Over the past five years, institutions of higher education in Australia and overseas have been investing increasingly larger sums of money in a range of e-learning initiatives. RMIT University, for example, has allocated AUS$50 million over the period 1999-2001 for aligning information technology to the needs of the core business of the university[1] and The University of Melbourne has allocated $12 million since 1997 for multimedia enhanced teaching and learning development[2]. This increased investment in e-learning initiatives appears to have occurred as a reaction to the view that higher education is in crisis. The crises centre around three issuesaccess to education, the cost of providing education, and dwindling public revenues (Daniel, 1997; Johnstone, 1992). Both authors believe that the use of information and communication technologies (ICT) in teaching and learning will provide at least part of the solution to many of these issues. Daniel (1997, p. 14), for example, believes that ''technology provides the most fertile ground for growing these key ingredients of university renewal: lower costs and unique attractions''. Bates (1997) believes there are four reasons for using technology in higher education: (1) improving the quality of learning; (2) improving access to education and training; (3) reducing the costs of education; and (4) improving the cost-effectiveness of education. Green and Gilbert (1995) noted:. .. the stated hope is that computing and information technologies will yield new levels of institutional and instructional ''productivity''. The stated expectation is that the infusion or integration of new technologies into instruction will, at minimum maintain and ideally enhance student learning while significantly reducing instructional costs. The second catalyst for the interest in e-learning appears to be centred around concern that higher education might not be able to continue its monopoly on the delivery of education. One area of potential competition is alleged to come from internation institutions of higher education, and an article in The Australian on 22 November 2000 claimed that Australian

ABSTRACT:A core goal for most learning analytic projects is to move from small-scale research towards broader institutional implementation, but this introduces a new set of challenges because institutions are stable systems, resistant to change. To avoid failure and maximize success, implementation of learning analytics at scale requires explicit and careful consideration of the entire TEL technology complex: the different groups of people involved, the educational beliefs and practices of those groups, the technologies they use, and the specific environments within which they operate. It is crucial not only to provide analytics and their associated tools, but also to begin with a clear strategic vision, assess institutional culture critically, identify potential barriers to adoption, develop approaches that can overcome these, and put in place appropriate forms of support, training, and community building. In this paper, we offer tools and case studies that will support educational institutions in deploying learning analytics at scale with the goal of achieving specified learning and teaching objectives. The ROMA Framework offers a step-by-step approach to the institutional implementation of learning analytics and this approach is grounded here by case studies of practice from the UK and Australia

Doctors use mobile devices to enhance efficiency in the workplace. In the current environment, doctors are making their own decisions based on balancing the risks and benefits of using mobile devices in the clinical setting. There is a need for guidelines around acceptable and ethical use that is patient-centred and that respects patient privacy.

Tonic sympathetic nerve activity (SNA) increases with age, but the mechanisms are unknown. There is evidence that SNA is positively related to total and abdominal body fat, which also increase with age. We tested the hypotheses that 1) the elevation in SNA with age is partially accounted for by higher abdominal and/or total body fat and 2) skeletal muscle is a target of the adiposity-related sympathetic effects. Direct microneurographic recordings of skeletal muscle SNA (MSNA) were obtained during supine rest in 16 older (64 +/- 1 yr, means +/- SE) and 16 young (24 +/- 1 yr) adult males. Central body fat was estimated by waist circumference (WC) and fat mass (FM) by hydrostatic weight. MSNA, WC, and FM were higher in the older vs. young males (44 +/- 2 vs. 22 +/- 2 bursts/min, 91 +/- 2 vs. 79 +/- 1 cm, and 19 +/- 2 vs. 9 +/- 1 kg, respectively; all P < 0.0001). Although univariate correlations were high for MSNA and both WC (r = 0.77) and FM (r = 0.75), stepwise multiple regression analysis revealed WC to be the best predictor of MSNA (R2 = 0.60, P < 0.0001), with FM explaining only an additional 2% of the variance (not significant). Statistically covarying for WC reduced but did not eliminate the difference in adjusted age-group means for MSNA (39 +/- 3 vs. 26 +/- 2 bursts/min, P = 0.003). We conclude that 1) the elevated SNA in older adults is partially related to higher body fat, particularly in the abdominal region, and 2) skeletal muscle is a target of the adiposity-related sympathetic effects observed with aging.