درخت حوزه‌های تخصصی

The purpose of this research was to predict love stories based on attachment styles and self-differentiation in students. This was a cross-sectional correlational research. The statistical population of this research consists of students of Rasht university in 2022-2023. 223 people were selected by multi-stage cluster sampling method. The Love Stories Scale (Sternberg, 1996), Revised Adult Attachment Scale (Collins & Reid 1990), and Differentiation of self-inventory-short form (Drake, 2011), were used to collect the data. Data were analyzed using SPSS-24 software and multivariate regression analysis method The findings of the study showed that attachment styles and self-differentiation can predict the love as a story of students, and the contribution of self-differentiation components was higher (41.3%). The value of the standardized regression coefficient (Beta) for the reactivity component (β=0.243, P<0.01), my position (β=0.169, P<0.01), emotional fault (P<0.01, β=0.194), and mixing with others (P<0.01, β=0.203). Thus, it was concluded that self-differentiation positively and meaningfully predicts love stories in students. The results of the current research show the importance of the role of self-differentiation and attachment styles in students, which can be effective in designing counseling programs in university counseling centers.

Human society faces new challenges, especially the ones damaging the human brain and cognition. The addictive use of cyberspace essentially causes these challenges. This study was a comparative descriptive-analytical study conducted to diagnose cognitive impairments of cyber addiction through a QEEG test and to investigate the effectiveness of cognitive intervention on the cognitive functions involved in the mental imagery of adolescents. The statistical population of the study included 9-12 year-old students who visited Mehraz Andisheh clinic in Shiraz. The available sampling method was used and 40 people with research conditions (including 20 girls and 20 boys) were randomly assigned to two control and experimental groups. This research was conducted in 2023. The study used a mobile-based social network addiction diagnosis questionnaire (Ahmadi et al.; 2015), software (RehaCom et al., IVA-2, Neuroguide), an EEG device, and Kim Carrad visual memory test. After being selected and called to the place of research, people who had the conditions to enter the study first completed the consent form. Then, the  EEG recording was conducted for 5 minutes with eyes open and closed. Also, as a pre-test, they performed a visual attention test (IVA-2) for visual memory (Kim Carrad test) and a visual processing test (Captain Log). Then, for ten sessions, the participants in the experimental group received cognitive rehabilitation by RehaCom software, each lasting for 45 minutes with an interval of one day. After the completion of the treatment sessions, the evaluations were repeated. In this research data analysis was done using SPSS software 26 and Analysis of Covariance (ANCOVA). The study findings showed that the cognitive intervention led to a significant difference at the 0.05 level in the pre-test and post-test scores of the experimental group while there was no significant difference between the pre-test and the post-test in the control group. Based on the results, it can be seen that the intervention led to a difference in the pre-test and post-test and a significant difference between the pre-test and post-test groups in the experimental group at the 0.05 level. The study results also showed a significant relationship between the cognitive impairments of cyber addiction and brain wave patterns. These results emphasize that brain signals may be used as an indicator to diagnose cognitive impairment in people with cyber addiction. Also, the results showed that RehaCom software could improve the cognitive functions involved in the mental imagery of teenagers suffering from Internet addiction.

Quantitative electroencephalogram (QEEG) quantitatively records brain wave oscillation changes in any brain activity. Beta waves are produced during brain consciousness when an external stimulus induces computation, reasoning, attention, and logical thinking. Accordingly, the present study tried to measure beta wave changes during writing to analyze the changes of beta oscillatory changes while performing a writing task at the intermediate level. The study followed a quantitative method with the quasi-experimental design. To meet the purpose, thirty foreign language learners (15 males and 15 females) participated in this study voluntarily. They were doing writing tasks while their brain waves were recorded in the F3, F4, and Fz brain regions by applying the QEEG technique. The results reported positive significant differences on beta oscillatory activities in these brain areas, indicating the effectiveness of writing task activities in enhancing attention. The study has implications for language teachers to manipulate creative writing tasks as compositions to enhance attention as a prerequisite of learning and, as a result, beta waves and for the TEFL scholars to apply interdisciplinary approaches to uncover the effects of different tasks on brain oscillatory activities.

Job burnout results in decreased productivity and the depletion of human resources, leading to detrimental impacts on individuals, families, and society. This study aimed to explore the relationships among five key variables: stress, depression, anxiety, job burnout, and teachers' working memory. The study population comprised teachers from Zabul city during the 2022-2024 academic years. A sample of 120 teachers was selected using cluster random sampling for descriptive and correlational analyses. Working memory was assessed using the Working Memory Questionnaire (Daneman & Carpenter, 1980). Stress, anxiety, and depression were measured using the Depression, Anxiety, and Stress Scale (DASS-21). Job burnout was evaluated with the Maslach and Jackson's 1980 Burnout Inventory (MBI). The study utilized descriptive statistics and Pearson's correlation coefficient, as well as step-by-step regression analysis, to examine the relationships among stress, depression, anxiety, job burnout, and working memory. The findings indicated a strong correlation among these variables. Regression analysis showed that burnout was the most significant predictor of teachers' working memory compared to the other three variables.