This study focuses on the examination of the relationship between resilience, optimism and social support among international students. International students who are studying as foreign students tend to experience greater stress and anxiety during their study. They need to adjust to the new environment and overcome challenges. The resilience level of students is very important as it can help them adjust their life pressures and stresses. The current study aims to look at the levels and patterns of resilience, optimism and social support among international students. The instruments used were Connor-Davidson Resilience Scale (CD-RISC) for resilience, Life Orientation Test Revised (LOT-R) for optimism and Multidimensional Scale of Perceived Social Support (MSPSS) for social support. A total of 291 international students were involved in the study. The findings revealed a significant difference in the resiliency level across races, with African students scoring higher than others. The regression analysis employed showed that optimism (B=. 593) and social support (B=. 204) are significant predictors of resilience.
The present study aims to examine whether self-efficacy mediates the relationship between optimism, dimensions of psychological well-being, and resilience among Iranian students. The participants in this study included 251 Iranian students from Universiti Putra Malaysia (UPM). Structural equation modeling using AMOS 20.0 was used to analyze the data. The results indicated that there were significant relationships between optimism, dimensions of psychological well-being, and resilience among Iranian students of UPM. The study findings presented that self-efficacy mediated the relationship between dimensions of psychological well-being (environmental mastery, autonomy, self-acceptance, positive relations with others, personal growth, and purpose in life) and resilience among Iranian students of UPM. Furthermore, self-efficacy was not observed to mediate the influence of optimism on resilience among Iranian students of UPM. The study’s findings help to understand the interrelationship between self-efficacy, various dimensions of psychological well-being, and resilience. Consequently, counselors, psychologists, and instructors can develop and plan valuable strategies to enhance students’ psychological factors.
This study aims to investigate the relationship between the components of motivation in self-regulated learning as well as the components of learning strategies in self-regulated learning and academic procrastination. Academic procrastination creates problems for undergraduates such as stress and poor academic performance which should be investigated as a serious issue in the educational context. The participants in this study included 100 undergraduates in Universiti Putra Malaysia. The result of a Pearson correlation analysis revealed intrinsic goal orientation, task values, rehearsal, elaboration, meta cognitive selfregulation, resource management strategies, organisation and critical thinking as self-regulated learning components that have significant negative correlations with academic procrastination. In addition, anxiety was found to have a significant positive correlation with academic procrastination. Extrinsic goal orientation and control of learning beliefs were not significantly correlated to academic procrastination. The findings suggested that in order to cope with academic procrastination, an academic procrastinator might consider being a self-regulated learner as most of the components of self-regulated learning indicated a strong relationship with academic procrastination that can be encouraged in order to improve those lacking components of self-regulated learning. Also, to help undergraduates to improve on the components of self-regulated learning that they lack, strategies can be planned by educators to deal with academic procrastination and to increase academic performance.
Craniofacial anomaly including deformational plagiocephaly as a result of deformities in head and facial bones evolution is a serious health problem in newbies. The impact of such condition on the affected infants is profound from both medical and social viewpoint. Indeed, timely diagnosing through different medical examinations like anthropometric measurements of the skull or even Computer Tomography (CT) image modality followed by a periodical screening and monitoring plays a vital role in treatment phase. In this paper, a classification model for detecting and monitoring deformational plagiocephaly in affected infants is presented. The presented model is based on a deep learning network architecture. The given model achieves high accuracy of 99.01% with other classification parameters. The input to the model are the images captured by commonly used smartphone cameras which waives the requirement to sophisticated medical imaging modalities. The method is deployed into a mobile application which enables the parents/caregivers and non-clinical experts to monitor and report the treatment progress at home.
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