درخت حوزه‌های تخصصی

The purpose of this study was to examine the role of moral intelligence and spiritual intelligence in predicting organizational flourishing and job performance. This research was applied in terms of purpose, and descriptive-correlational with regression analysis in terms of methodology. The statistical population consisted of 200 administrative employees of the Education Department of Zahedan, from which 127 individuals were selected through simple random sampling based on Morgan's table. Data were collected using the Moral Intelligence Questionnaire by Lennick, Kiel, and Jordan (2005), the Spiritual Intelligence Questionnaire by Emmons (2000), the Organizational Flourishing Questionnaire by Spreitzer (2007), and the Job Performance Questionnaire by Patterson (1922). For data analysis, descriptive statistical methods and inferential statistics (Pearson correlation and multiple regression) were employed using SPSS 22 software. The findings revealed that there are positive and significant relationships between moral intelligence and spiritual intelligence with organizational flourishing and job performance. The results of regression analysis showed that moral intelligence and spiritual intelligence jointly explained 53% of the variance in job performance and 50% of the variance in organizational flourishing. Moral intelligence was found to be a stronger predictor of job performance, while spiritual intelligence was a more effective predictor of organizational flourishing. These findings indicate that moral intelligence and spiritual intelligence play distinct yet complementary roles in improving individual and organizational performance. The development of both types of intelligence is essential for achieving optimal performance and sustainable flourishing in educational organizations.

Online learning programs have gained significant popularity in recent years. However, despite their widespread adoption, completion and success rates for online courses are notably lower than those for traditional in-person education. If students' final academic performance could be predicted early by analyzing their behavior within the virtual learning environment, timely alerts could be issued, and targeted interventions could be recommended to prevent underperformance and course abandonment. Previous studies have predicted academic performance using various features, such as demographic data, academic history, in-term exam results, and assignment assessments. However, many online learning platforms do not provide access to such data, rendering these methods ineffective. This study focuses on the early prediction of students' academic performance by extracting novel behavioral features based on their interactions with the online learning platform. To develop robust predictive models, we utilize an integrated approach combining multiple feature selection methods to extract the most informative interaction patterns, followed by application of advanced machine learning algorithms including ensemble learning techniques and artificial neural networks (ANNs). The evaluation results demonstrate that our proposed approach can predict students' final academic performance with an accuracy of 90.62%, using only data collected during the first third of the online course.