مطالب

Online learning platforms have become commonplace in modern society today, but high dropout rates and decrement students’ performance still require more attention in such online learning environments. The purpose of this research is to accelerate the identification of students at risk of academic failure in order to take appropriate corrective action. Therefore, we have proposed model to achieve this goal and ultimately improve the performance of students and faculty. Then, for early prediction of students at risk of academic failure, the short-term memory neural network (LSTM) and the widely used support vector algorithm have been used to analyze students’ time based behaviors using data from the University of Tehran e-learning system. To demonstrate the optimal performance of the predictive algorithm, we compared the LSTM network with the support vector algorithm with different evaluation criteria. The results show that the use of LSTM network for early prediction of students at risk provides higher predictive accuracy compared to the support vector machine algorithm. In this research, our method in predicting students’ performance with LSTM network has achieved 94% accuracy and with support vector machine algorithm has achieved 88% accuracy. In addition, the Area Under the Curve (AUC) was 0.936 and 0.882, respectively, using the LSTM algorithm and the support vector machine. Therefore, according to the obtained results, it can be seen that our proposed algorithm has an important and effective contribution to improving the final performance of teachers and students during the course.

The main criterion in investment decisions is to maximize the investors utility. Traditional capital asset pricing models cannot be used when asset returns do not follow a normal distribution. For this reason, we use capital asset pricing model with independent and identically asymmetric power distributed (CAPM-IIAPD) and capital asset pricing model with asymmetric independent and identically asymmetric exponential power distributed with two tail parameters(CAPM-AIEPD) to estimate return and risk. When the assumption of normality is violated, the first and second moments lose their efficiency in optimization and we need to use the third and fourth moments. For the first time, we propose independent and identically asymmetric exponential power distributed with two tail parameters. Then, we use higher moments optimization with unequal weights to optimize portfolios. The results indicate that capital asset pricing model with independent and identically asymmetric power distributed (CAPM-IIAPD) is better than asymmetric independent and identically asymmetric exponential power distributed with two tail parameters(CAPM-AIEPD) to estimate return and risk. Adjusted Sharp ratio in portfolio optimization in second moments are higher than others. Adjusted returns to risk in third and fourth moments in the CAPM-IIAPD model significantly differ from the CAPM-AIEPD model and have a better performance.

هدف از پژوهش حاضر بررسی رابطه بین هوش هیجانی و میزان عشق در دوران کرونا بود که با روش کمی از نوع همبستگی انجام شد. جامعه آماری این پژوهش، زنان و مردان متأهل و مجرد 20 تا 45 ساله با مدرک تحصیلی دیپلم تا دکتری در تهران، کرج، اصفهان، شیراز، مشهد و تبریز بود که تعداد 100 نفر زن و مرد به صورت در دسترس انتخاب شدند. روش جمع آوری اطلاعات غیر تصادفی از نوع در دسترس بود که به دلیل بیماری کرونا پرسشنامه ها از طریق سایت پرسال به صورت لینک طراحی و از طریق یکی از فضاهای مجازی توزیع گردید. ابزار پژوهش شامل پرسشنامه رگه هوش هیجانی شوت و مقیاس عشق مثلثی استرنبرگ بود که در اختیار آزمودنی ها قرار گرفت. نتایج نشان داد که بین هوش هیجانی و عشق رابطه معنادار وجود دارد.

The important role of leadership in management of today's organizations has been emphasized for many years by executives and experts in the field. The role of knowledge in leading organizations is prominent. This study aims to examine the role of the knowledge-Oriented Leadership in market orientation that seeks to achieve innovation. Market orientation (MO) lies at the bottom of modern marketing thinking and practice. Although research has shown that MO contributes to firm performance through innovation implementation, an understanding is lacking on how the dimensions of MO (customer orientation, competitor orientation, and inter-functional coordination) may have differential effects on innovation, especially in the sales force context. As a survey research, data were gathered through a structured questionnaire with a sample of 92 executive-level employees employed in ISPs in Mazandaran province. SEM was used to examine the standard error of the estimate and t-values. Findings suggest that knowledge-oriented leadership has a positive effect on innovation implementation by mediating of market orientation. Interestingly, innovative organizational culture was found to have mediated the relationship between Knowledge-oriented leadership and innovation implementation of the ISPs in Mazandaran. Moreover, results indicate that effect of sales force market orientation on sales performance is fully mediated by innovation implementation.