Working together for Toby : early childhood student teachers engaging in collaborative problem-based learning around child abuse and neglect

Early childhood teacher education programs have a responsibility, amongst many, to prepare teachers for decision-making on real world issues, such as child abuse and neglect. Their repertoire of skills can be enhanced by engaging with others, either face-to-face or online, in authentic problem-based learning. This paper draws on a study of early childhood student teachers who engaged in an authentic learning experience, which was to consider and to suggest how they would act upon a real-life case of child abuse encountered in an early childhood classroom in Queensland. This was the case of Toby (a pseudonym), who was suspected of being physically abused at home. Students drew upon relevant legislation, policy and resource materials to tackle Toby’s case. The paper provides evidence of students grappling with the complexity of a child abuse case and establishing, through collaboration with others, a proactive course of action. The paper has a dual focus. First, it discusses the pedagogical context in which early childhood student teachers deal with issues of child abuse and neglect in the course of their teacher education program. Second, it examines evidence of students engaging in collaborative problem-solving around issues of child abuse and neglect and teachers’ responsibilities, both legal and professional, to the children and families they work with. Early childhood policy-makers, practitioners and teacher educators are challenged to consider how early childhood teachers are best equipped to deal with child protection and early intervention.

Nonverbal Learning Disability explained : the link to shunted hydrocephalus

A Nonverbal Learning Disability is believed to be caused by damage, disorder or destruction of neuronal white matter in the brain’s right hemisphere and may be seen in persons experiencing a wide range of neurological diseases such as hydrocephalus and other types of brain injury (Harnadek & Rourke 1994). This article probes the relationship between shunted hydrocephalus and Nonverbal Learning Disability. Description of hydrocephalus and intelligence associated with hydrocephalus concludes with explication of the ‘final common pathway’ that links residual damage caused by the hydrocephalic condition to a Nonverbal Learning Disability (Rourke & Del Dotto 1994, p. 37). The paper seeks to assist teachers, teacher aides, psychologists, guidance officers, support workers, parents and disability service providers whose role is to understand and advocate for individuals with shunted hydrocephalus and spina bifida.

An integrated approach to tourism planning in a developing nation : a case study from Beloi (Timor-Leste)

In this chapter we present a case study set in Beloi, a fishing village located on Ataúro Island, 30 km across the sea from Díli, capital of Timor-Leste (East-Timor). We explore the tension between tourism development, food security and marine conservation in a developing country context. In order to better understand the relationships between the social, ecological and economic issues that arise in tourism planning we use an approach and associated methodology based on storytelling, complexity theory and concept mapping. Through testing scenarios with this methodology we hope to evaluate which trade-offs are acceptable to local people in return for the hoped-for economic boost from increased tourist visitation and associated developments.

Towards an integrated model of information behavior

Information behavior models generally focus on one of many aspects of information behavior, either information finding, conceptualized as information seeking, information foraging or information sense-making, information organizing and information using. This ongoing study is developing an integrated model of information behavior. The research design involves a 2-week-long daily information journal self-maintained by the participants, combined with two interviews, one before, and one after the journal-keeping period. The data from the study will be analyzed using grounded theory to identify when the participants engage in the various behaviors that have already been observed, identified, and defined in previous models, in order to generate useful sequential data and an integrated model.

Preparing quality teachers : making learning visible

Teacher quality is recognised as a lynchpin for education reforms internationally, and both Federal and State governments in Australia have turned their attention to teacher education institutions: the starting point for preparing quality teachers. Changes to policy and shifts in expectations impact on Faculties of Education, despite the fact that little is known about what makes a quality teacher preparation program effective. New accountability measures, mandated Professional Standards, and proposals to test all graduates before registration, mean that teacher preparation programs need capacity for flexibility and responsiveness. The risk is that undergraduate degree programs can become ‘patchwork quilts’ with traces of the old and new stitched together, sometimes at the expense of coherence and integrity. This paper provides a roadmap used by one large Faculty of Education in Queensland for reforming and reconceptualising the curriculum for a 4-year undergraduate program, in response to new demands from government and the professional bodies.

Good practice for enhancing the engagement and success of commencing students

There is widespread recognition that higher education institutions (HEIs) must actively support commencing students to ensure equity in access to the opportunities afforded by higher education. This role is particularly critical for students who because of educational, cultural or financial disadvantage or because they are members of social groups currently under-represented in higher education, may require additional transitional support to “level the playing field.” The challenge faced by HEIs is to provide this “support” in a way that is integrated into regular teaching and learning practices and reaches all commencing students. The Student Success Program (SSP) is an intervention in operation at the Queensland University of Technology (QUT) designed to identify and support those students deemed to be at risk of disengaging from their learning and their institution. Two sets of evidence of the impact of the SSP are presented: First, its expansion (a) from a one-faculty pilot project (Nelson, Duncan & Clarke, 2009) to all faculties and (b) into a variety of applications mirroring the student life cycle; and second, an evaluation of the impact of the SSP on students exposed to it. The outcomes suggest that: the SSP is an example of good practice that can be successfully applied to a variety of learning contexts and student enrolment situations; and the impact of the intervention on student persistence is sustained for at least 12 months and positively influences student retention. It is claimed that the good practice evidenced by the SSP is dependent on its integration into the broader First Year Experience Program at QUT as an example of transition pedagogy in action.

Matching cognitive styles to computer-based instruction : an approach for enhanced learning in electrical engineering

An approach aimed at enhancing learning by matching individual students' preferred cognitive styles to computer-based instructional (CBI) material is presented. This approach was used in teaching some components of a third-year unit in an electrical engineering course at the Queensland University of Technology. Cognitive style characteristics of perceiving and processing information were considered. The bimodal nature of cognitive styles (analytic/imager, analytic/verbalizer, wholist/imager and wholist/verbalizer) was examined in order to assess the full ramification of cognitive styles on learning. In a quasi-experimental format, students' cognitive styles were analysed by cognitive style analysis (CSA) software. On the basis of the CSA results the system defaulted students to either matched or mismatched CBI material. The consistently better performance by the matched group suggests potential for further investigations where the limitations cited in this paper are eliminated. Analysing the differences between cognitive styles on individual test tasks also suggests that certain test tasks may better suit certain cognitive styles.

Personalizing the design of computer-based instruction to enhance learning

This paper reports two studies designed to investigate the effect on learning outcomes of matching individuals' preferred cognitive styles to computer-based instructional (CBI) material. Study 1 considered the styles individually as Verbalizer, Imager, Wholist and Analytic. Study 2 considered the bi-dimensional nature of cognitive styles in order to assess the full ramification of cognitive styles on learning: Analytic/Imager, Analytic/ Verbalizer, Wholist/Imager and the Wholist/Verbalizer. The mix of images and text, the nature of the text material, use of advance organizers and proximity of information to facilitate meaningful connections between various pieces of information were some of the considerations in the design of the CBI material. In a quasi-experimental format, students' cognitive styles were analysed by Cognitive Style Analysis (CSA) software. On the basis of the CSA result, the system defaulted students to either matched or mismatched CBI material by alternating between the two formats. The instructional material had a learning and a test phase. Learning outcome was tested on recall, labelling, explanation and problem-solving tasks. Comparison of the matched and mismatched instruction did not indicate significant difference between the groups, but the consistently better performance by the matched group suggests potential for further investigations where the limitations cited in this paper are eliminated. The result did indicate a significant difference between the four cognitive styles with the Wholist/Verbalizer group performing better then all other cognitive styles. Analysing the difference between cognitive styles on individual test tasks indicated significant difference on recall, labelling and explanation, suggesting that certain test tasks may suit certain cognitive styles.

Sequential nonlinear manifold learning

The computation of compact and meaningful representations of high dimensional sensor data has recently been addressed through the development of Nonlinear Dimensional Reduction (NLDR) algorithms. The numerical implementation of spectral NLDR techniques typically leads to a symmetric eigenvalue problem that is solved by traditional batch eigensolution algorithms. The application of such algorithms in real-time systems necessitates the development of sequential algorithms that perform feature extraction online. This paper presents an efficient online NLDR scheme, Sequential-Isomap, based on incremental singular value decomposition (SVD) and the Isomap method. Example simulations demonstrate the validity and significant potential of this technique in real-time applications such as autonomous systems.

A statistical framework for natural feature representation

This paper presents a robust stochastic framework for the incorporation of visual observations into conventional estimation, data fusion, navigation and control algorithms. The representation combines Isomap, a non-linear dimensionality reduction algorithm, with expectation maximization, a statistical learning scheme. The joint probability distribution of this representation is computed offline based on existing training data. The training phase of the algorithm results in a nonlinear and non-Gaussian likelihood model of natural features conditioned on the underlying visual states. This generative model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The instantiated likelihoods are expressed as a Gaussian mixture model and are conveniently integrated within existing non-linear filtering algorithms. Example applications based on real visual data from heterogenous, unstructured environments demonstrate the versatility of the generative models.

Learning articulated motion structures with Bayesian Networks

This paper presents a general methodology for learning articulated motions that, despite having non-linear correlations, are cyclical and have a defined pattern of behavior Using conventional algorithms to extract features from images, a Bayesian classifier is applied to cluster and classify features of the moving object. Clusters are then associated in different frames and structure learning algorithms for Bayesian networks are used to recover the structure of the motion. This framework is applied to the human gait analysis and tracking but applications include any coordinated movement such as multi-robots behavior analysis.

Applying ISOMAP to the learning of hyperspectral image

In this paper, we present the application of a non-linear dimensionality reduction technique for the learning and probabilistic classification of hyperspectral image. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. It gives much greater information content per pixel on the image than a normal colour image. This should greatly help with the autonomous identification of natural and manmade objects in unfamiliar terrains for robotic vehicles. However, the large information content of such data makes interpretation of hyperspectral images time-consuming and userintensive. We propose the use of Isomap, a non-linear manifold learning technique combined with Expectation Maximisation in graphical probabilistic models for learning and classification. Isomap is used to find the underlying manifold of the training data. This low dimensional representation of the hyperspectral data facilitates the learning of a Gaussian Mixture Model representation, whose joint probability distributions can be calculated offline. The learnt model is then applied to the hyperspectral image at runtime and data classification can be performed.

Applying incremental EM to Bayesian classifiers in the learning of hyperspectral remote sensing data

In this paper, we apply the incremental EM method to Bayesian Network Classifiers to learn and interpret hyperspectral sensor data in robotic planetary missions. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. Many spacecraft carry spectroscopic equipment as wavelengths outside the visible light in the electromagnetic spectrum give much greater information about an object. The algorithm used is an extension to the standard Expectation Maximisation (EM). The incremental method allows us to learn and interpret the data as they become available. Two Bayesian network classifiers were tested: the Naive Bayes, and the Tree-Augmented-Naive Bayes structures. Our preliminary experiments show that incremental learning with unlabelled data can improve the accuracy of the classifier.

Animateur : participatory studio-based learning in computer graphics animation

The impact of digital technology within the creative industries has brought with it a range of new opportunities for collaborative, cross-disciplinary and multi-disciplinary practice. Along with these opportunities has come the need to re-evaluate how we as educators approach teaching within this new digital culture. Within the field of animation, there has been a radical shift in the expectations of students, industry and educators as animation has become central to a range of new moving image practices. This paper interrogates the effectiveness of adopting a studio-based collaborative production project as a method for educating students within this new moving-image culture. The project was undertaken, as part of the Creative Industries Transitions to New Professional Environments program at Queensland University of Technology (QUT) in Brisbane Australia. A number of students studying across the Creative Industries Faculty and the Faculty of Science and Technology were invited to participate in the development of a 3D animated short film. The project offered students the opportunity to become actively involved in all stages of the creative process, allowing them to experience informal learning through collaborative professional practice. It is proposed that theoretical principles often associated with andragogy and constructivism can be used to design and deliver programs that address the emerging issues surrounding the teaching of this new moving image culture.

Engaging students in clinical bacteriology: A fresh look

Frequently there is a disconnectedness, either perceived or actual, between theoretical principles and laboratory practice in science education and this holds true for clinical microbiology where traditionally knowledge is delivered in ‘chunks’ in a lecture format with the misguided belief that students have to know ‘everything about everything’. This preoccupation with content delivery often leaves no time for active class discussion or reflection. Moreover, laboratory classes are treated as add-ons to the process, rather than an integrated part of the whole learning experience. In redesigning our units (subjects) we have bridged the gap between the theory and practice of clinical bacteriology. In doing so, we have seen a transformation in the learning experiences of our students and in the way we teach.