درخت حوزه‌های تخصصی

Criteria weighting is a crucial step in the entire decision-making process. Determining the appropriate weights will lead to more reliable results. This study aims to use a coalitional game method for calculating proper criteria weights in multi-criteria decision-making (MCDM). In this paper, the Shapley value method is used to determine the weight of criteria. A numerical case study of 65 banks has been used to explain the efficiency of the proposed method. To this end, using the TOPSIS technique, the alternatives are ranked once in Shapley value and again in the Shannon entropy weighted matrix. Then the results are obtained applying Spearman rank correlation coefficient are compared to efficiency-based ranking using data envelopment analysis (DEA) as a powerful benchmarking method. In the proposed method, unlike many conventional weighting methods, the selection of criteria weights is made in a coalitional game with the participation of all criteria; the obtained weights are both intuitively and objectively fairer, and more reliable rankings are provided. According to the logical and fair calculation of weights, having a simple and understandable mathematical method, and no need for experts’ judgment, the proposed method can be used in real problems. Especially where realistic ranking has a significant impact on the equitable allocation and absorption of resources.

Purpose: The purpose of the study was to examine teachers’ perceptions towards research by first investigating teachers’ conceptions about research and second, by examining the extent to which teachers read and do research. Methodology: To this end, the participants of the present study were 98 English language teachers from language institutes and schools. Utilizing Borg’s (2009) questionnaire, at first, the questionnaire responses were analyzed to determine the teachers’ view of research; in this section, the teachers were asked to point out to what extent they felt the activities described in 10 scenarios were or were not research. Findings: The results indicated that three scenarios were considered to be “probably research” and scenarios 4, 5 and 6 were regarded as “definitely research” by the participants. On the other hand, two scenarios were recognized as “definitely research”. The results of the second research question showed that among 98 English teachers only 24 teachers “often or sometimes” read published research which is a very high index. Teachers’ reasons for the answers to the second research question were lack of time, knowledge, access to resources, and institutional support. The findings of this research indicate that teachers’ conceptions of research are very near to conventional scientific theories. Only 12.2% of the teachers mentioned that they “do research”. Conclusion: the findings of the present study may be useful for teachers who want to promote their English teaching as such findings are suggested to improve their level of engagement in research activities, and consequently the quality of their research.

The volume of digital text data is continuously increasing both online and offline storage, which makes it difficult to read across documents on a particular topic and find the desired information within a possible available time. This necessitates the use of technique such as automatic text summarization. Many approaches and algorithms have been proposed for automatic text summarization including; supervised machine learning, clustering, graph-based and lexical chain, among others. This paper presents a novel systematic review of various graph-based automatic text summarization models.