درخت حوزه‌های تخصصی

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.

Language testing and assessment has grown in popularity and gained significance in the last few decades, and there is a rising need for assessment literate stakeholders in the field of language education. As teachers play a major role in assessing students, there is a need to make sure they have the right level of assessment knowledge and skills to accomplish their duties as assessors. The present study sought to develop and validate an assessment literacy test. To this end, a thirty-five scenario-based Language Assessment Literacy Test (LALT) was developed based on the seven standards covered by the Standards for Teacher Competence in Educational Assessment of Students (1990). Construct validity of the test was investigated by collecting data from 168 Iranian EFL teachers. To investigate the validity of the measure, tests of reliability, item difficulty, and item discrimination were carried out. The test was then subjected to EFA (Exploratory Factor Analysis whose results showed a seven-factor solution for the test items. The implications of the study for EFL teachers, language testers, teacher trainers, and curriculum developers are discussed.

Pablo Neruda est l’un des plus grands poètes du vingtième siècle. Ce qui le distingue des autres, c’est la variété de sujets dans ses poésies et la langue simple et authentique qu’il y applique. Notre article a pour fin de faire une étude comparative des traductions française et persane de l’un de ses recueils, intitulé Cent Sonnets d'Amour selon les théories de Henri Meschonnic, linguiste, poète et théoricien français, notamment d’après son point de vue sur l’ajout et la suppression dans les traductions ; nous allons illustrer cette recherche par des exemples extraits des textes tendus en français et en persan. Cela dit, l’objectif de notre recherche est de trouver des réponses aux questions concernant la possibilité de rapprocher la forme et le fond de la traduction de ceux du texte original entre deux langues sœurs (l’espagnol et le français) et entre celles qui semblent lointaines (l’espagnol et le persan).

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.

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.

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.