درخت حوزه‌های تخصصی

Purpose: Academic engagement as a basis for reformist efforts in the field of education is very important in achieving learning experience so that indirectly guarantees students' academic achievement. Therefore, purpose of this study was to prediction of academic engagement based on academic optimism, perception competence and achievement emotions in students. The descriptive- correlation method was used. Methodology: The statistical population consists of all male students studying of Hewraman region in year academic 2019-20 to 1476 student. In this study, according to random cluster sampling method, 100 married students by formula Plant of Tabachnick & Fidell (2007) were selected as samples and they were asked to academic engagement scale (AES) of Reeve (2013), perception competence scale of Harter (1982), academic optimism scale (AOS) of Tschannen‐Moran, Bankole, Mitchell and Moore (2013) and achievement emotions questionnaire (AEQ) of Pekrun, Goetz & Perry (2005). Findings: The data were analyzed by tests of Pearson correlation and multivariate regression. Results: Findings showed that there was positive and significant correlation between spdemic optimism, perception competence and positive achievement emotions with academic engagement (P<0/01). There was negative and significant correlation between negative with academic engagement (P<0/01). Regression analyses also revealed that %79/2 of variance of academic engagement was explained by academic optimism, perception competence and achievement emotions. Conclusion: This study confirmed the significant contribution of academic optimism, perception competence and achievement emotions in academic engagement of students. The results of this research can help planners and implementers of education organization to adopt appropriate educational policies in order to engage students in academic studies and the factors affecting it.

<span lang="EN-GB">This research presents a mathematical model for performance-based budgeting and combines it with rolling budget for increased flexibility. The model has been designed by Chebyshev's goal programming technique with fuzzy approach. The parameters or coefficients of the model are derived by measuring the productivity of the organizations considering eight criteria. Data for calculating productivity indicators were collected from gas refineries of Iran in 2011–2015 and analysed by Excel and GAMS software. Then, the model was tested for determining the 2016 budget of those refineries. The model was solved by LINGO software by linking it to Excel. The solution of the model reduced 0.68% of the total refinery's budget compared with the actual budgets for 2016, which is higher than the annual budget of some of the companies in this group.</span>