The role of the learning community in the development of discipline knowledge and generic graduate outcomes

In this paper we describe a study of learning outcomes at a research-intensive Australian university. Three graduate outcome variables (discipline knowledge and skills, communication and problem solving, and ethical and social sensitivity) are analysed separately using OLS regression and comparisons are made of the patterns of unique contributions from four independent variables (the CEQ Good Teaching and Learning Communities Scales, and two new, independent, scales for measuring Teaching and Program Quality). Further comparisons of these patterns are made across the Schools of the university. Results support the view that teaching and program quality are not the only important determinants of students' learning outcomes. It is concluded that, whilst it continues to be appropriate for universities to be concerned with the quality of their teaching and programs, the interactive, social and collaborative aspects of students' learning experiences, captured in the notion of the Learning Community, are also very important determinants of graduate outcomes, and so should be included in the focus of attempts at enhancing the quality of student learning.

Promoting Learning for Sustainability: Principals' Perceptions of the Role of Outdoor and Environmental Education Centres

Outdoor and Environmental Education Centres provide programs that are designed to address a range of environmental education aims, and contribute broadly to student learning for sustainability. This paper examines the roles such Centres can play, and how they might contribute to the Australian Government’s initiative in relation to sustainable schools. Interviews with the principals of 23 such Centres in Queensland revealed three roles or models under which they operate: the destination model; the expert/advisor model; and the partnership model. Principals’ understandings of these roles are discussed and the factors that support or hinder their implementation are identified. It is concluded that while the provision of programs in the environment is still a vital role of outdoor and environmental education centres, these can also be seen as a point of entry to long-term partnerships with whole school communities.

Detecting and sorting targeting peptides wtih neural networks and support vector machines

This paper presents a composite multi-layer classifier system for predicting the subcellular localization of proteins based on their amino acid sequence. The work is an extension of our previous predictor PProwler v1.1 which is itself built upon the series of predictors SignalP and TargetP. In this study we outline experiments conducted to improve the classifier design. The major improvement came from using Support Vector machines as a "smart gate" sorting the outputs of several different targeting peptide detection networks. Our final model (PProwler v1.2) gives MCC values of 0.873 for non-plant and 0.849 for plant proteins. The model improves upon the accuracy of our previous subcellular localization predictor (PProwler v1.1) by 2% for plant data (which represents 7.5% improvement upon TargetP).

I-Spi: innovative shared practical ideas: a support guide for literacy and numeracy in the early years of schooling, for home tutor/supervisors in distance education

Innovative Shared Practical Ideas (I-Spi) is a guide to help you and your children learn together. It is designed to affirm, support and strengthen your role as home tutor/supervisors in your daily learning sessions with your children. In this guide particular emphasis is given to the value of talk, formal and informal early literacy and numeracy practices (including ideas from distance school lessons, from home tutor/supervisors, research, and beyond), assessment of these practices together with informal assessment ideas for gauging your children’s literacy and numeracy progress, and stepping in and building on strategies

Machine learning for matching astronomy catalogues

An emerging issue in the field of astronomy is the integration, management and utilization of databases from around the world to facilitate scientific discovery. In this paper, we investigate application of the machine learning techniques of support vector machines and neural networks to the problem of amalgamating catalogues of galaxies as objects from two disparate data sources: radio and optical. Formulating this as a classification problem presents several challenges, including dealing with a highly unbalanced data set. Unlike the conventional approach to the problem (which is based on a likelihood ratio) machine learning does not require density estimation and is shown here to provide a significant improvement in performance. We also report some experiments that explore the importance of the radio and optical data features for the matching problem.

Improving support vector solutions by selecting a sequence of training subsets

In this paper we demonstrate that it is possible to gradually improve the performance of support vector machine (SVM) classifiers by using a genetic algorithm to select a sequence of training subsets from the available data. Performance improvement is possible because the SVM solution generally lies some distance away from the Bayes optimal in the space of learning parameters. We illustrate performance improvements on a number of benchmark data sets.

Personality and goal orientation as predictors of performance in a learning context

The current study aimed to investigate and provide furthering evidence of individual differences as determinants of task performance. This research focused on the effects of the personality traits Openness to Experience and Neuroticism, and two goal orientation traits. Learning Orientation and Avoid Orientation, on task performance. The hypotheses addressed the predictability of the traits, the differential effects of personality and goal orientation traits, and the mediating effects of goal orientation on the relationship between personality and performance. The results were based on questionnaire responses completed by a sample of 103 students. Scores on a computerised Air Traffic Control (ATC) decision-making task were used as a measure of task performance. Learning Orientation was found to be a significant predictor of performance, whilst the effect of Neuroticism was 'approaching' significance. Results indicated strong support for the differential relationship between personality traits and corresponding goal orientation traits. The mediating relationship between Openness to Experience, Learning Orientation and performance was also found to be 'approaching' significance. Results were indicative of the influences of personality and goal orientation on consequent performance outcomes. Implications were discussed, as well as suggestions for possible future directions in research assessing the predictabilit)' of individual differences in learning contexts.

Maintaining the change: Studying and supporting teaching for new learning

Teacher educators who advocate new learning approaches hope that their graduates will address the needs of digitally and globally sophisticated students. A critical, enquiry-based framework for teaching attempts to unravel many traditional assumptions about learning, assumptions which continue to shape preservice teachers’ practices even through early career years. Evidence in relation to effective take up of New Learning education approaches by graduates is sparse. This paper will explore how three teacher educators attempt to wrestle with ways New Learning frameworks can transform outmoded yet embedded views in beginning teachers. They ask: Can changed approaches be consolidated and mobilised against some of the adverse conditions that predominate in schools? And if this is possible, what support might be required for beginning teachers who are struggling to implement a change process

Instruction to support mental computation development in young children of diverse ability

Fostering young children's mental computation capacity is essential to support their numeracy development. Debate continues as to whether young children should be explicitly taught strategies for mental computation, or be afforded the freedom to develop their own. This paper reports on teaching experiments with two groups of students in their first year of schooling: those considered 'at-risk', and those deemed mathematically advanced. Both groups made considerable learning gains as a result of instruction. Importantly, the gains of the at-risk group are likely to renew both their own, and their teacher's confidence in their ability to learn. In this paper, the instructional programs are documented, highlighting the influence of instruction upon the children's development.

The (new ?) basics of a teacher professional learning community

This paper argues for the need to further theorise the concept of teacher professional learning communities and provides empirical evidence to support this case. The paper presents findings from an ongoing research project, which investigates the nature of teacher professional learning communities. The study reveals that actual communities do not conform to a normailized articulation of features, as outlined in much of the literature on this topic. Consequently, it represents an attempt to engage in the difficult but necessary task of simultaneously fashioning theory from practice, whilst interpreting theory, in practice. The study proposes that current functionalist understandings of teacher professional learning communities are based upon a literature base which is insufficiently nuanced to capture the complexity inherent within these bodies. A broader base of a more critical sociological literature is also drawn upon to better understand actual, "lived" teacher communities, which are somewhat difficult to describe. In part, such communities exhibit features of functionalist conceptions but they are also organic entities which may be quite unpredictable in their outcomes and cannot be reduced to specific features; they each have their own specific "logic of practice" (Bourdieu, 1990) which influences their activities, in their particular field. The argument proposed here is that in one particular community, this complexity may be represented by the many purposes which the community served, arguably often unbeknown to its members, which fashioned the actual community. This paper tries to add to the existing theoretical base of literature, at the same time as providing evidence to support this theorisation.

Learning orientation and market orientation: From an alliance perspective

The literature on market orientation has grown in significance during the past decade. Whilst there is more literature on market orientation, the literature on learning orientation is also starting to increase. Numerous researchers have studied and found support for the positive relationship between market orientation and learning orientation (eg Slater and Narver 1995). However, not many researchers have investigated the relationship that firms with a high learning orientation have a high market orientation (eg Sinkula, Baker and Noordewier 1997). This paper attempts to investigate that link in more detail. An alliance perspective is taken in understanding this relationship.

Blood type classification using computer vision and machine learning

In emergency situations, where time for blood transfusion is reduced, the O negative blood type (the universal donor) is administrated. However, sometimes even the universal donor can cause transfusion reactions that can be fatal to the patient. As commercial systems do not allow fast results and are not suitable for emergency situations, this paper presents the steps considered for the development and validation of a prototype, able to determine blood type compatibilities, even in emergency situations. Thus it is possible, using the developed system, to administer a compatible blood type, since the first blood unit transfused. In order to increase the system’s reliability, this prototype uses different approaches to classify blood types, the first of which is based on Decision Trees and the second one based on support vector machines. The features used to evaluate these classifiers are the standard deviation values, histogram, Histogram of Oriented Gradients and fast Fourier transform, computed on different regions of interest. The main characteristics of the presented prototype are small size, lightweight, easy transportation, ease of use, fast results, high reliability and low cost. These features are perfectly suited for emergency scenarios, where the prototype is expected to be used.