Using problem based learning to teach future Australian IS professionals

Teaching Information Systems (IS) to Australian tertiary students has become increasing problematic with many of them relying on a surface level approach to study. This will surely affect their understanding of IS material and in turn affect their effectiveness in the workplace.

This paper examines the issues behind this trend and considers Problem Based Learning (PBL) asan aid to counteract it.

Learning cultures of problem-based learning teams

The learning experiences of first-year engineering students to a newly implemented engineering problem-based learning (PBL) curriculum is reported here, with an emphasis on student approaches to learning. Ethnographic approaches were used for data collection and analysis. This study found that student learning in a PBL team in this setting was mainly influenced by the attitudes, behaviour and learning approaches of the student members in that team. Three different learning cultures that emerged from the analysis of eight PBL teams are reported here. They are the finishing culture, the performing culture and the collaborative learning culture. It was found that the team that used a collaborative approach to learning benefited the most in this PBL setting. Students in this team approached learning at a deep level. The findings of this study imply that students in a problem-based, or project-based, learning setting may not automatically adopt a collaborative learning culture. Hence, it is important for institutions and teachers to identify and consider the factors that influence student learning in their particular setting, provide students with necessary tools and ongoing coaching to nurture deep learning approaches in PBL teams.

Sustainable in 2030? The impact of population growth in Melbourne`s inner activity centres

The advent of sustainable approaches to managing an increase of population in our urban centres, such as the Melbourne 2030 planning policy, has led to questions regarding their successful implementation at local government level. Issues relating to the location of sustainable built form and infrastructure are of particular importance considering Melbourne 2030's direction regarding intensification around existing activity nodes. The following paper embarks on an investigation into the impact of the projected population growth set out in the 2030 policy, focusing particularly on the consequent implications of increased residential densities in and around activity centres within the inner Melbourne region. Utilising various mapping techniques, a series of comparative built form/density scenarios will be generated that begin to explore the issues of implementation faced at a local government level.

Strategic leadership of teaching and learning centres : from reality to ideal

This paper reports on the third phase of a study of Australian Teaching and Learning Centres to identify factors that contribute to the effective strategic leadership of Centres. Focus groups at 10 Australian universities included 66 respondents, providing a diverse range of perspectives, from students to members of the university executive. Analysis of participant contributions extended findings from prior project phases and the wider literature. They also contributed to the final construction of the strategic leadership Teaching and Learning Centre maturity framework presented here. Centres remain in a state of flux, enduring regular reconfiguration. For most Centres, their level of interaction with students is low and increased engagement with students would be of benefit. Perceptions of Centres vary widely, reinforcing the importance of a strategic partnership between the University’s Senior Executive, the Centre and faculties as a prerequisite for implementing identified high-impact strategies for improvement in teaching and learning.

Surface coverage of filter medium in deep bed filtration : mathematical modelling and experiments

This paper highlights the importance of surface coverage in modeling the removal of particles in deep bed filtration. A model that considers the saturation of sites on which particle deposition occurs is used. Experimental results obtained with monodispersed suspensions of 0.46 and 0.816 μm latex particles at different influent concentrations and ionic strengths were used to calculate the fraction of filter grain surface (β1) on which actual particle deposition occurs. This will be useful in evaluating the filter performance in terms of the utilization of available surface area of the filter medium. Further, the level of dendrite formation of particles on filter grains during filtration is expressed in terms of β1 and the specific surface coverage, θT (the fraction of a filter grain surface that is covered by particles at time T, assuming that the filter grain is covered by a monolayer of particles). This can be used to compare the contribution of deposited particles in the removal efficiency of deep bed filtration for suspensions with different physical and chemical characteristics.

RF rectifiers for EM power harvesting in a deep brain stimulating device

A passive deep brain stimulation (DBS) device can be equipped with a rectenna, consisting of an antenna and a rectifier, to harvest energy from electromagnetic fields for its operation. This paper presents optimization of radio frequency rectifier circuits for wireless energy harvesting in a passive head-mountable DBS device. The aim is to achieve a compact size, high conversion efficiency, and high output voltage rectifier. Four different rectifiers based on the Delon doubler, Greinacher voltage tripler, Delon voltage quadrupler, and 2-stage charge pumped architectures are designed, simulated, fabricated, and evaluated. The design and simulation are conducted using Agilent Genesys at operating frequency of 915 MHz. A dielectric substrate of FR-4 with thickness of 1.6 mm, and surface mount devices (SMD) components are used to fabricate the designed rectifiers. The performance of the fabricated rectifiers is evaluated using a 915 MHz radio frequency (RF) energy source. The maximum measured conversion efficiency of the Delon doubler, Greinacher tripler, Delon quadrupler, and 2-stage charge pumped rectifiers are 78, 75, 73, and 76 % at -5 dBm input power and for load resistances of 5-15 kΩ. The conversion efficiency of the rectifiers decreases significantly with the increase in the input power level. The Delon doubler rectifier provides the highest efficiency at both -5 and 5 dBm input power levels, whereas the Delon quadrupler rectifier gives the lowest efficiency for the same inputs. By considering both efficiency and DC output voltage, the charge pump rectifier outperforms the other three rectifiers. Accordingly, the optimised 2-stage charge pumped rectifier is used together with an antenna to harvest energy in our DBS device.

Dealing with alcohol-related problems in the night-time economy: a study protocol for mapping trends in harm and stakeholder views surrounding local community level interventions

This project will provide a comprehensive investigation into the prevalence of alcohol-related harms and community attitudes in the context of community-based interventions being implemented to reduce harm in two regional centres of Australia. While considerable experimentation and innovation to address these harms has occurred in both Geelong and Newcastle, only limited ad-hoc documentation and analysis has been conducted on changes in the prevalence of harm as a consequence, leaving a considerable gap in terms of a systematic, evidence-based analysis of changes in harm over time and the need for further intervention. Similarly, little evidence has been reported regarding the views of key stakeholder groups, industry, government agencies, patrons or community regarding the need for, and the acceptability of, interventions to reduce harms. This project will aim to provide evidence regarding the impact and acceptability of local initiatives aimed at reducing alcohol-related harms.

Thermal comfort of multiple user groups in indoor aquatic centres

Aquatic centres are popular recreational facilities in Australia and other developed countries. These buildings have experienced exponential demand over the past few decades. The growing desire for better indoor environmental quality in aquatic centres has resulted in a marked increase in energy consumption in this sector. With the existence of multiple user groups, achieving thermal comfort has always been challenging. Even though several thermal comfort studies are conducted in other building types, such studies are very limited with respect to aquatic centres. This paper analyses the thermal comfort conditions of various user groups in seven aquatic centres in Australia. Comfort measurements are performed through monitoring environmental parameters and surveying swimmers, staff and spectators. The results revealed the variation of air temperatures among the buildings, resulting in high level of thermal discomfort for the spectators and staff in some of the buildings. The thermal sensation of the staff and spectators had good correlation with the indoor temperatures and PMVs. Altering temperature settings according to the seasons will help to improve the comfort with respect to the adaptation and expectation of the occupants.

DeepCare: a deep dynamic memory model for predictive medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.

Investigating energy and indoor environmental performance of aquatic centres

Aquatic centres are popular recreational facilities in Australia and other developed countries. These buildings have experienced exponential demand over the past few decades. The growing desire for better indoor environmental quality in aquatic centres has resulted in a marked increase in energy consumption in this sector. Community expectations in relation to aquatic centres are rising and these spaces are associated with wellness and health. Energy consumption in indoor swimming pool buildings is high due to the high indoor air temperatures, increased ventilation heat losses and the need to disinfect water. This study investigates the energy consumption and indoor environmental quality of seven aquatic centres in Australia. The construction and various energy consuming systems of the facilities are analysed and compared against the energy consumption. Thermal comfort data is collected through measuring the indoor environmental parameters. Building envelopes were found to be leaky in most of the buildings resulting in energy wastage. The main indicators for energy consumption were gross floor area, area of pool surface, and number of visitors. It was found that the set point temperatures were significantly high in some of the buildings resulting in high level of discomfort for the spectators and staff.

Effects of low- versus high-fidelity simulations on the cognitive burden and performance of entry-level paramedicine students: a mixed-methods comparison trial using eye-tracking, continuous heart rate, difficulty rating scales, video observation and inte

INTRODUCTION: High-fidelity simulation-based training is often avoided for early-stage students because of the assumption that while practicing newly learned skills, they are ill suited to processing multiple demands, which can lead to "cognitive overload" and poorer learning outcomes. We tested this assumption using a mixed-methods experimental design manipulating psychological immersion. METHODS: Thirty-nine randomly assigned first-year paramedicine students completed low- or high-environmental fidelity simulations [low-environmental fidelity simulations (LFenS) vs. high-environmental fidelity simulation (HFenS)] involving a manikin with obstructed airway (SimMan3G). Psychological immersion and cognitive burden were determined via continuous heart rate, eye tracking, self-report questionnaire (National Aeronautics and Space Administration Task Load Index), independent observation, and postsimulation interviews. Performance was assessed by successful location of obstruction and time-to-termination. RESULTS: Eye tracking confirmed that students attended to multiple, concurrent stimuli in HFenS and interviews consistently suggested that they experienced greater psychological immersion and cognitive burden than their LFenS counterparts. This was confirmed by significantly higher mean heart rate (P < 0.001) and National Aeronautics and Space Administration Task Load Index mental demand (P < 0.05). Although group allocation did not influence the proportion of students who ultimately revived the patient (58% vs. 30%, P < 0.10), the HFenS students did so significantly more quickly (P < 0.01). The LFenS students had low immersion resulting in greater assessment anxiety. CONCLUSIONS: High-environmental fidelity simulation engendered immersion and a sense of urgency in students, whereas LFenS created assessment anxiety and slower performance. We conclude that once early-stage students have learned the basics of a clinical skill, throwing them in the "deep end" of high-fidelity simulation creates significant additional cognitive burden but this has considerable educational merit.

Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks

Network traffic analysis has been one of the most crucial techniques for preserving a large-scale IP backbone network. Despite its importance, large-scale network traffic monitoring techniques suffer from some technical and mercantile issues to obtain precise network traffic data. Though the network traffic estimation method has been the most prevalent technique for acquiring network traffic, it still has a great number of problems that need solving. With the development of the scale of our networks, the level of the ill-posed property of the network traffic estimation problem is more deteriorated. Besides, the statistical features of network traffic have changed greatly in terms of current network architectures and applications. Motivated by that, in this paper, we propose a network traffic prediction and estimation method respectively. We first use a deep learning architecture to explore the dynamic properties of network traffic, and then propose a novel network traffic prediction approach based on a deep belief network. We further propose a network traffic estimation method utilizing the deep belief network via link counts and routing information. We validate the effectiveness of our methodologies by real data sets from the Abilene and GÉANT backbone networks.