مطالب

The growing interest in learning English language as an international language through international textbooks has necessitated the need for the evaluation of the cultural content embedded in English coursebooks. With regard to the close interrelation of language and culture, this study investigated the extent to which cultural aspects are represented in two international EFL textbooks. To this end, content analysis was used to analyze two commercial coursebooks (i.e., Summit 1a and Viewpoint 1), which are used in Iranian language institutes. The results of the directed content analysis revealed that two subcategories emerged from the content analysis (i.e., technology and environment), which were coded as an off-list category. Therefore, it was found that the reevaluation and extension of Stern’s (1993) framework for culture is necessary. Moreover, the results of the quantitative content analysis showed that there was not a balanced representation of cultural aspects in the selected coursebooks and the most frequent element was individual values in life. In addition, it was found that there was significant variation between the two coursebooks in the representation of culture. The study concluded that more in-depth investigation of cultural content is needed to develop cultural awareness of language learners. This study has implications for language material developers, language teachers, and language learners.

With the advent of user-generated text information on the Internet, text sentiment analysis plays an essential role in online business transactions. The expression of feelings and opinions depends on the domains, which have different distributions. In addition, each of these domains or so-called product groups has its vocabulary and peculiarities that make analysis difficult. Therefore, different methods and approaches have been developed in this area. However, most of the analysis involved a single-domain and few studies on cross-domain mood classification using deep neural networks have been performed. The aim of this study was therefore to examine the accuracy and transferability of deep learning frameworks for the cross-domain sentiment analysis of customer ratings for different product groups as well as the cross-domain sentiment classification in five categories “very positive”, “positive”, “neutral”, “negative” and “very negative”. Labels were extracted and weighted using the Long Short-Term Memory (LSTM) Recurrent Neural Network. In this study, the RNN LSTM network was used to implement a deep transfer learning framework because of its significant results in sentiment analysis. In addition, two different methods of text representation, BOW and CBOW were used. Based on the results, using deep learning models and transferring weights from the source domain to the target domain can be effective in cross-domain sentiment analysis.

The adoption of E-learning in academic environments, particularly in Iran, has accelerated over the past decades. However, the lack of established guidelines for instructional design seems to have hindered the creation of effective online educational environments. Therefore, this study aimed to conduct a comprehensive qualitative research synthesis (QRS) to identify the criteria for an optimal online learning environment for Iranian university students. The study followed Major and Savin-Baden's three-phase QRS model and included 12 studies with 252 participants and 42 researchers. The synthesis integrated findings from multiple studies to gain insights into experts’ opinions and students’ perceptions, preferences, and experiences within online learning environments. The research identified four overarching themes: targeted learning, effective teaching management, socio-affective engagement, and learner empowerment. Targeted learning focuses on problem-oriented and need-oriented teaching; effective teaching management emphasizes balanced content and time management, clear objectives, and diverse presentation and evaluation methods; socio-affective engagement involves interactive feedback, social presence, and emotional communication; learner empowerment stresses autonomy, agency and active learning, including experiential and discovery learning. Applying these findings seems to offer a genuine contribution, leading to the development of culturally relevant and high-quality E-learning experiences and addressing the challenges posed by the nascent E-learning system in Iran.

The unpredictability of the stock market makes it a serious area of study and analysis. With the help of the accumulated information available in the current digital age and the power of high-performance computing machines, there is a great focus on using these capabilities to design algorithms that can learn stock market trends and successfully predict stock prices. The main goal is to create an intelligent system that provides these features for predicting short-term stock price trends to facilitate the investment decision process. To increase the accuracy and productivity of these systems and facilitate the routine of using common-sense knowledge in machine learning systems, developing or enriching knowledge bases and ontology for market modeling will be one of the effective measures in this field. In this research, an attempt has been made to strengthen and enrich the basic ontology created by the authors by using other global ontologies related to the subject of the stock market, and parts of the target space that were not addressed have been added to the ontology. By combining reference ontologies, a level of standardization is also created for the ontology and stability in the representation of concepts and relationships is ensured. In the next step, it has been tried to test the impact of the concepts and relations of the ontology in predicting stock price movements. For this purpose, news in the field of economy is considered as input and a model is created that first filters the textual inputs related to the desired stock symbol and then observes their effect on the price changes of the related stock. After improving the performance and comprehensiveness of the ontology, the study conducted in this report presented a model to measure and prove the effect of the relationships in this ontology on price changes. In practice, according to human limitations and the tools used, this effect was observed and confirmed with a proper level of certainty by checking the economic news.