Leadership for creating a thinking school at Buranda State School

This article explores the role of principal leadership in creating a thinking school. It contributes to the school leadership literature by exploring the intersection of two important areas of study in education - school leadership and education for thinking - which is a particularly apt area of study, because effective school leadership is crucial if students are to learn to be critical and creative thinkers, yet this connection has not be widely investigated. We describe how one principal, Hinton, turned around an underperforming school by using critical and creative philosophical thinking as the focus for students, staff and parents. Then, drawing on the school leadership literature, the article describes seven attributes of school leadership beginning with four articulated by Leithwood and colleagues (2006) (building vision and setting direction; redesigning the organisation; understanding and developing people; managing the teaching and learning program), and adding three others (influence; self-development; and responding to context). This framework is then used in a case study format in a collaboration between practitioner and researchers to first explore evidence from empirical studies and personal reflection about Hinton's leadership of Buranda State School, and second to illuminate how these general features of school leadership apply to creating a thinking school. Based on the case study and using the general characteristics of school leadership, a framework for leading a thinking school is described. Because the framework is based on a turnaround school, this framework has wide applicability: to schools that are doing well as an indication of how to implement a contemporary approach to curriculum and pedagogy; and to schools that are underperforming and want a rigorous, high expectation and contemporary way to improve student learning.

A model for enhancing assessment and teaching practice at the coalface : Insights from a Fellow-In-Residence engagement program

Australian universities are currently engaging with new governmental policies and regulations that require them to demonstrate enhanced quality and accountability in teaching and research. The development of national academic standards for learning outcomes in higher education is one such instance of this drive for excellence. These discipline-specific standards articulate the minimum, or Threshold Learning Outcomes, to be addressed by higher education institutions so that graduating students can demonstrate their achievement to their institutions, accreditation agencies, and industry recruiters. This impacts not only on the design of Engineering courses (with particular emphasis on pedagogy and assessment), but also on the preparation of academics to engage with these standards and implement them in their day-to-day teaching practice on a micro level. This imperative for enhanced quality and accountability in teaching is also significant at a meso level, for according to the Australian Bureau of Statistics, about 25 per cent of teachers in Australian universities are aged 55 and above and more than 54 per cent are aged 45 and above (ABS, 2006). A number of institutions have undertaken recruitment drives to regenerate and enrich their academic workforce by appointing capacity-building research professors and increasing the numbers of early- and mid-career academics. This nationally driven agenda for quality and accountability in teaching permeates also the micro level of engineering education, since the demand for enhanced academic standards and learning outcomes requires both a strong advocacy for a shift to an authentic, collaborative, outcomes-focused education and the mechanisms to support academics in transforming their professional thinking and practice. Outcomes-focused education means giving greater attention to the ways in which the curriculum design, pedagogy, assessment approaches and teaching activities can most effectively make a positive, verifiable difference to students’ learning. Such education is authentic when it is couched firmly in the realities of learning environments, student and academic staff characteristics, and trustworthy educational research. That education will be richer and more efficient when staff works collaboratively, contributing their knowledge, experience and skills to achieve learning outcomes based on agreed objectives. We know that the school or departmental levels of universities are the most effective loci of changes in approaches to teaching and learning practices in higher education (Knight & Trowler, 2000). Heads of Schools are being increasingly entrusted with more responsibilities - in addition to setting strategic directions and managing the operational and sometimes financial aspects of their school, they are also expected to lead the development and delivery of the teaching, research and other academic activities. Guiding and mentoring individuals and groups of academics is one critical aspect of the Head of School’s role. Yet they do not always have the resources or support to help them mentor staff, especially the more junior academics. In summary, the international trend in undergraduate engineering course accreditation towards the demonstration of attainment of graduate attributes poses new challenges in addressing academic staff development needs and the assessment of learning. This paper will give some insights into the conceptual design, implementation and empirical effectiveness to date, of a Fellow-In-Residence Engagement (FIRE) program. The program is proposed as a model for achieving better engagement of academics with contemporary issues and effectively enhancing their teaching and assessment practices. It will also report on the program’s collaborative approach to working with Heads of Schools to better support academics, especially early-career ones, by utilizing formal and informal mentoring. Further, the paper will discuss possible factors that may assist the achievement of the intended outcomes of such a model, and will examine its contributions to engendering an outcomes-focussed thinking in engineering education.

Integration of opinion into customer analysis model

As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. In order to enhance customer satisfaction and their shopping experiences, it has become important to analysis customers reviews to extract opinions on the products that they buy. Thus, Opinion Mining is getting more important than before especially in doing analysis and forecasting about customers’ behavior for businesses purpose. The right decision in producing new products or services based on data about customers’ characteristics means profit for organization/company. This paper proposes a new architecture for Opinion Mining, which uses a multidimensional model to integrate customers’ characteristics and their comments about products (or services). The key step to achieve this objective is to transfer comments (opinions) to a fact table that includes several dimensions, such as, customers, products, time and locations. This research presents a comprehensive way to calculate customers’ orientation for all possible products’ attributes.

Reasoning about component similarity in building product models from the construction perspective

Identifying the design features that impact construction is essential to developing cost effective and constructible designs. The similarity of building components is a critical design feature that affects method selection, productivity, and ultimately construction cost and schedule performance. However, there is limited understanding of what constitutes similarity in the design of building components and limited computer-based support to identify this feature in a building product model. This paper contributes a feature-based framework for representing and reasoning about component similarity that builds on ontological modelling, model-based reasoning and cluster analysis techniques. It describes the ontology we developed to characterize component similarity in terms of the component attributes, the direction, and the degree of variation. It also describes the generic reasoning process we formalized to identify component similarity in a standard product model based on practitioners' varied preferences. The generic reasoning process evaluates the geometric, topological, and symbolic similarities between components, creates groupings of similar components, and quantifies the degree of similarity. We implemented this reasoning process in a prototype cost estimating application, which creates and maintains cost estimates based on a building product model. Validation studies of the prototype system provide evidence that the framework is general and enables a more accurate and efficient cost estimating process.

How do supercontinents break up? — assessing the continental large igneous province (LIP) record of the break-up of Pangea

LIP emplacement is linked to the timing and evolution of supercontinental break-up. LIP-related break-up produces volcanic rifted margins, new and large (up to 108 km2) ocean basins, and new, smaller continents that undergo dispersal and potentially reassembly (e.g., India). However, not all continental LIPs lead to continental rupture. We analysed the <330 Ma continental LIP record(following final assembly of Pangea) to find relationships between LIP event attributes (e.g., igneous volume, extent, distance from pre-existing continental margin) and ocean basin attributes (e.g., length of new ocean basin/rifted margin) and how these varied during the progressive break up of Pangea. No correlation exists between LIP magnitude and size of the subsequent ocean basin or rifted margin. Our review suggests a three-phased break-up history of Pangea: 1) “Preconditioning” phase (∼330–200 Ma): LIP events (n=7) occurred largely around the supercontinental margin clustering today in Asia, with a low (<20%) rifting success rate. The Panjal Traps at ∼280 Ma may represent the first continental rupturing event of Pangea, resulting in continental ribboning along the Tethyan margin; 2) “Main Break-up” phase (∼200–100 Ma): numerous large LIP events(n=10) in the supercontinent interior, resulting in highly successful fragmentation (90%) and large, new ocean basins(e.g., Central/South Atlantic, Indian, >3000 km long); 3) “Waning” phase (∼100–0 Ma): Declining LIP magnitudes (n=6), greater proximity to continental margins (e.g., Madagascar, North Atlantic, Afro-Arabia, Sierra Madre) producing smaller ocean basins (<2600 km long). How Pangea broke up may thus have implications for earlier supercontinent reconstructions and LIP record.

Whom to group with - a Bourdieusian narrative analysis of international students in an Australian university

Introduction to the topic or context and/or mapping of the literature Increasing degree-seeking, self-funded, international students from affluent Asian countries, who use English as an additional language (EAL), have contributed to cultural and linguistic diversities in Australian universities. Such diversities further posed challenges in pedagogy and assessment. In particular, these students' English proficiency and cultural attributes were highlighted as factors in productive group discussions, and equitable group assessment. The focus in the research literature thus far is on how EAL international students can better English proficiency and adaptability to group participation. However, little is known from sociological perspectives about the power relations involved in EAL students' choice of group members in group discussions.

Work integrated research higher degree studies : experiences, benefits, barriers and coping strategies

Attributed to the changing social, political and economic landscape of the ‘knowledge economy’, Australian universities are under pressure to produce researchers that have a variety of skills which meet the demands of an increasingly diverse job market. As a consequence, the Australian PhD now includes a range of doctoral degrees. This paper reports on the experiences of two PhD students engaged in an informally managed research higher degree program described in this paper as a Work Integrated Research Higher Degree (WIRHD). Their learning process shares the attributes from both the traditional PhD program and professional doctorates. However, because of the blended nature of the learning contexts, what students need to manage within the WIRHD is much more complicated than the established RHD programs. An exploratory case study approach exploring experiences, benefits, barriers and coping strategies was conducted with the view to develop a preliminary integrative framework that attempts to explain the various contexts that influence the learning experience of WIRHD candidates. The paper concludes with some recommended strategies for helping WIRHD candidates to manage the challenges associated with their learning process.

Integrating collaborative filtering and matching-based search for product recommendation

Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and thus help them in making good decisions about which product to buy from the vast number of product choices available to them. Many of the current recommender systems are developed for simple and frequently purchased products like books and videos, by using collaborative-filtering and content-based recommender system approaches. These approaches are not suitable for recommending luxurious and infrequently purchased products as they rely on a large amount of ratings data that is not usually available for such products. This research aims to explore novel approaches for recommending infrequently purchased products by exploiting user generated content such as user reviews and product click streams data. From reviews on products given by the previous users, association rules between product attributes are extracted using an association rule mining technique. Furthermore, from product click streams data, user profiles are generated using the proposed user profiling approach. Two recommendation approaches are proposed based on the knowledge extracted from these resources. The first approach is developed by formulating a new query from the initial query given by the target user, by expanding the query with the suitable association rules. In the second approach, a collaborative-filtering recommender system and search-based approaches are integrated within a hybrid system. In this hybrid system, user profiles are used to find the target user’s neighbour and the subsequent products viewed by them are then used to search for other relevant products. Experiments have been conducted on a real world dataset collected from one of the online car sale companies in Australia to evaluate the effectiveness of the proposed recommendation approaches. The experiment results show that user profiles generated from user click stream data and association rules generated from user reviews can improve recommendation accuracy. In addition, the experiment results also prove that the proposed query expansion and the hybrid collaborative filtering and search-based approaches perform better than the baseline approaches. Integrating the collaborative-filtering and search-based approaches has been challenging as this strategy has not been widely explored so far especially for recommending infrequently purchased products. Therefore, this research will provide a theoretical contribution to the recommender system field as a new technique of combining collaborative-filtering and search-based approaches will be developed. This research also contributes to a development of a new query expansion technique for infrequently purchased products recommendation. This research will also provide a practical contribution to the development of a prototype system for recommending cars.

Reverse biased Schottky contact hydrogen sensors based on Pt∕nanostructured ZnO∕SiC

Pt/nanostructured ZnO/SiC Schottky contact devices were fabricated and characterized for hydrogen gas sensing. These devices were investigated in reverse bias due to greater sensitivity, which attributes to the application of nanostructured ZnO. The current-voltage (I-V) characteristics of these devices were measured in different hydrogen concentrations. Effective change in the barrier height for 1% hydrogen was calculated as 27.06 meV at 620°C. The dynamic response of the sensors was also investigated and a voltage shift of 325 mV was recorded at 620°C during exposure to 1% hydrogen in synthetic air.

Speaker diarization : "who spoke when"

Speaker diarization is the process of annotating an input audio with information that attributes temporal regions of the audio signal to their respective sources, which may include both speech and non-speech events. For speech regions, the diarization system also specifies the locations of speaker boundaries and assign relative speaker labels to each homogeneous segment of speech. In short, speaker diarization systems effectively answer the question of ‘who spoke when’. There are several important applications for speaker diarization technology, such as facilitating speaker indexing systems to allow users to directly access the relevant segments of interest within a given audio, and assisting with other downstream processes such as summarizing and parsing. When combined with automatic speech recognition (ASR) systems, the metadata extracted from a speaker diarization system can provide complementary information for ASR transcripts including the location of speaker turns and relative speaker segment labels, making the transcripts more readable. Speaker diarization output can also be used to localize the instances of specific speakers to pool data for model adaptation, which in turn boosts transcription accuracies. Speaker diarization therefore plays an important role as a preliminary step in automatic transcription of audio data. The aim of this work is to improve the usefulness and practicality of speaker diarization technology, through the reduction of diarization error rates. In particular, this research is focused on the segmentation and clustering stages within a diarization system. Although particular emphasis is placed on the broadcast news audio domain and systems developed throughout this work are also trained and tested on broadcast news data, the techniques proposed in this dissertation are also applicable to other domains including telephone conversations and meetings audio. Three main research themes were pursued: heuristic rules for speaker segmentation, modelling uncertainty in speaker model estimates, and modelling uncertainty in eigenvoice speaker modelling. The use of heuristic approaches for the speaker segmentation task was first investigated, with emphasis placed on minimizing missed boundary detections. A set of heuristic rules was proposed, to govern the detection and heuristic selection of candidate speaker segment boundaries. A second pass, using the same heuristic algorithm with a smaller window, was also proposed with the aim of improving detection of boundaries around short speaker segments. Compared to single threshold based methods, the proposed heuristic approach was shown to provide improved segmentation performance, leading to a reduction in the overall diarization error rate. Methods to model the uncertainty in speaker model estimates were developed, to address the difficulties associated with making segmentation and clustering decisions with limited data in the speaker segments. The Bayes factor, derived specifically for multivariate Gaussian speaker modelling, was introduced to account for the uncertainty of the speaker model estimates. The use of the Bayes factor also enabled the incorporation of prior information regarding the audio to aid segmentation and clustering decisions. The idea of modelling uncertainty in speaker model estimates was also extended to the eigenvoice speaker modelling framework for the speaker clustering task. Building on the application of Bayesian approaches to the speaker diarization problem, the proposed approach takes into account the uncertainty associated with the explicit estimation of the speaker factors. The proposed decision criteria, based on Bayesian theory, was shown to generally outperform their non- Bayesian counterparts.

Human factors in estimating

It is commonly held that the ability of the estimator to apply professional skill and judgement is an important factor in the production of an accurate cost estimate. This chapter identifies these abilities and attributes of the individual estimator that affects estimating accuracy. These human factors are examined under the headings of the role of the estimators, skills of the estimator, characteristics of the estimator, interpretation of data, and the influence of expertise.

Applying quality management to project delivery in Hong Kong : A field study of management perceptions

The concept of market-driven rather than product-driven quality management has been given prominence through the report of a recent inquiry into the performance of the Hong Kong construction industry. The report submitted to the Government of Hong Kong in 2001 establishes a new vision of ‘an integrated industry that is capable of continuous improvement towards excellence in the market-driven environment’. Given the current economic downturn, major contractors are facing many challenges to realize this new quality oriented vision. This paper addresses the critical and timely issue of applying quality management to the project delivery process in Hong Kong. The paper attempts to capture and critically examine management perceptions of quality management aspects as applied to a local large-scale road construction project. Based on the analysis of questionnaire feedback and face-to-face interviews, the paper reveals key attributes of a successful application of quality management approaches, and identifies a mechanism for facilitating such implementation.

Motivations for eWOM exchanges in an online community : self-development, problem solving support, relaxation

Electronic word-of-mouth (eWOM) has gained significant attention from academics and practitioners since it has become an important source of consumers’ product information, which can influence consumer purchase intentions (Cheung & Lee, 2012). eWOM exchanges exist in two types of online communities: online communities of practice and online communities of interest. A few prior studies in online communities of interest have examined members’ motivations for product knowledge exchange (Hung & Li, 2007; Ma & Agarwal, 2007). However, there is a lack of understanding of member motivations for exchanging social bonds and enjoyment in addition to exchanging knowledge pertaining to products in the community. It is important to have an initial comprehension of motivation as an antecedent of these three eWOM exchanges so as to be able to determine the driving factors that lead members to generate eWOM communication. Thus, the research problem "What are the driving factors for members to exchange eWOM in an online community?" was justified for investigation. The purpose of this study was to examine different member motivations for exchanging three types of eWOM. Resource exchange theory and theory on consumer motivation and behavior were applied to develop a conceptual framework for this study. This study focused on an online beauty community since there is an increasing trend of consumers turning to online beauty resources so as to exchange useful beauty product information (SheSpot, 2011). As this study examined consumer motivation in an online beauty community, a web-based survey was the most effective and efficient way to gain responses from beauty community members and these members were appropriate samples from which to draw a conclusion about the whole population. Multiple regression analysis was used to test the relationships between member motivations and eWOM exchanges. It was found that members have different motivations for exchanging knowledge, social bonds, and enjoyment related to products: self-development, problem solving support, and relaxation, respectively. This study makes three theoretical contributions. First, this study identifies the influence of self-development motivation on knowledge exchange in an online community of interest, just as this motivation has previously been found in online communities of practice. This study highlights that members of the two different types of online communities share similar goals of knowledge exchange, despite the two communities evincing different attributes (e.g., member characteristics and tasks’ objectives). Further, this study will assist researchers to understand other motivations identified by prior research in online communities of practice since such motivations may be applicable to online communities of interest. Second, this study offers a new perspective on member motivation for social bonding. This study indicates that in addition to social support from friends and family, consumers are motivated to build social bonds with members in an online community of interest since they are an important source of problem solving support in regard to products. Finally, this study extends the body of knowledge pertaining to member motivation for enjoyment exchange. This study provides a basis for researchers to understand that members in an online community of interest value experiential aspects of enjoyable consumption activities, and thus based on group norms, members have a mutual desire for relaxation from enjoyment exchange. The major practical contribution is that this study provides an important guideline for marketing managers to develop different marketing strategies based on member motivations for exchanging three types of eWOM in an online community of interest, such as an online beauty community. This will potentially help marketing managers increase online traffic and revenue, and thus bring success to the community. Although, this study contributes to the literature by highlighting three distinctive member motivations for eWOM exchanges in an online community of interest, there are some possible research limitations. First, this study was conducted in an online beauty community in Australia. Hence, further research should replicate this study in other industries and nations so as to give the findings greater generalisability. Next, online beauty community members are female skewed. Thus, future research should examine whether similar patterns of motivations would emerge in other online communities that tend to be populated by males (e.g., communities focused on football). Further, a web-based survey has its limitations in terms of self-selection and self-reporting (Bhatnagar & Ghose, 2004). Therefore, further studies should test the framework by employing different research methods in order to overcome these weaknesses.

Predicate encryption with various properties

Predicate encryption (PE) is a new primitive which supports exible control over access to encrypted data. In PE schemes, users' decryption keys are associated with predicates f and ciphertexts encode attributes a that are specified during the encryption procedure. A user can successfully decrypt if and only if f(a) = 1. In this thesis, we will investigate several properties that are crucial to PE. We focus on expressiveness of PE, Revocable PE and Hierarchical PE (HPE) with forward security. For all proposed systems, we provide a security model and analysis using the widely accepted computational complexity approach. Our first contribution is to explore the expressiveness of PE. Existing PE supports a wide class of predicates such as conjunctions of equality, comparison and subset queries, disjunctions of equality queries, and more generally, arbitrary combinations of conjunctive and disjunctive equality queries. We advance PE to evaluate more expressive predicates, e.g., disjunctive comparison or disjunctive subset queries. Such expressiveness is achieved at the cost of computational and space overhead. To improve the performance, we appropriately revise the PE to reduce the computational and space cost. Furthermore, we propose a heuristic method to reduce disjunctions in the predicates. Our schemes are proved in the standard model. We then introduce the concept of Revocable Predicate Encryption (RPE), which extends the previous PE setting with revocation support: private keys can be used to decrypt an RPE ciphertext only if they match the decryption policy (defined via attributes encoded into the ciphertext and predicates associated with private keys) and were not revoked by the time the ciphertext was created. We propose two RPE schemes. Our first scheme, termed Attribute- Hiding RPE (AH-RPE), offers attribute-hiding, which is the standard PE property. Our second scheme, termed Full-Hiding RPE (FH-RPE), offers even stronger privacy guarantees, i.e., apart from possessing the Attribute-Hiding property, the scheme also ensures that no information about revoked users is leaked from a given ciphertext. The proposed schemes are also proved to be secure under well established assumptions in the standard model. Secrecy of decryption keys is an important pre-requisite for security of (H)PE and compromised private keys must be immediately replaced. The notion of Forward Security (FS) reduces damage from compromised keys by guaranteeing confidentiality of messages that were encrypted prior to the compromise event. We present the first Forward-Secure Hierarchical Predicate Encryption (FS-HPE) that is proved secure in the standard model. Our FS-HPE scheme offers some desirable properties: time-independent delegation of predicates (to support dynamic behavior for delegation of decrypting rights to new users), local update for users' private keys (i.e., no master authority needs to be contacted), forward security, and the scheme's encryption process does not require knowledge of predicates at any level including when those predicates join the hierarchy.

A hybrid association rule mining approach for characterizing network traffic behaviour

Understanding network traffic behaviour is crucial for managing and securing computer networks. One important technique is to mine frequent patterns or association rules from analysed traffic data. On the one hand, association rule mining usually generates a huge number of patterns and rules, many of them meaningless or user-unwanted; on the other hand, association rule mining can miss some necessary knowledge if it does not consider the hierarchy relationships in the network traffic data. Aiming to address such issues, this paper proposes a hybrid association rule mining method for characterizing network traffic behaviour. Rather than frequent patterns, the proposed method generates non-similar closed frequent patterns from network traffic data, which can significantly reduce the number of patterns. This method also proposes to derive new attributes from the original data to discover novel knowledge according to hierarchy relationships in network traffic data and user interests. Experiments performed on real network traffic data show that the proposed method is promising and can be used in real applications. Copyright2013 John Wiley & Sons, Ltd.