Effective use of WebCT in a problem-based learning course for a dual mode delivery

As part of a radical curriculum change, the Faculty of Engineering and Surveying at the University of Southern Queensland (USQ) introduced a strand of four problem-based learning (PBL) courses in 2002 [1]. While the first of these courses concentrates on building team skills, the second in the strand, Engineering Problem Solving 2 (ENG2102), introduces statistical analysis, basic physics concepts and elements of GIS and mapping as part of the technical content. The annual enrolment in this course is about 250 including over 100 students studying this course externally. One of the important factors to be considered in the design and delivery of such course is to provide an effective e-learning system considering that a significant number of the students study through the distance mode

Melodrama and meaning

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Remote-sensing constraints on South America fire traits by Bayesian fusion of atmospheric and surface data

Satellite observations reveal substantial burning during the 2007 and 2010 tropical South America fire season, with both years exhibiting similar total burned area. However, 2010 CO fire emissions, based on satellite CO concentration measurements, were substantially lower (−28%), despite the once‐in‐a‐century drought in 2010. We use Bayesian inference with satellite measurements of CH_4 and CO concentrations and burned area to quantify shifts in combustion characteristics in 2010 relative to 2007. We find an 88% probability in reduced combusted biomass density associated with the 2010 fires and an 82% probability of lower fire carbon losses in 2010 relative to 2007. Higher combustion efficiency was a smaller contributing factor to the reduced 2010 CO emissions. The reduction in combusted biomass density is consistent with a reduction (4–6%) in Global Ozone Monitoring Experiment 2 solar‐induced fluorescence (a proxy for gross primary production) during the preceding months and a potential reduction in biomass (≤8.3%) due to repeat fires

Detection of fossil fuel emission trends in the presence of natural carbon cycle variability

Atmospheric CO₂ observations have the potential to monitor regional fossil fuel emission (FFCO₂) changes to support carbon mitigation efforts such as the Paris Accord, but they must contend with the confounding impacts of the natural carbon cycle. Here, we quantify trend detection time and magnitude in gridded total CO₂ fluxes—the sum of FFCO₂ and natural carbon fluxes—under an idealized assumption that monthly total CO₂ fluxes can be perfectly resolved at a 2°×2° resolution. Using Coupled Model Intercomparison Project 5 (CMIP5) 'business-as-usual' emission scenarios to represent FFCO₂ and simulated net biome exchange (NBE) to represent natural carbon fluxes, we find that trend detection time for the total CO₂ fluxes at such a resolution has a median of 10 years across the globe, with significant spatial variability depending on FFCO₂ magnitude and NBE variability. Differences between trends in the total CO₂ fluxes and the underlying FFCO₂ component highlight the role of natural carbon cycle variability in modulating regional detection of FFCO₂ emission trends using CO₂ observations alone, particularly in the tropics and subtropics where mega-cities with large populations are developing rapidly. Using CO₂ estimates alone at such a spatiotemporal resolution can only quantify fossil fuel trends in a few places—mostly limited to arid regions. For instance, in the Middle East, FFCO₂ can explain more than 75% of the total CO₂ trends in ~70% of the grids, but only ~20% of grids in China can meet such criteria. Only a third of the 25 megacities we analyze here show total CO₂ trends that are primarily explained (>75%) by FFCO₂. Our analysis provides a theoretical baseline at a global scale for the design of regional FFCO₂ monitoring networks and underscores the importance of estimating biospheric interannual variability to improve the accuracy of FFCO₂ trend monitoring. We envision that this can be achieved with a fully integrated carbon cycle assimilation system with explicit constraints on FFCO₂ and NBE, respectively

Adaptable and Collaborative Virtual Learning Environments Supporting Soft Skills

The adaptation of virtual learning environments (VLEs) into academic and business cultures has increasingly become a dominant factor in their operation. Often seen as avenues providing flexible solutions and attributing to new approaches in maintaining and applying learning opportunities, the implementation of e-learning has the potential to provide new approaches to satisfy the requirements of learning. However this powerful tool introduces an avalanche of ethical and social aspects to contend with. An incentive for the deployment of these environments is the promise of advancements provided through technology, such as improved usability and communication. There should be focuses on both the ethical and social aspects to deploying an educational community. Although their application is usually in a supportive role, alongside conventional teaching practices, the additional resources to maintain these environments can sometimes outweigh practicality and be limited when utilised. These environments should be seen as the opportunity to manifest wisdom and skill through innovative approaches and provide participants with efficient methods to self-reflect via each other. Are advancements in technology truly producing innovative and unconventional approaches, which provide a seamless transition from conventional classroom teaching? This paper examines the problems associated with the deployment of virtual learning environments to sustain knowledge through collaborative and adaptable learning and focuses on the problems associated with supporting soft skills online