The influences of conceptions of learning and learner self-concept on high school students' approaches to learning

This study investigated the mediating effect of learner selfconcept between conceptions of learning and students' approaches to learning using structural equation modelling. Data were collected using a modified version of Biggs' Learning Process Questionnaire, together with the recently developed 'What is Learning Survey' and 'Learner Self-Concept Scale'. A sample of 355 high school students participated in the study. Results indicate that learner self-concept does mediate between conceptions of meaning and approaches to learning. Students who adopted a deep approach liked learning new things and indirectly viewed learning as experiential, involving social interaction and directly viewed learning as personal development. Implications for teachers are discussed, with consideration given to appropriate classroom practice.

Childhood and social interaction in everyday life : an epilogue

The epilogue pulls together the conceptual and methodological significance of the papers in the special issue exploring childhood and social interaction in everyday life in Sweden, Norway, United States and Australia. In considering the special issue, four domains of childhood are identified and discussed: childhood is a social construct where children learn how to enter into and participate in their social organizations, competency is best understood when communicative practices are examined in situ, children’s talk and interaction show situated culture in action, and childhood consists of shared social orders between children and adults. Emerging analytic interests are proposed, including investigating how children understand locations and place. Finally, the epilogue highlights the core focus of this special issue, which is showing children’s own methods for making sense of their everyday contexts using the interactional and cultural resources they have to hand.

Improving the management of building life cycle : a data mining approach

The construction industry has adapted information technology in its processes in terms of computer aided design and drafting, construction documentation and maintenance. The data generated within the construction industry has become increasingly overwhelming. Data mining is a sophisticated data search capability that uses classification algorithms to discover patterns and correlations within a large volume of data. This paper presents the selection and application of data mining techniques on maintenance data of buildings. The results of applying such techniques and potential benefits of utilising their results to identify useful patterns of knowledge and correlations to support decision making of improving the management of building life cycle are presented and discussed.

Encouraging ethics and challenging corruption : reforming governance in public institutions

Encouraging Ethics and Preventing Corruption brings theory and practice together in addressing the question: How are we to be ethical in public life and through public institutions? It is a major contribution to public sector ethics within Australia and internationally because it provides an exhaustive analysis of reform across a decade in one jurisdiction, Queensland, and then proceeds to itemise a best practice integrity system or ethics regime. Drawing on the extensive research of two of Australia's leading practical ethicists, this text is essential reading for all students and practitioners of applied and professional ethics in the public sphere. Part A of the text provides a preferred theoretical and conceptual framework which both justifies and guides the development of a public sector ethics regime. Part B examines the place of the individual within a world of institutional ethics. Part C outlines the Queensland governance reforms introduced since 1989 following the Fitzgerald Inquiry which exposed corruption in the police and ministry. The final chapter, the 'Epilogue', gathers the insights of earlier chapters and suggests a more explicitly ethics-centred approach to governance reform that may take us 'beyond best practice'. Clearly, while it is the Australian context we have in mind, we are confident that this is a text which addresses the quest for integrity and ethics in government wherever society is committed to social and liberal democratic ideals.

The influence of role models on students' entrepreneurial intentions

The Smart State initiative requires both improved education and training, particularly in technical fields, plus entrepreneurship to commercialise new ideas. In this study, we propose an entrepreneurial intentions model as a guide to examine the educational choices and entrepreneurial intentions of first-year University students, focusing on the effect of role models. A survey of over 1000 first -year University students revealed that the most enterprising students were choosing to study in the disciplines of information technology and business, economics and law, or selecting dual degree programs that include business. The role models most often identified for their choice of field of study were parents, followed by teachers and peers, wish females identifying more role models than males. For entrepreneurship, students' role models were parents and peers, followed by famous persons and teachers. Males and females identified similar numbers of role models, but males found starting a business more desirable and more feasible, and reported higher entrepreneurial intention. The implications of these findings for Smart State policy are discussed.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Using data mining to improve building life cycle

The building life cycle process is complex and prone to fragmentation as it moves through its various stages. The number of participants, and the diversity, specialisation and isolation both in space and time of their activities, have dramatically increased over time. The data generated within the construction industry has become increasingly overwhelming. Most currently available computer tools for the building industry have offered productivity improvement in the transmission of graphical drawings and textual specifications, without addressing more fundamental changes in building life cycle management. Facility managers and building owners are primarily concerned with highlighting areas of existing or potential maintenance problems in order to be able to improve the building performance, satisfying occupants and minimising turnover especially the operational cost of maintenance. In doing so, they collect large amounts of data that is stored in the building’s maintenance database. The work described in this paper is targeted at adding value to the design and maintenance of buildings by turning maintenance data into information and knowledge. Data mining technology presents an opportunity to increase significantly the rate at which the volumes of data generated through the maintenance process can be turned into useful information. This can be done using classification algorithms to discover patterns and correlations within a large volume of data. This paper presents how and what data mining techniques can be applied on maintenance data of buildings to identify the impediments to better performance of building assets. It demonstrates what sorts of knowledge can be found in maintenance records. The benefits to the construction industry lie in turning passive data in databases into knowledge that can improve the efficiency of the maintenance process and of future designs that incorporate that maintenance knowledge.

The impact of perceived stage of cancer on carers’ anxiety and depression during the patients’ final year of life

Objective: This paper explores the effects of perceived stage of cancer (PSOC) on carers' anxiety and depression during the patients' final year. Methods: A consecutive sample of patients and carers (N=98) were surveyed at regular intervals regarding PSOC, and anxiety and depression using the Hospital Anxiety and Depression Scale. Means were compared by gender using the Mann-Whitney U-test. The chi-square was used to analyse categorical data. Agreement between carers' and patients' PSOC was estimated using kappa statistics. Correlations between carers' PSOC and their anxiety and depression were calculated using the Spearman's rank correlation. Results: Over time, an increasing proportion of carers reported that the cancer was advanced, culminating at 43% near death. Agreement regarding PSOC was fair (kappa=0.29-0.34) until near death (kappa=0.21). Carers' anxiety increased over the year; depression increased in the final 6 months. Females were more anxious (p=0.049, 6 months; p=0.009, 3 months) than males, and more depressed until 1 month to death. The proportion of carers reporting moderate-severe anxiety almost doubled over the year to 27%, with more females in this category at 6 months (p=0.05). Carers with moderate-severe depression increased from 6 to 15% over the year. Increased PSOC was weakly correlated with increased anxiety and depression. Conclusions: Carers' anxiety exceeded depression in severity during advanced cancer. Females generally experienced greater anxiety and depression. Carers were more realistic than patients regarding the ultimate outcome, which was reflected in their declining mental health, particularly near the end.

Discriminating between primary school students with high and low self-esteem.

From an initial sample of 747 primary school students, the top 16 percent (n =116) with high self-esteem (HSE) and the bottom 15 percent (n = I1 I) with low selfesteem (LSE) were se/eeted. These two groups were then compared on personal and classroom variables. Significant differences were found for all personal (self-talk, selfconcepts) and classroom (teacher feedback, praise, teacher-student relationship, and classroom environment) variables. Students with HSE scored more highly on all variables. Discriminant Function Analysis (DFA) was then used to determine which variables discriminated between these two groups of students. Learner self-concept, positive and negative self-talk, classroom environment, and effort feedback were the best discriminators of students with high and low self-esteem. Implications for educational psychologists and teachers are discussed.

Elementary school students’ learner self-concept, academic self-concepts and approaches to learning

The present study investigated the relationships between academic selfconcepts, learner self-concept, and approaches to learning in elementary school students. A sample of 580 Australian Grade 6 and 7 school students with a mean age of 10.7 years participated in the study. Weak negative correlations between learner self-concepts and surface approaches to learning were identiŽ ed. In contrast, deep approaches for both boys and girls showed the highest positive correlations with school self-concept and learning self-concept. Only slight variations in these Ž gures were found between boys and girls.

Terminally ill cancer patients’ wish to hasten death

This exploratory study investigated factors associated with the wish to hasten death among a sample of terminally ill cancer patients. Semi-structured interviews conducted on a total of 72 hospice and home palliative care patients were subjected to qualitative analysis using QSRNUDIST. The main themes to emerge suggested that patients with a high wish to hasten death had greater concerns with physical symptoms and psychologica l suffering, perceived themselves to be more of a burden to others, and experienced higher levels of demoralization, while also reporting less confidence in symptom control, fewer social supports, less satisfaction with life experiences and fewer religious beliefs when compared with patients who had a moderate or no wish to hasten death. The implications of these findings will be discussed.

Elementary students, creativity, and technology

This article reports on the impact on student personal creativity of a longitudinal study that had as its major goal the creation of a unique intervention program for elementary students. The intervention was based on the National Profile and Statement (Curriculum Corporation, 1994a, 1994b) for the curriculum area of technology. The intervention program comprised thematically based units of work that integrated all eight Australian Key Learning Areas (KLA). A pretest/posttest control group design investigation (Campbell & Stanley, 1963) was undertaken with 580 students from 7 schools and 24 class groups that were randomly divided into 3 treatment groups. One group (10 classes) formed the control group. Another 7 classes received the year-long intervention program, while the remaining 7 classes received the intervention, but with the added seamless integration of their available classroom computer technologies. The effect of the intervention on the personal creativity characteristics of the students involved in the study was assessed using the Creativity Checklist, an instrument that was developed during the study. The results suggest that the purposeful integration of computer technology with the intervention program positively affects the personal creativity characteristics of students.

Whole of life cycle cost analysis in bridge rehabilitation

The report presents a methodology for whole of life cycle cost analysis of alternative treatment options for bridge structures, which require rehabilitation. The methodology has been developed after a review of current methods and establishing that a life cycle analysis based on a probabilistic risk approach has many advantages including the essential ability to consider variability of input parameters. The input parameters for the analysis are identified as initial cost, maintenance, monitoring and repair cost, user cost and failure cost. The methodology utilizes the advanced simulation technique of Monte Carlo simulation to combine a number of probability distributions to establish the distribution of whole of life cycle cost. In performing the simulation, the need for a powerful software package, which would work with spreadsheet program, has been identified. After exploring several products on the market, @RISK software has been selected for the simulation. In conclusion, the report presents a typical decision making scenario considering two alternative treatment options.