated the agreeableness trait as the sole predictor of TE. Our findings have fruitful pedagogical implications discussed in the conclusion section.

"Lexical Chains" is one of the newest branches of statistical semantics, which is defiend as the process of identifying words that have semantic relationship with each other, and help us to identify the topic and main content of the text. In this research, we aim to determine lexical chains of sermons No. 87 and 191 of Nahj al-Balaghah, based on the theory of "Saxena", as well as "Barzilay" and "Elhadad", using the descriptive-analytical and statistical method. The results show that in both sermons, the semantic relation "Antonym" has the most frequency. In sermons No. 87 and 191, respectively, 8% and 5% of the chains are "strong chains", and play the main role in determining the main themes of the sermons. According to the data, choosing the title "religious, ethical, scientific" for the sermon No. 87, and "moral, ethical, social, intellectual" for the sermon No. 191 -contrary to what "Mohammad Dashti" mentioned for them- seems more appropriate.

In order to cater for the ever-increasing demand for learning English, private language institutes have mushroomed across Iran. These institutes adopt different policies such as designing their own Teacher Training Course (TTC), following the syllabus of Certificate for Teaching English to Adults (CELTA), or localizing it for the Iranian context to train competent English teachers. Surprisingly, there exists little research evaluating these TTC's or investigating if localizing CELTA is an effective strategy for the Iranian context. This study aimed to fill this gap by delineating the steps taken by a widely-known Iranian language institute in localizing CELTA for the Iranian context, and secondly to determine if the teachers were satisfied with this localized TTC. Data collected through the websites and interviews were subjected to conceptual content analysis; while the data mined through evaluation questionnaires were analysed through quantitative descriptive statistics. The findings revealed that the Iranianized TTC suffered from discrepancies such as unreliable selection of the participants, lack of opportunities for teaching practices, not observing experienced teachers' classes, and focusing on received rather than experiential knowledge. These findings are discussed, and a number of practical suggestions are offered to improve the Iranian TTC.

The book Practical Language Testing, authored by Glenn Fulcher (2010), is an epitome of comprehensive and detailed documentation on the relevance of the appropriate techniques, knowledge, and conceptual frameworks required to interpret and develop language assessments. It is also a systematic, helpful reference for researchers and educators that address assessment selection and design concerns, offering instructors guidance on picking the proper test for a particular educational setting and assisting in interpreting the scores generated by such tests. The book lays the groundwork for instructors to establish a thoughtful awareness of test scenarios to furnish the most efficient language assessment for their learners. The book's language is highly lucid and user-friendly to both experts and novices. The book finds an appropriate amalgamation between practical applications and theoretical underpinnings to empower practitioners in comprehending the objectives of effective assessment and its function in facilitating good learning and teaching. On account of the fact that the referenced book has the potential to make a substantial contribution and have significant implications for teachers, test designers, and other stakeholders, the current article attempts to provide a review of the book and emphasize the critical concepts in each chapter.

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.