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.

Codes of conduct for mobile device use in meetings : an exploratory analysis

The present paper addresses the findings of a preliminary investigation into policy and codes of conduct pertaining to the use of laptops and PDA’s in business meetings. The purpose of this study was to conduct a review of policies or codes of conduct pertaining to the use of laptops and PDAs in meetings. The investigation included academic literature, policy searches in the public domain of the Internet, as well as personal contact with target industries (large corporations – N=1000 + employees). The results highlight the dearth of policy and codes of conducts pertaining to the use of laptops and PDA’s in business meetings. Consequently, given the growing interdependence between mobile technologies and the contemporary workplace, there exists an opportunity for communication professionals to further research and develop policy and codes of conduct in this area. Implications for corporate communication policies and practices are also discussed.

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.

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.

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.

A Bayesian approach for place recognition

This paper presents a robust place recognition algorithm for mobile robots. The framework proposed combines nonlinear dimensionality reduction, nonlinear regression under noise, and variational Bayesian learning to create consistent probabilistic representations of places from images. These generative models are learnt from a few images and used for multi-class place recognition where classification is computed from a set of feature-vectors. Recognition can be performed in near real-time and accounts for complexity such as changes in illumination, occlusions and blurring. The algorithm was tested with a mobile robot in indoor and outdoor environments with sequences of 1579 and 3820 images respectively. This framework has several potential applications such as map building, autonomous navigation, search-rescue tasks and context recognition.

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.

Expression and connection : the integration of the reflective learning process and the writing process into social network sites

A number of instructors have recently adopted social network sites (SNSs) for learning. However, the learning design of SNSs often remains at a preliminary level similar to a personal log book because it does not properly include reflective learning elements such as individual reflection and collaboration. This article looks at the reflective learning process and the public writing process as a way of improving the quality of reflective learning on SNSs. It proposes a reflective learning model on SNSs based on two key pedagogical concepts for social networking: individual expression and collaborative connection. It is expected that the model would be helpful for instructors in designing a reflective learning process on SNSs in an effective and flexible way.

Cyberbullying : legal implications for schools

Most educators are now aware that cyberbullying is bullying through the use of technology. This new form of bullying makes use of the diverse range of technology now available including email, texting, chat rooms, mobile phones, mobile phone cameras, i-pods and websites. Cyberbullying shares many of the same attributes as face-to-face bullying such as a power imbalance which causes a sense of helplessness on the part of the victim, repetition and the intent to hurt.