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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.

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.