Law student lifestyle pressures

One significant factor influencing student wellbeing is the degree to which their studies are subject to external lifestyle pressures. These pressures are relieved or exacerbated by choices students make around their approaches to study, and the amount of time they devote to work and leisure. This Chapter considers results from a 2012 survey of law students at the University of New South Wales (UNSW), Sydney, Australia. Those results are compared to results from a similar US law student survey, and comparable data from the UK and Australia more broadly. In addition, the UNSW study compares key lifestyle choices of undergraduate (LLB) and graduate (JD) law students. The significance of the analysis in this Chapter for understanding law students’ wellbeing is that comparing American and Australian law students’ lifestyle patterns provides insights into contextual variation between both groups, which is important to bear in mind when comparing American and Australian research on law students’ wellbeing, and appreciating the limits of such comparisons. In particular, much of the wellbeing literature to date has focused on course-based stressors, but in light of recent research indicating that improvements in students’ course-based experiences may not have a direct effect on law students’ elevated levels of psychological distress, it is important to understand the broader life pressures and stressors that may be impacting law students’ wellbeing.

Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling

Deep convolutional neural networks (DCNNs) have been employed in many computer vision tasks with great success due to their robustness in feature learning. One of the advantages of DCNNs is their representation robustness to object locations, which is useful for object recognition tasks. However, this also discards spatial information, which is useful when dealing with topological information of the image (e.g. scene labeling, face recognition). In this paper, we propose a deeper and wider network architecture to tackle the scene labeling task. The depth is achieved by incorporating predictions from multiple early layers of the DCNN. The width is achieved by combining multiple outputs of the network. We then further refine the parsing task by adopting graphical models (GMs) as a post-processing step to incorporate spatial and contextual information into the network. The new strategy for a deeper, wider convolutional network coupled with graphical models has shown promising results on the PASCAL-Context dataset.

Rural first year university students: As engaged, aspirational and motivated as anyone – but different science ‘choices’ in year 12 and university

This paper reports on findings from the Interests and Recruitment in Science study, which explored the experiences of first year students studying science, technology, engineering and mathematics (STEM) courses in Australian universities. First year STEM students who went to school in rural or regional areas were as engaged, aspirational and motivated as their more metropolitan counterparts. However, they were less likely to have studied physics or advance mathematics, and more likely to have enrolled in an Agricultural or Environmental Science degree. The relationships between these results and broader contextual issues such as employment and Higher Education budgetary and policy settings are discussed.

The selection, optimization, and compensation model in the work context: A systematic review and meta-analysis of two decades of research

Over the past two decades, the selection, optimization, and compensation (SOC) model has been applied in the work context to investigate antecedents and outcomes of employees' use of action regulation strategies. We systematically review, meta-analyze, and critically discuss the literature on SOC strategy use at work and outline directions for future research and practice. The systematic review illustrates the breadth of constructs that have been studied in relation to SOC strategy use, and that SOC strategy use can mediate and moderate relationships of person and contextual antecedents with work outcomes. Results of the meta-analysis show that SOC strategy use is positively related to age (rc = .04), job autonomy (rc = .17), self-reported job performance (rc = .23), non-self-reported job performance (rc = .21), job satisfaction (rc = .25), and job engagement (rc = .38), whereas SOC strategy use is not significantly related to job tenure, job demands, and job strain. Overall, our findings underline the importance of the SOC model for the work context, and they also suggest that its measurement and reporting standards need to be improved to become a reliable guide for future research and organizational practice.

Context-based assessment: Creating opportunities for resonance between classroom fields and societal fields

There is on-going international interest in the relationships between assessment instruments, students’ understanding of science concepts and context-based curriculum approaches. This study extends earlier research showing that students can develop connections between contexts and concepts – called fluid transitions – when studying context-based courses. We provide an in-depth investigation of one student’s experiences with multiple contextual assessment instruments that were associated with a context-based course. We analyzed the student’s responses to context-based assessment instruments to determine the extent to which contextual tests, reports of field investigations, and extended experimental investigations afforded her opportunities to make connections between contexts and concepts. A system of categorizing student responses was developed that can inform other educators when analyzing student responses to contextual assessment. We also refine the theoretical construct of fluid transitions that informed the study initially. Implications for curriculum and assessment design are provided in light of the findings.