Relationship between Students’ Critical Thinking and Self-efficacy Beliefs in Ferdowsi University of Mashhad, Iran

AbstractNowadays, critical thinking and motivational factors affecting it, such as self-efficacy have been heavily regarded by higher education systems. This descriptive-correlation study aimed to investigate the relationship between students’ self-efficacy and critical thinking in Ferdowsi University of Mashhad, Iran. A random sample of 216 students completed Sherer et al.’s (1982) General Self-efficacy Scale and the California Critical Thinking Skills Test- Form B (1994). Finding showed a significantly positive relationship between students’ self-efficacy and critical thinking (r= 0.21, p< 0.001). Hence, self-efficacy as motivational factor should be considered for developing learners’ critical thinking skills

Study the relationship between emotional intelligence and how to apply the standards of effective teaching by faculty Medical Sciences

Introduction: The ability to set and manage faculty members’ emotions in classrooms is an important and determinant factor in successful and effective teaching. The main objective of this study was to determine the relationship between emotional intelligence and effectiveness of teaching and implementation of effective teaching components by faculty members of Mazandaran University of Medical Sciences. Methods: This descriptive study was conducted on 393 faculty members of Mazandaran University of Medical Sciences who were teaching in 2012-2013 academic years. A sample of 191 faculty members was selected according to krejcie and Morgan’s sample size table. Research tools were a research-made effective teaching questionnaire and Bar-Ann emotional intelligence questionnaire. The collected data were analyzed using descriptive statistics, Pearson Correlation and Regression analysis. Results: The findings showed a significant relationship between faculty members’ emotional intelligence and their effective teaching (p< 0.05). Their emotional intelligence was able to predict the effective teaching with a significant coefficient (ß=0.78, p<0.0005). There was a significant correlation between faculty members’ emotional intelligence and implementation of all the components of effective teaching (teaching design, education, classroom management, human relations, assessment, and desired personality traits) (P<0.05). Conclusion: The results indicated that by controlling variables such as age, gender, and education of faculty members, emotional intelligence can strongly predict the effectiveness of their teaching. In other words, faculty members` high emotional intelligence could be a determining factor for their effective teaching

A Decomposition Method for Solving q-Difference Equations

The q-difference equations are important in q-calculus. In this paper, we apply the iterative method which is suggested by Daftardar and Jafari, hereafter called the Daftardar-Jafari method, for solving a type of q-partial differential equations. We discuss the convergency of this method. In the implementation of this technique according to other iterative methods such as Adomian decomposition and homotopy perturbation methods, one does not need the calculation of the Adomian’s polynomials for nonlinear terms. It is proven that under a special constraint, the given result by this method converges to exact solution of nonlinear q-ordinary or q-partial differential equations

Learning Styles and Their Correlation with Self-Directed Learning Readiness in Nursing and Midwifery Students

Introduction: Self directed learning has become a focus of nursing education in the past few decades due to the developmental changes in Nursing and Midwifery. Also one of the factors effecting students' learning is their learning style. This study investigates the preferred learning style and its role in self-directed learning readiness in Nursing and Midwifery students. Methods: The statistical population in this correlation descriptive study included all undergraduate students (N=550) of Nursing and Midwifery in Mashhad University of Medical Sciences. A randomly stratified sample of 214 (%27 males and %73 females) received Kolb’s Learning Styles Inventory and Fisher’s Self-Directed Learning Readiness Scale (SDLRS). For statistical analysis, descriptive statistics (Mean, and standard deviation) and inferential statistics techniques (t test, X2 , MANOVA, ANOVA) were used. Results: Response rate was %87. Mean scores of SDLRS in students according to their preferred learning styles were 177.12±30.23, 176.75±21.9, 176.33±18.95 and 186.14±17.55 for divergent, accommodative, assimilative, and convergent styles, respectively. According to ANOVA, these mean scores were not significantly different. Chi2 test showed that while there were significant differences between the frequencies of preferred styles, there was not a significant difference in the prioritized styles between male and female students. The results of t test and MANOVA indicated that there was no significant difference between male and female students in total SDLRS scores and its subscales. Conclusion: The first priority of the majority of students was assimilative style and also there were no significant gender differences in learning styles and SDLRS scores. Accordingly, it is recommended to more adapt and adjust teaching methods to these learning traits in students

Artificial Intelligence in Cancer Care: From Diagnosis to Prevention and Beyond

&lt;p&gt;Artificial Intelligence (AI) has made significant strides in revolutionizing cancer care, encompassing various aspects from diagnosis to prevention and beyond. With its ability to analyze vast amounts of data, recognize patterns, and make accurate predictions, AI has emerged as a powerful tool in the fight against cancer. This article explores the applications of AI in cancer care, highlighting its role in diagnosis, treatment decision-making, prevention, and ongoing management. In the realm of cancer diagnosis, AI has demonstrated remarkable potential. By processing patient data, including medical imaging, pathology reports, and genetic profiles, AI algorithms can assist in early detection and accurate diagnosis. Image recognition algorithms can analyze radiological images, such as mammograms or CT scans, to detect subtle abnormalities and assist radiologists in identifying potential tumors. AI can also aid pathologists in analyzing tissue samples, leading to more precise and efficient cancer diagnoses. AI's impact extends beyond diagnosis into treatment decision-making. The integration of AI algorithms with clinical data allows for personalized treatment approaches. By analyzing patient characteristics, disease stage, genetic markers, and treatment outcomes, AI can provide valuable insights to oncologists, aiding in treatment planning and predicting response to specific therapies. This can lead to more targeted and effective treatment strategies, improving patient outcomes and reducing unnecessary treatments and side effects. Furthermore, AI plays a crucial role in cancer prevention. By analyzing genetic and environmental risk factors, AI algorithms can identify individuals at higher risk of developing certain cancers. This enables targeted screening programs and early interventions, allowing for timely detection and prevention of cancer. Additionally, AI can analyze population-level data to identify trends and patterns, contributing to the development of public health strategies for cancer prevention and control. AI's involvement in cancer care goes beyond diagnosis and treatment, encompassing ongoing management and survivorship. AI-powered systems can monitor treatment response, track disease progression, and detect recurrence at an early stage. By continuously analyzing patient data, including imaging, laboratory results, and clinical assessments, AI algorithms can provide real-time insights, facilitating timely interventions and adjustments to treatment plans. This proactive approach to disease management improves patient outcomes and enhances quality of life.&lt;/p&gt