The interactions between individuals and their cognitive traits results in language learning. The aim of this article is to investigate the relationship between Iranian EFL learners' language learning strategies and their language proficiency. Therefore, the Strategy Inventory for Language Learning (ILLS) and Michigan English Language Proficiency Test (MELPT) were administered to a group of 63 students studying English language to determine the best predictors of language proficiency regarding the five subscales of the ILLS. Analysis by Pearson product-moment correlation showed significant correlations between cognitive strategies and Iranian EFL learners' language proficiency. Moreover, regression analysis discovered that cognitive strategies could predict language proficiency by explaining 0.59% of the changes in Iranian learners' language proficiency. The educational and scientific consequences and implications of the study are discussed.

Recommender Systems (RS) help users in making appropriate decisions. In the area of RS research, many researchers focused on improving the performances of the existing methods, but most of them have not considered the potential of their employed methods in reaching the ultimate solution. In our view, the Machine Learning supervised approach as one of the existing techniques to create an RS can reach higher degrees of success in this field. Thus, we implemented a Collaborative Filtering recommender system using various Machine Learning supervised classifiers to study their performances. These classifiers implemented not only on a traditional platform but also on the Apache Spark platforms. The Caret package is used to implement the algorithms in the classical computational platform, and the H2O and Sparklyr are used to run the algorithms on the Spark Machine. Accordingly, we compared the performance of our algorithms with each other and with other algorithms from recent literature. Our experiments indicate the Caret-based algorithms are significantly slower than the Sparklyr and H2O based algorithms. Also, in the Spark platform, the runtime of the Sparklyr-based algorithm decreases with increasing the cluster size. However, the H2O-based algorithms run slower with increasing the cluster size. Moreover, the comparison of the results of our implemented algorithms with each other and with other algorithms from recent literature shows the Bayesian network is the fastest classifier between our implemented classifiers, and the Gradient Boost Model is the most accurate algorithm in our research. Therefore, the supervised approach is better than the other methods to create a collaborative filtering recommender system.

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Language is a socio-cultural phenomenon. A large number of language learning strategies arise from the interactions between individuals and their socio-cultural contexts. The aim of this article was to explore the relationship between EFL learners' language learning strategies and their social and cultural capital. To this end, the Social and Cultural Capital Questionnaire (SCCQ) and the Strategy Inventory for Language Learning (SILL) were administered to a sample of 63 undergraduate students majoring in English language so that the researchers could examine the best predictors of language learning strategies in terms of five factors of the SCCQ, namely, social competence, social solidarity, literacy, cultural competence, and extraversion. Results from Pearson product-moment correlation showed highly significant correlations between all five factors of SCCQ and learners' language learning strategies. Moreover, results from the regression analysis revealed that a combination of literacy, social solidarity, and extraversion was the best predictor of language learning strategies by explaining 50% of the variances in EFL learners' language learning strategies scores. The implications of the study are discussed.

Medical practitioners' ethnocentric orientations and English language skills contribute to intercultural communication in the context of health care. The present study is a quantitative survey study and aims to investigate the relationship between ethnocentrism and investment in learning English in the multicultural setting of English classrooms at an Iranian medical university. To this end, 200 Iranian medical students' levels of ethnocentrism and investment were measured using GENE and IELL scales. The data were analysed using descriptive data analysis and correlation analysis. The findings of this study revealed a strong negative relationship between the two constructs. The participants had relatively moderate levels of ethnocentrism and investment, but female medical students were found to be significantly less ethnocentric than male students. However, there were no significant differences between the two genders' levels of investment. Further, considering the importance of context this research, the relationship among ethnocentrism and investment, and various contextual factors such as linguistic loyalty, intercultural contact, and social comfort in multiethnic English classrooms were explored to explain the findings. Among different contextual factors, social comfort in classroom seemed to have the greatest impact on investment. Conclusion, suggestions, and limitations are discussed.