Southern drift: the learning styles of first and third year students of the built environment

This paper focuses on learning processes across the design curriculum of Deakin University School of Architecture and Building (Australia) through the recognition of the four learning styles - 'accommodating', 'diverging', 'assimilating' and 'converging' - that are defined in the Experiential Learning theory of Kolb. The research has been conducted to evaluate the effects of
learning style preferences on the performance of built environment students from diverse backgrounds and cultures in projects across a range of learning situations. The results of the research are being used to inform andragogical refinements that will be tested in design studio and technology lecture units studied by students of Architecture and Construction Management. The paper will focus on the results of a cross-curriculum learning style survey. The sUivey was conducted as part of a Strategic
Teaching and Learning Grant funded project currently running at Deakin as a reflexive research program aimed at resolving the learning difficulties of students collaborating in multi~disciplinary and multi~cultural team assignments. By addressing the issues of multidisciplinarity, cultural inclusiveness and the internationalisation of higher education, the research program aims ultimately at the education of graduates who are able to bring leadership to multidisciplinary design collaborations co-operating across international boundaries towards a global sustainable future.

Southern drift : the learning styles of first and third-year students of the built environment

This paper investigates learning processes across a built environment design curriculum through the recognition of the four learning styles defined in the experiential learning theory of Kolb, i.e., 'accommodating', 'diverging', 'assimilating' and 'converging.' The paper focuses on the results of a cross-curriculum learning style survey. The results of the survey appear to explain why many prior studies of the personality characteristics, learning and cognitive styles of practitioners and of design students at different stages of their education appear conflicting. The hypothesis tested to resolve these inconsistencies asked whether design-learning styles are fixed or change as students' progress through their studies. The survey provides evidence of a statistically significant relationship between learning styles and year of study. The evidence suggests a southern drift (the term refers to the spatial interrelationship of styles in the two-dimensional Kolb Learning Style Index [LSI] cycle) towards the abstract conceptualisation mode of the learning process as students near the completion of their studies. This fluidity in learning style remains a hypothesis until further research is able to study one cohort for the entirety of a degree program. The paper argues that the possibility of learning style fluidity needs determining if learning style theory is to provide a workable model for informing the teaching of architecture.

Learning style drift : correlation between built environment students' learning styles and the learning styles of their teachers

This paper focuses on the results of a cross-curriculum learning style survey conducted in an Australian School of Architecture and Building as part of an ongoing project aimed at resolving the learning difficulties of students collaborating in multi-disciplinary and multicultural team assignments. The research was conducted to determine how learning style differences in heterogeneous design teams might be addressed through pedagogy. We will argue that the likelihood of and reasons for learning style fluidity in student design cohorts needs determining if learning style theory is to provide a workable model for informing the teaching of design.
In light of evidence in student cohorts of learning style changes as students progress through their studies (Tucker, 2007), this research discusses one explanation of what appears to belearning style fluidity in architecture student cohorts. If, as prior research has indicated, the learning styles of academics are quite different from practitioners, evidence of a learning style drift in built environment students towards the predominant learning styles of their design teachers might suggest that students are learning how to be academics rather than practitioners. This, of course, might have serious implications for built environment teaching and for practice.

High current density and drift velocity in templated conducting polymers

Conducting polymers prepared by a templated vapour phase polymerisation process involving solid phase transition metal complexes are found to produce polymers with charge carriers that exhibit maximum drift velocity in the range of 1 m/s. This super-mobility seems to be related to a high degree of ordering in the materials as evidenced by the X-ray diffraction data. This may result from a templated polymerisation process. The high mobility manifests itself as a capacity to sustain very high current densities (>10000 A/cm2); such high current densities are of importance in thin film conductor applications.

Using multiple windows to track concept drift

In this paper we present a multiple window incremental learning algorithm that distinguishes between virtual concept drift and real concept drift. The algorithm is unsupervised and uses a novel approach to tracking concept drift that involves the use of competing windows to interpret the data. Unlike previous methods which use a single window to determine the drift in the data, our algorithm uses three windows of different sizes to estimate the change in the data. The advantage of this approach is that it allows the system to progressively adapt and predict the change thus enabling it to deal more effectively with different types of drift. We give a detailed description of the algorithm and present the results obtained from its application to two real world problems: background image processing and sound recognition. We also compare its performance with FLORA, an existing concept drift tracking algorithm.

Using selective memory to track concept drift effectively

In this paper we describe a supervised learning algorithm that uses selective memory to track concept drift. Unlike previous methods to track concept drift that use window heuristics to adapt to changes, we present an improved approach that discriminates between the instances observed. The advantage of this method is that it allows the system to both adapt to and track drift more accurately as well as filter the noise in the data more effectively. We present the algorithm and compare its performance with FLORA a well known concept drift tracking algorithm.

Limited population structure, genetic drift and bottlenecks characterise an endangered bird species in a dynamic, fire-prone ecosystem

Fire is a major disturbance process in many ecosystems world-wide, resulting in spatially and temporally dynamic landscapes. For populations occupying such environments, fire-induced landscape change is likely to influence population processes, and genetic patterns and structure among populations. The Mallee Emu-wren Stipiturus mallee is an endangered passerine whose global distribution is confined to fire-prone, semi-arid mallee shrublands in south-eastern Australia. This species, with poor capacity for dispersal, has undergone a precipitous reduction in distribution and numbers in recent decades. We used genetic analyses of 11 length-variable, nuclear loci to examine population structure and processes within this species, across its global range. Populations of the Mallee Emu-wren exhibited a low to moderate level of genetic diversity, and evidence of bottlenecks and genetic drift. Bayesian clustering methods revealed weak genetic population structure across the species' range. The direct effects of large fires, together with associated changes in the spatial and temporal patterns of suitable habitat, have the potential to cause population bottlenecks, serial local extinctions and subsequent recolonisation, all of which may interact to erode and homogenise genetic diversity in this species. Movement among temporally and spatially shifting habitat, appears to maintain long-term genetic connectivity. A plausible explanation for the observed genetic patterns is that, following extensive fires, recolonisation exceeds in-situ survival as the primary driver of population recovery in this species. These findings suggest that dynamic, fire-dominated landscapes can drive genetic homogenisation of populations of species with low-mobility and specialised habitat that otherwise would be expected to show strongly structured populations. Such effects must be considered when formulating management actions to conserve species in fire-prone systems.

Ontogenetic development of migration : Lagrangian drift trajectories suggest a new paradigm for sea turtles

Long distance migration occurs in a wide variety of taxa including birds, insects, fishes, mammals and reptiles. Here, we provide evidence for a new paradigm for the determinants of migration destination. As adults, sea turtles show fidelity to their natal nesting areas and then at the end of the breeding season may migrate to distant foraging sites. For a major rookery in the Mediterranean, we simulated hatchling drift by releasing 288 000 numerical particles in an area close to the nesting beaches. We show that the pattern of adult dispersion from the breeding area reflects the extent of passive dispersion that would be experienced by hatchlings. Hence, the prevailing oceanography around nesting areas may be crucial to the selection of foraging sites used by adult sea turtles. This environmental forcing may allow the rapid evolution of new migration destinations if ocean currents alter with climate change.

Asymmetric self-learning for tackling Twitter spam drift

Spam has become a critical problem on Twitter. In order to stop spammers, security companies apply blacklisting services to filter spam links. However, over 90% victims will visit a new malicious link before it is blocked by blacklists. To eliminate the limitation of blacklists, researchers have proposed a number of statistical features based mechanisms, and applied machine learning techniques to detect Twitter spam. In our labelled large dataset, we observe that the statistical properties of spam tweets vary over time, and thus the performance of existing ML based classifiers are poor. This phenomenon is referred as 'Twitter Spam Drift'. In order to tackle this problem, we carry out deep analysis of 1 million spam tweets and 1 million non-spam tweets, and propose an asymmetric self-learning (ASL) approach. The proposed ASL can discover new information of changed tweeter spam and incorporate it into classifier training process. A number of experiments are performed to evaluate the ASL approach. The results show that the ASL approach can be used to significantly improve the spam detection accuracy of using traditional ML algorithms.

Geospatial estimation-based auto drift correction in wireless sensor networks

Wireless sensor networks are often deployed in large numbers, over a large geographical region, in order to monitor the phenomena of interest. Sensors used in the sensor networks often suffer from random or systematic errors such as drift and bias. Even if they are calibrated at the time of deployment, they tend to drift as time progresses. Consequently, the progressive manual calibration of such a large-scale sensor network becomes impossible in practice. In this article, we address this challenge by proposing a collaborative framework to automatically detect and correct the drift in order to keep the data collected from these networks reliable. We propose a novel scheme that uses geospatial estimation-based interpolation techniques on measurements from neighboring sensors to collaboratively predict the value of phenomenon being observed. The predicted values are then used iteratively to correct the sensor drift by means of a Kalman filter. Our scheme can be implemented in a centralized as well as distributed manner to detect and correct the drift generated in the sensors. For centralized implementation of our scheme, we compare several krigingand nonkriging-based geospatial estimation techniques in combination with the Kalman filter, and show the superiority of the kriging-based methods in detecting and correcting the drift. To demonstrate the applicability of our distributed approach on a real world application scenario, we implement our algorithm on a network consisting of Wireless Sensor Network (WSN) hardware. We further evaluate single as well as multiple drifting sensor scenarios to show the effectiveness of our algorithm for detecting and correcting drift. Further, we address the issue of high power usage for data transmission among neighboring nodes leading to low network lifetime for the distributed approach by proposing two power saving schemes. Moreover, we compare our algorithm with a blind calibration scheme in the literature and demonstrate its superiority in detecting both linear and nonlinear drifts.

The Post-revision drift, analyst characteristics and investor attention

The aim of this dissertation is to provide a coherent explanation for the post-analyst recommendation drift. First, I find that the post-analyst recommendation drift is explained by the degree of attention paid by individual investors. Second I find that the extremeness and the credibility of information leads to changes in the degree of attention and a post-analyst recommendation drift. Finally, I find that the diffusion of private information contained in the analyst recommendation interacts with attention related biases leading to a post-recommendation drift.