Transforming Queensland VET : Challenges and Opportunities (Volume 1)

The volume is a collection of papers that address issues associated with change in the delivery of VET programs in Queensland, foreshadowed by the release of The Queensland Skill Plan in 2006. Issues that relate to the implementation of the Actions identified in the Queensland Skills Plan are the focus of the collection. In particular, the incorporation of Information Communication Technologies (ICTs) and e-learning approaches in the delivery of training packages is a key topic, how such change can be managed in the delivery of training programs, as well as broader professional development issues for VET practitioners. Change at an organisational level is the focus of two papers. Lyn Ambrose uses ideas from Diffusion of Innovations Theory to consider how the adoption eLearning in a TAFE community can be addressed. The paper by Susan Todhunter also discusses the organisational challenges in change initiatives in TAFE Institutes. Specific issues related to in the professional development of VET teachers are the focus of the papers by Mary Campbell, Sharon Altena, and Judy Gronold. Mary Campbell discusses the importance of building staff capabilities within the TAFE system and how this might be managed. Sharon Altena considers how professional development programs are currently delivered and how new approaches to professional development for TAFE teachers are needed to ensure changes can be sustained in teaching practice. The paper by Judy Gronold takes up a specific challenge for VET practitioners in the Queensland Skills Plan. She addresses issues related to embedding employability skills into training delivery in order to address industries’ need for flexible, multi-skilled productive workers. Mark Driver discusses the issues resulting from increased number of mature-aged learners in VET programs and how this change in the demographic profile of students presents challenges to the VET system. In the paper by David McKee, implications in the incorporation of ICTs into trade training are discussed and the need for effective change management strategies to ensure a smooth transition to new ways of delivering trade training. Finally, in the paper by David Roberts, the potential of Problem-Based Learning (PBL) approaches in VET training and the role of ICTs within such approaches are discussed. David uses horticulture training as an example to discuss the issues in implementing PBL effectively in VET programs. These papers were completed by the authors as a part of their postgraduate studies at QUT. The views reported are those of the authors and should not be attributed to the Queensland Department of Education, Training and the Arts.

Transforming Queensland VET : Challenges and Opportunities (volume 2)

This volume is the second in a series that addresses change and development in the delivery of vocational and education programs in Queensland. A similar volume was published in 2007. Considerable change was foreshadowed for TAFE Queensland by the release of The Queensland Skill Plan (QSP) in 2006. This volume addresses implementation issues for the Actions identified in the QSP. The chapters focus on a breadth of issues that relate to the changing landscape for teaching and learning in TAFE Institutes. The incorporation of Information Communication Technologies (ICTs) and e-learning approaches into the delivery of training packages remain key foci for change, as was evident in the first volume of this series. The chapters also consider issues for some client groups in VET, as well as approaches to professional development to build the capabilities of staff for new teaching and learning environments. The chapter by Sandra Lawrence examines the professional development issues for staff across TAFE institutes in the implementation of the Learning Management System. Suzanne Walsh discusses the issues of new “learning spaces” and “Mode 2 learning in the re-development at Southbank Institute. The chapter by Angela Simpson focuses on VET in schools and school-to-work transition programs. Josie Drew, in her chapter, takes up the issues of embedding employability skills into the delivery of training packages through flexible delivery. The chapter by Colleen Hodgins focuses on the organisational challenges for Lead Institutes in relation to the professional development for TAFE educators in light of policy changes. Bradley Jones discusses the changing roles of libraries in VET contexts and their importance. He examines the adequacy of the VOCED database and reflects on the current nature, role, and practices of VET libraries. Finally, Piero Dametto discusses the pragmatics for TAFE educators in understanding the use of digital objects and learning objects within the LMS and LCMS systems that were presaged in the QSP. These papers were completed by the authors as a part of their postgraduate studies at QUT. The views reported are those of the authors and should not be attributed to the Queensland Department of Education, Training and the Arts. Donna Berthelsen Faculty of Education Queensland University of Technology

The development and evaluation of an innovative nursing practice model to improve undergraduate nursing students' competence in paediatric physical assessment

Introduction The purpose of this study was to develop, implement and evaluate the impact of an educational intervention, comprising an innovative model of clinical decisionmaking and educational delivery strategy for facilitating nursing students‘ learning and development of competence in paediatric physical assessment practices. Background of the study Nursing students have an undergraduate education that aims to produce graduates of a generalist nature who demonstrate entry level competence for providing nursing care in a variety of health settings. Consistent with population morbidity and health care roles, paediatric nursing concepts typically form a comparatively small part of undergraduate curricula and students‘ exposure to paediatric physical assessment concepts and principles are brief. However, the nursing shortage has changed traditional nursing employment patterns and new graduates form the majority of the recruitment pool for paediatric nursing speciality staff. Paediatric nursing is a popular career choice for graduates and anecdotal evidence suggests that nursing students who select a clinical placement in their final year intend to seek employment in paediatrics upon graduation. Although concepts of paediatric nursing are included within undergraduate curriculum, students‘ ability to develop the required habits of mind to practice in what is still regarded as a speciality area of practice is somewhat limited. One of the areas of practice where this particularly impacts is in paediatric nursing physical assessment. Physical assessment is a fundamental component of nursing practice and competence in this area of practice is central to nursing students‘ development of clinical capability for practice as a registered nurse. Timely recognition of physiologic deterioration of patients is a key outcome of nurses‘ competent use of physical assessment strategies, regardless of the practice context. In paediatric nursing contexts children‘s physical assessment practices must specifically accommodate the child‘s different physiological composition, function and pattern of clinical deterioration (Hockenberry & Barrera, 2007). Thus, to effectively manage physical assessment of patients within the paediatric practice setting nursing students need to integrate paediatric nursing theory into their practice. This requires significant information processing and it is in this process where students are frequently challenged. The provision of rules or models can guide practice and assist novice-level nurses to develop their capabilities (Benner, 1984; Benner, Hooper-Kyriakidis & Stannard, 1999). Nursing practice models are cognitive tools that represent simplified patterns of expert analysis employing concepts that suit the limited reasoning of the inexperienced, and can represent the =rules‘ referred to by Benner (1984). Without a practice model of physical assessment students are likely to be uncertain about how to proceed with data collection, the interpretation of paediatric clinical findings and the appraisal of findings. These circumstances can result in ad hoc and unreliable nursing physical assessment that forms a poor basis for nursing decisions. The educational intervention developed as part of this study sought to resolve this problem and support nursing students‘ development of competence in paediatric physical assessment. Methods This study utilised the Context Input Process Product (CIPP) Model by Stufflebeam (2004) as the theoretical framework that underpinned the research design and evaluation methodology. Each of the four elements in the CIPP model were utilised to guide discrete stages of this study. The Context element informed design of the clinical decision-making process, the Paediatric Nursing Physical Assessment model. The Input element was utilised in appraising relevant literature, identifying an appropriate instructional methodology to facilitate learning and educational intervention delivery to undergraduate nursing students, and development of program content (the CD-ROM kit). Study One employed the Process element and used expert panel approaches to review and refine instructional methods, identifying potential barriers to obtaining an effective evaluation outcome. The Product element guided design and implementation of Study Two, which was conducted in two phases. Phase One employed a quasiexperimental between-subjects methodology to evaluate the impact of the educational intervention on nursing students‘ clinical performance and selfappraisal of practices in paediatric physical assessment. Phase Two employed a thematic analysis and explored the experiences and perspectives of a sample subgroup of nursing students who used the PNPA CD-ROM kit as preparation for paediatric clinical placement. Results Results from the Process review in Study One indicated that the prototype CDROM kit containing the PNPA model met the predetermined benchmarks for face validity and the impact evaluation instrumentation had adequate content validity in comparison with predetermined benchmarks. In the first phase of Study Two the educational intervention did not result in statistically significant differences in measures of student performance or self-appraisal of practice. However, in Phase Two qualitative commentary from students, and from the expert panel who reviewed the prototype CD-ROM kit (Study One, Phase One), strongly endorsed the quality of the intervention and its potential for supporting learning. This raises questions regarding transfer of learning and it is likely that, within this study, several factors have influenced students‘ transfer of learning from the educational intervention to the clinical practice environment, where outcomes were measured. Conclusion In summary, the educational intervention employed in this study provides insights into the potential e-learning approaches offer for delivering authentic learning experiences to undergraduate nursing students. Findings in this study raise important questions regarding possible pedagogical influences on learning outcomes, issues within the transfer of theory to practice and factors that may have influenced findings within the context of this study. This study makes a unique contribution to nursing education, specifically with respect to progressing an understanding of the challenges faced in employing instructive methods to impact upon nursing students‘ development of competence. The important contribution transfer of learning processes make to students‘ transition into the professional practice context and to their development of competence within the context of speciality practice is also highlighted. This study contributes to a greater awareness of the complexity of translating theoretical learning at undergraduate level into clinical practice, particularly within speciality contexts.

The ability of economic thinking : the “nature” versus “nurture” debate

In their studies, Eley and Meyer (2004) and Meyer and Cleary (1998) found that there are sources of variation in the affective and process dimensions of learning in mathematics and clinical diagnosis specific to each of these disciplines. Meyer and Shanahan (2002) argue that: General purpose models of student learning that are transportable across different discipline contexts cannot, by definition, be sensitive to sources of variation that may be subject-specific (2002. p. 204). In other words, to explain the differences in learning approaches and outcomes in a particular discipline, there are discipline-specific factors, which cannot be uncovered in general educational research. Meyer and Shanahan (2002) argue for a need to "seek additional sources of variation that are perhaps conceptually unique ... within the discourse of particular disciplines" (p. 204). In this paper, the development of an economics-specific construct (called economic thinking ability) is reported. The construct aims to measure discipline-sited ability of students that has important influence on learning in economics. Using this construct, economic thinking abilities of introductory and intermediate level economics students were measured prior to the commencement, and at the end, of their study over one semester. This enabled factors associated with students' pre-course economic thinking ability and their development in economic thinking ability to be investigated. The empirical findings will address the 'nature' versus 'nurture' debate in economics education (Frank, et aI., 1993; Frey et al., 1993; Haucap and Tobias 2003). The implications for future research in economics education will also be discussed.

GYAN: A methodology for rule extraction from artificial neural networks

Artificial neural network (ANN) learning methods provide a robust and non-linear approach to approximating the target function for many classification, regression and clustering problems. ANNs have demonstrated good predictive performance in a wide variety of practical problems. However, there are strong arguments as to why ANNs are not sufficient for the general representation of knowledge. The arguments are the poor comprehensibility of the learned ANN, and the inability to represent explanation structures. The overall objective of this thesis is to address these issues by: (1) explanation of the decision process in ANNs in the form of symbolic rules (predicate rules with variables); and (2) provision of explanatory capability by mapping the general conceptual knowledge that is learned by the neural networks into a knowledge base to be used in a rule-based reasoning system. A multi-stage methodology GYAN is developed and evaluated for the task of extracting knowledge from the trained ANNs. The extracted knowledge is represented in the form of restricted first-order logic rules, and subsequently allows user interaction by interfacing with a knowledge based reasoner. The performance of GYAN is demonstrated using a number of real world and artificial data sets. The empirical results demonstrate that: (1) an equivalent symbolic interpretation is derived describing the overall behaviour of the ANN with high accuracy and fidelity, and (2) a concise explanation is given (in terms of rules, facts and predicates activated in a reasoning episode) as to why a particular instance is being classified into a certain category.

A prioritised fuzzy constraint satisfaction approach to model agent negotiation for railway scheduling

With the recent regulatory reforms in a number of countries, railways resources are no longer managed by a single party but are distributed among different stakeholders. To facilitate the operation of train services, a train service provider (SP) has to negotiate with the infrastructure provider (IP) for a train schedule and the associated track access charge. This paper models the SP and IP as software agents and the negotiation as a prioritized fuzzy constraint satisfaction (PFCS) problem. Computer simulations have been conducted to demonstrate the effects on the train schedule when the SP has different optimization criteria. The results show that by assigning different priorities on the fuzzy constraints, agents can represent SPs with different operational objectives.

Patterns, heuristics for architectural design support: making use of evolutionary modelling in design

Software used by architectural and industrial designers – has moved from becoming a tool for drafting, towards use in verification, simulation, project management and project sharing remotely. In more advanced models, parameters for the designed object can be adjusted so a family of variations can be produced rapidly. With advances in computer aided design technology, numerous design options can now be generated and analyzed in real time. However the use of digital tools to support design as an activity is still at an early stage and has largely been limited in functionality with regard to the design process. To date, major CAD vendors have not developed an integrated tool that is able to both leverage specialized design knowledge from various discipline domains (known as expert knowledge systems) and support the creation of design alternatives that satisfy different forms of constraints. We propose that evolutionary computing and machine learning be linked with parametric design techniques to record and respond to a designer’s own way of working and design history. It is expected that this will lead to results that impact on future work on design support systems-(ergonomics and interface) as well as implicit constraint and problem definition for problems that are difficult to quantify.

Forecasting negative effects of monotony and sensation seeking on performance during a vigilance task

The driving task requires sustained attention during prolonged periods, and can be performed in highly predictable or repetitive environments. Such conditions could create hypovigilance and impair performance towards critical events. Identifying such impairment in monotonous conditions has been a major subject of research, but no research to date has attempted to predict it in real-time. This pilot study aims to show that performance decrements due to monotonous tasks can be predicted through mathematical modelling taking into account sensation seeking levels. A short vigilance task sensitive to short periods of lapses of vigilance called Sustained Attention to Response Task is used to assess participants‟ performance. The framework for prediction developed on this task could be extended to a monotonous driving task. A Hidden Markov Model (HMM) is proposed to predict participants‟ lapses in alertness. Driver‟s vigilance evolution is modelled as a hidden state and is correlated to a surrogate measure: the participant‟s reactions time. This experiment shows that the monotony of the task can lead to an important decline in performance in less than five minutes. This impairment can be predicted four minutes in advance with an 86% accuracy using HMMs. This experiment showed that mathematical models such as HMM can efficiently predict hypovigilance through surrogate measures. The presented model could result in the development of an in-vehicle device that detects driver hypovigilance in advance and warn the driver accordingly, thus offering the potential to enhance road safety and prevent road crashes.

Web searching interaction model based on user cognitive styles

As more and more information is available on the Web finding quality and reliable information is becoming harder. To help solve this problem, Web search models need to incorporate users’ cognitive styles. This paper reports the preliminary results from a user study exploring the relationships between Web users’ searching behavior and their cognitive style. The data was collected using a questionnaire, Web search logs and think-aloud strategy. The preliminary findings reveal a number of cognitive factors, such as information searching processes, results evaluations and cognitive style, having an influence on users’ Web searching behavior. Among these factors, the cognitive style of the user was observed to have a greater impact. Based on the key findings, a conceptual model of Web searching and cognitive styles is presented.

Ontology-based image annotation

With regard to the long-standing problem of the semantic gap between low-level image features and high-level human knowledge, the image retrieval community has recently shifted its emphasis from low-level features analysis to high-level image semantics extrac- tion. User studies reveal that users tend to seek information using high-level semantics. Therefore, image semantics extraction is of great importance to content-based image retrieval because it allows the users to freely express what images they want. Semantic content annotation is the basis for semantic content retrieval. The aim of image anno- tation is to automatically obtain keywords that can be used to represent the content of images. The major research challenges in image semantic annotation are: what is the basic unit of semantic representation? how can the semantic unit be linked to high-level image knowledge? how can the contextual information be stored and utilized for image annotation? In this thesis, the Semantic Web technology (i.e. ontology) is introduced to the image semantic annotation problem. Semantic Web, the next generation web, aims at mak- ing the content of whatever type of media not only understandable to humans but also to machines. Due to the large amounts of multimedia data prevalent on the Web, re- searchers and industries are beginning to pay more attention to the Multimedia Semantic Web. The Semantic Web technology provides a new opportunity for multimedia-based applications, but the research in this area is still in its infancy. Whether ontology can be used to improve image annotation and how to best use ontology in semantic repre- sentation and extraction is still a worth-while investigation. This thesis deals with the problem of image semantic annotation using ontology and machine learning techniques in four phases as below. 1) Salient object extraction. A salient object servers as the basic unit in image semantic extraction as it captures the common visual property of the objects. Image segmen- tation is often used as the �rst step for detecting salient objects, but most segmenta- tion algorithms often fail to generate meaningful regions due to over-segmentation and under-segmentation. We develop a new salient object detection algorithm by combining multiple homogeneity criteria in a region merging framework. 2) Ontology construction. Since real-world objects tend to exist in a context within their environment, contextual information has been increasingly used for improving object recognition. In the ontology construction phase, visual-contextual ontologies are built from a large set of fully segmented and annotated images. The ontologies are composed of several types of concepts (i.e. mid-level and high-level concepts), and domain contextual knowledge. The visual-contextual ontologies stand as a user-friendly interface between low-level features and high-level concepts. 3) Image objects annotation. In this phase, each object is labelled with a mid-level concept in ontologies. First, a set of candidate labels are obtained by training Support Vectors Machines with features extracted from salient objects. After that, contextual knowledge contained in ontologies is used to obtain the �nal labels by removing the ambiguity concepts. 4) Scene semantic annotation. The scene semantic extraction phase is to get the scene type by using both mid-level concepts and domain contextual knowledge in ontologies. Domain contextual knowledge is used to create scene con�guration that describes which objects co-exist with which scene type more frequently. The scene con�guration is represented in a probabilistic graph model, and probabilistic inference is employed to calculate the scene type given an annotated image. To evaluate the proposed methods, a series of experiments have been conducted in a large set of fully annotated outdoor scene images. These include a subset of the Corel database, a subset of the LabelMe dataset, the evaluation dataset of localized semantics in images, the spatial context evaluation dataset, and the segmented and annotated IAPR TC-12 benchmark.

Academic practice as explanatory framework : reconceptualising international student academic engagement and university teaching

This paper joins growing interest in the concept of practice, and uses it to reconceptualise international student engagement with the demands of study at an Australian university. Practice foregrounds institutional structures and student agency and brings together psychologically- and socially-oriented perspectives on international student learning approaches. Utilising discourse theory, practice is defined as habitual and individual instances of socially-contextualised configurations of elements such as actions and interactions, roles and relations, identities, objects, values, and language. In the university context, academic practice highlights the institutionally-sanctioned ways of knowing, doing and being that constitute academic tasks. The concept is applied here to six international students’ ‘readings’ of and strategic responses to academic work in a Master of Education course. It is argued that academic practice provides a comprehensive framework for explaining the interface between university academic requirements and international student learning, and the crucial role that teaching has in facilitating the experience.

Wellness in higher education : a transformative framework for health related disciplines

The perceived benefits of Wellness Education in University environments are substantiated by a number of studies in relation to the place, impact and purpose of Wellness curricula. Many authors recommend that Wellness curriculum design must include personal experiences, reflective practice and active self-managed learning approaches in order to legitimise the adoption of Wellness as a personal lifestyle approach. Wellness Education provides opportunities to engage in learning self-regulation skills both within and beyond the context of the Wellness construct. Learner success is optimised by creating authentic opportunities to develop and practice self regulation strategies that facilitate making meaning of life's experiences. Such opportunities include provision of options for self determined outcomes and are scaffolded according to learner needs; thus, configuring a learner-centred curriculum in Wellness Education would potentially benefit by overlaying principles from the domains of Self Determination Theory, Self Regulated Learning and Transformative Education Theory to highlight authentic, transformative learning as a lifelong approach to Wellness.

Overview of the INEX 2010 XML mining track : clustering and classification of XML documents

The XML Document Mining track was launched for exploring two main ideas: (1) identifying key problems and new challenges of the emerging field of mining semi-structured documents, and (2) studying and assessing the potential of Machine Learning (ML) techniques for dealing with generic ML tasks in the structured domain, i.e., classification and clustering of semi-structured documents. This track has run for six editions during INEX 2005, 2006, 2007, 2008, 2009 and 2010. The first five editions have been summarized in previous editions and we focus here on the 2010 edition. INEX 2010 included two tasks in the XML Mining track: (1) unsupervised clustering task and (2) semi-supervised classification task where documents are organized in a graph. The clustering task requires the participants to group the documents into clusters without any knowledge of category labels using an unsupervised learning algorithm. On the other hand, the classification task requires the participants to label the documents in the dataset into known categories using a supervised learning algorithm and a training set. This report gives the details of clustering and classification tasks.

Fast content-based file type identification

Digital forensic examiners often need to identify the type of a file or file fragment based only on the content of the file. Content-based file type identification schemes typically use a byte frequency distribution with statistical machine learning to classify file types. Most algorithms analyze the entire file content to obtain the byte frequency distribution, a technique that is inefficient and time consuming. This paper proposes two techniques for reducing the classification time. The first technique selects a subset of features based on the frequency of occurrence. The second speeds classification by sampling several blocks from the file. Experimental results demonstrate that up to a fifteen-fold reduction in file size analysis time can be achieved with limited impact on accuracy.

Convexity, Classification, and Risk Bounds

Many of the classification algorithms developed in the machine learning literature, including the support vector machine and boosting, can be viewed as minimum contrast methods that minimize a convex surrogate of the 0–1 loss function. The convexity makes these algorithms computationally efficient. The use of a surrogate, however, has statistical consequences that must be balanced against the computational virtues of convexity. To study these issues, we provide a general quantitative relationship between the risk as assessed using the 0–1 loss and the risk as assessed using any nonnegative surrogate loss function. We show that this relationship gives nontrivial upper bounds on excess risk under the weakest possible condition on the loss function—that it satisfies a pointwise form of Fisher consistency for classification. The relationship is based on a simple variational transformation of the loss function that is easy to compute in many applications. We also present a refined version of this result in the case of low noise, and show that in this case, strictly convex loss functions lead to faster rates of convergence of the risk than would be implied by standard uniform convergence arguments. Finally, we present applications of our results to the estimation of convergence rates in function classes that are scaled convex hulls of a finite-dimensional base class, with a variety of commonly used loss functions.