مطالب

This study is a mixed-methods parallel design aiming at exploring the perception of 49 ESP teachers toward formative classroom assessment with regard to their language assessment literacy (LAL) in Iran within the theoretical framework of Hay and Penney (2013). The data were collected through an online semi-structured interview and a questionnaire, extracted from Shahzamani and Tahririan's (2021) questionnaire, consisting of 13 Likert items and 9 open-ended questions which were validated for this study through confirmatory factor analysis. The results of a Chi-square test on the Likert items showed a significant difference between the participant’s responses to each item which revealed an overall tendency of the ESP teachers toward employing formative assessment as an efficient way for evaluating their own teaching efficacy, the quality of the teaching materials, and their students' progress. The results also revealed that the two features of 1) ability to assess learners based on cultural-related issues in a certain context, and 2) ability to design suitable assessment methods for multilevel ESP classes should be added to the components of LAL in the existing literature. The results have implications for teacher educators to consider these two features in teacher education programs. Also, academic authorities are to notice that reliance on summative assessment and evaluation merely based on final exam scores are not appreciated by most ESP teachers.           

Rudbar, located in the south of Gilan, on both sides of the Sefid-rud River, is a mountainous and one of the ancient regions dating back to at least 200,000 years ago. The artifacts obtained from Darband- Rashi Cave, Marlik Hill, Caluraz, Halimehjan, etc. indicate the existence of ancient civilizations in this region, each of which has its own cultural characteristics. Shemam is one of the old villages with a rich cultural history in the highlands of South Rostamabad. The first archeological excavations in this village were carried out under the supervision of Shahidzadeh in 1970 and a big cemetery belonging to the Parthian period was discovered with slum graves. During the earthquake of 1989, this village was severely damaged and the people living there moved to the lower part and Rostamabad. In recent years, people have resumed building houses on their ancestral estates. During these constructions and the statue, which is accompanied by the supervision of cultural heritage experts of Gilan province, traces of a hand-made structure appeared. Therefore, the construction work was stopped and after issuing a permit from the Cultural Heritage Research Institute of the country, number 4002867, in summer 2021, speculation was made in this area under the supervision of Solmaz Raof. This structure is most likely a grain storage pit dug in the heart of the limestone bed. The wall inside the structure is covered with mud and the floor is covered with beaten mud. Other cultural data obtained from this excavation include pottery pieces. According to comparative study and comparison, most of these pieces are related to the Parthian period.

The impact of personal judgment on the assessment of an individual’s financial situation has been drastically reduced through the development of credit scoring. The systems are capable of deciding based on an applicant’s total score which is a combination of several factors and indicators. Over the past few decades, credit scoring has been considered an essential tool for evaluation in various institutions and has also been able to transform the industry as a whole. Most of the research conducted in the field has taken into account traditional credit scoring, but considering the ever-evolving technological world that we live in and the increasing emergence of new social media networks, such research has now become obsolete. Such technological advancements have not only paved the way for far more sophisticated credit scoring systems but also essentially rendered the previous generations useless. It should be noted that credit scoring and its features have widely been discussed across the globe but, considering the various aspects and models that have to be taken into account, no one best method has been designed or suggested for it so far. This study shows that social media channels tend to perform relatively well in predicting stock market trends when the overall index is growing positively. The research also illustrates that a higher number of days of activity and a large number of signals released do not necessarily mean that the channels can or have credited their offered stock return on a one-month time frame. The methodology used is "CRISP-DM," which consists of six steps. The main variables include social and financial variables that are examined for six months. In the research, we seek to identify, analyze and categorize active telegram channels in stock signals using the data mining model and the RFM method. The k-means algorithm is selected for this category. Then, in each cluster, the importance of social variables and the performance of the channels are extracted by the EXTRATREECLASSIFIER algorithm, and channel performance is measured by considering the changes in the total index.