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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.

The present study scrutinized the probable significant correlation between Iranian language learners’ emotional intelligence (EQ) and reading proficiency. Moreover, the significant predicting role of the participants’ EQ for their reading comprehension proficiency was investigated. To this end, 196 Iranian B.A. English language teaching students at Islamic Azad university of Neyshabur attended the study. They formed unequal proportion of gender. The study followed a quantitative descriptive design, in which the significant correlation between the variables and the significant predicting role of EQ were investigated. As the normality of data was proved through Kolmogrow-smirnow test, the parametric statistical analyses, including descriptive statistics, Pearson correlation coefficient, independent sample T-test, and linear regression analyses were employed. The data collection instruments were an ETS TOEFL reading comprehension test and Bar On’s (1997) inventory of emotional quotient. The findings demonstrated significant positive correlation coefficients between the participants’ reading proficiency and overall EQ as well as most of its subcategories. Three highest positive significant correlation coefficients were seen among the participants’ reading proficiency level, happiness, problem-solving, and emotional self-awareness successively. The results of linear regression analysis also reflected that EQ significantly predicted the participants’ reading proficiency. No significant differences were found between the genders concerning EQ. The findings laid emphasis on the influential importance of psychological non-linguistic factors in improving linguistic competence of language learners. Language teachers should be acutely conscious of the highly influential role of psychological paradigms in boosting language learning process of language learners.

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.

To date, with the everyday growth of technology and the increase of online classes, busy modern teachers seek to lighten their burdens and boost their learners’ autonomy. Thus, this multi-method action research aimed to probe the differences between receiving computer-generated and direct corrective feedback (CF) on the syntactic accuracy and complexity of female adolescent Iranian EFL learners’ writing. Two intact classes took part in the study; one group (Group C) received CF from an AWE (Automated Writing Evaluation) tool, called Ginger software, and the other group (Group T) received CF from their teacher. Subject-verb agreement and verb form errors were considered to measure the syntactic accuracy while the average sentence length and verb form variation were regarded as the syntactic complexity. Moreover, via a questionnaire, the students’ perceptions of the kind of CF they had received were taken into account to see which one was more effective. Findings obtained from MANOVA revealed that both groups made significant improvements regarding syntactic accuracy. Group C produced more complex outputs after being exposed to the treatment while Group T didn’t make such progress. The results obtained from the questionnaire also indicated that both groups found the CF satisfactory. However, Group C held more positive attitudes. Not only was the Ginger application effective in decreasing the number of students’ writing errors but also it motivated them to be better writers and write more. The study further points to the likely merits of technology-enhanced feedback in improving EFL learners' writing ability in general, and their written syntactic accuracy/complexity in particular.

Developing a learner’s ability to communicate in a second language is the goal of communicative language teaching (CLT). Many EFL teachers now accept and employ CLT as a teaching method. Although teachers’ views have a significant influence on how they practice CLT, there haven’t been many studies about teachers’ attitudes about CLT in a specific context, such as Iran. This was revealed through a study of related literature. The current study sought to examine how EFL teachers felt about CLT. The first aim of the present study is to ascertain how EFL teachers feel about CLT. Examining the fundamental causes of instructors’ attitudes is the second goal. Thirdly, determining whether there is a disconnect between instructors' perceptions about CLT and their classroom behavior. The researchers employed a mixed-methods strategy in this study. The study included 30 EFL instructors. Three phases made up the research design: firstly, a questionnaire for gathering quantitative data was used, then interviews and observations were used for gathering qualitative data. The findings of this study showed that, in general, EFL teachers had positive attitudes about the CLT’s tenets and exhibited CLT’s traits in their teaching. There was not much of a discrepancy between the teachers’ perceptions about CLT and their actual classroom actions. This study offers valuable insights that can inform curriculum development, teacher training, and policy decisions, ultimately enhancing the quality of English language education in Iran and similar contexts around the world.

Since 2010, deep learning has been further developed, and the concept of multi-modality has penetrated into all walks of life. However, it has not been fully researched and applied in college English teaching, so this study modeled and practiced the multimodal teaching method of college English under the deep learning mode and its application. The definitions of modality and medium are first introduced, and then the definition of multimodality in this study is clarified. Then the classification of multimodal transport is expounded. The random forest algorithm is chosen as the main algorithm of this research, and a dynamic multimodal model is established. After that, there was a collaboration with a university and sophomore students were selected for practice. After processing and analyzing the collected data, it was found that in the data sample of 268 students, the number of students who did not study independently accounted for 24%, which indicates that most college students lack interest in learning English. Preliminary tests were also conducted on students' English proficiency throughout the year, and the results showed that the students' English proficiency was at a pass level and the overall English proficiency was weak. Reassessment of students' English proficiency showed that the actual teaching effect of each English proficiency was greater than 85%, and the effectiveness of English teaching in the selected universities was significantly improved. The average score improved by 8 points, indicating that multimodal teaching is scientifically effective After a semester of multimodal teaching, the English teaching effectiveness of the university selected in this article has significantly improved. The research results indicate that the development of deep computer learning has introduced multimodal concepts into the teaching field, which is very suitable for assisting language learning based on its own advantages. After a semester of multimodal teaching, the English teaching effectiveness of the university selected in this article has significantly improved. The research results indicate that the development of deep computer learning has introduced multimodal concepts into the teaching field, which is very suitable for assisting language learning based on its own advantages.

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.

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.