Can teacher collaboration overcome barriers to interdisciplinary learning in a disciplinary university? A case study using climate change

A teacher network was formed at an Australian university in order to better promote interdisciplinary student learning on the complex social-environmental problem of climate change. Rather than leaving it to students to piece together disciplinary responses, eight teaching academics collaborated on the task of exposing students to different types of knowledge in a way that was more than the summing of disciplinary parts. With a part-time network facilitator providing cohesion, network members were able to teach into each other’s classes, and share material and student activities across a range of units that included business, zoology, marine science, geography and education. Participants reported that the most positive aspects of the project were the collegiality and support for teaching innovation provided by peers. However, participants also reported being time-poor and overworked. Maintaining the collaboration beyond the initial one year project proved difficult because without funding for the network facilitator, participants were unable to dedicate the time required to meet and collaborate on shared activities. In order to strengthen teacher collaboration in a university whose administrative structures are predominantly discipline-based, there is need for recognition of the benefits of interdisciplinary learning to be matched by recognition of the need for financial and other resources to support collaborative teaching initiatives.

Multiple instance dictionary learning for activity representation

This paper presents an effective feature representation method in the context of activity recognition. Efficient and effective feature representation plays a crucial role not only in activity recognition, but also in a wide range of applications such as motion analysis, tracking, 3D scene understanding etc. In the context of activity recognition, local features are increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational requirements, their performance is still limited for real world applications due to a lack of contextual information and models not being tailored to specific activities. We propose a new activity representation framework to address the shortcomings of the popular, but simple bag-of-words approach. In our framework, first multiple instance SVM (mi-SVM) is used to identify positive features for each action category and the k-means algorithm is used to generate a codebook. Then locality-constrained linear coding is used to encode the features into the generated codebook, followed by spatio-temporal pyramid pooling to convey the spatio-temporal statistics. Finally, an SVM is used to classify the videos. Experiments carried out on two popular datasets with varying complexity demonstrate significant performance improvement over the base-line bag-of-feature method.

Learning Nonlinear Visual Processing from Natural Images

The paradigm of computational vision hypothesizes that any visual function -- such as the recognition of your grandparent -- can be replicated by computational processing of the visual input. What are these computations that the brain performs? What should or could they be? Working on the latter question, this dissertation takes the statistical approach, where the suitable computations are attempted to be learned from the natural visual data itself. In particular, we empirically study the computational processing that emerges from the statistical properties of the visual world and the constraints and objectives specified for the learning process. This thesis consists of an introduction and 7 peer-reviewed publications, where the purpose of the introduction is to illustrate the area of study to a reader who is not familiar with computational vision research. In the scope of the introduction, we will briefly overview the primary challenges to visual processing, as well as recall some of the current opinions on visual processing in the early visual systems of animals. Next, we describe the methodology we have used in our research, and discuss the presented results. We have included some additional remarks, speculations and conclusions to this discussion that were not featured in the original publications. We present the following results in the publications of this thesis. First, we empirically demonstrate that luminance and contrast are strongly dependent in natural images, contradicting previous theories suggesting that luminance and contrast were processed separately in natural systems due to their independence in the visual data. Second, we show that simple cell -like receptive fields of the primary visual cortex can be learned in the nonlinear contrast domain by maximization of independence. Further, we provide first-time reports of the emergence of conjunctive (corner-detecting) and subtractive (opponent orientation) processing due to nonlinear projection pursuit with simple objective functions related to sparseness and response energy optimization. Then, we show that attempting to extract independent components of nonlinear histogram statistics of a biologically plausible representation leads to projection directions that appear to differentiate between visual contexts. Such processing might be applicable for priming, \ie the selection and tuning of later visual processing. We continue by showing that a different kind of thresholded low-frequency priming can be learned and used to make object detection faster with little loss in accuracy. Finally, we show that in a computational object detection setting, nonlinearly gain-controlled visual features of medium complexity can be acquired sequentially as images are encountered and discarded. We present two online algorithms to perform this feature selection, and propose the idea that for artificial systems, some processing mechanisms could be selectable from the environment without optimizing the mechanisms themselves. In summary, this thesis explores learning visual processing on several levels. The learning can be understood as interplay of input data, model structures, learning objectives, and estimation algorithms. The presented work adds to the growing body of evidence showing that statistical methods can be used to acquire intuitively meaningful visual processing mechanisms. The work also presents some predictions and ideas regarding biological visual processing.

Multi-state-choice automata model for expedient learning in random environment

A new automata model Mr,k, with a conceptually significant innovation in the form of multi-state alternatives at each instance, is proposed in this study. Computer simulations of the Mr,k, model in the context of feature selection in an unsupervised environment has demonstrated the superiority of the model over similar models without this multi-state-choice innovation.

The gradual release of responsibility: A case study of teaching science inquiry skills

This thesis investigated how a year-4 teacher used a pedagogical approach referred to as the Gradual Release of Responsibility (GRR) model of instruction for teaching Science Inquiry Skills in a primary classroom. Through scaffolding her students' learning using the GRR, the teacher guided her students towards developing an understanding about Scientific Inquiry leading to the foundations of scientific literacy. A learning environment was established in which students engaged in rich conversations, designed and conducted experiments using fair testing procedures, analysed and offered justifications for results, and negotiated knowledge claims in ways similar to some of those in the scientific community.

An innovative clustering technique for unsupervized learning in the context of remotely sensed earth resources data analysis

A new clustering technique, based on the concept of immediato neighbourhood, with a novel capability to self-learn the number of clusters expected in the unsupervized environment, has been developed. The method compares favourably with other clustering schemes based on distance measures, both in terms of conceptual innovations and computational economy. Test implementation of the scheme using C-l flight line training sample data in a simulated unsupervized mode has brought out the efficacy of the technique. The technique can easily be implemented as a front end to established pattern classification systems with supervized learning capabilities to derive unified learning systems capable of operating in both supervized and unsupervized environments. This makes the technique an attractive proposition in the context of remotely sensed earth resources data analysis wherein it is essential to have such a unified learning system capability.

Crossing literacy and informed learning boundaries with Manga

This research investigated high school students’ experiences of informed learning in a literacy development workshop. It was conducted in the library of an Australian high school with a low socio-economic population. Building upon students’ fascination with Manga fiction and artwork, the workshop was part of a larger university-community engagement project Crossing Boundaries with Reading which aimed to address widespread literacy challenges at the school. The paper first provides a brief literature review that introduces the concept of informed learning, or the experience of using information to learn. In practice, informed learning fosters simultaneous learning about using information and learning about a topic. Thus, information is a transformative force that extends beyond functional information literacy skills. Then, the paper outlines the phenomenographic methodology used in this study, the workshop context and the research participants. The findings reveal three different ways that students experienced the workshop: as an art lesson; as a life lesson; and as an informed learning lesson. The discussion highlights the power of informed learning as a holistic approach to information literacy education. The study’s findings are significant as students from low socio-economic backgrounds are often at risk of experiencing disadvantage throughout their lives if they do not develop a range of literacies including the ability to use information effectively. Responding to this problem, the paper provides an empirically-based example of informed learning to support further research and develop professional practice. While the research context is limited to one high school library, the findings are of potential value for teacher-librarians, educators and information professionals elsewhere.

Learning with a mutualistic teacher

The concept of a “mutualistic teacher” is introduced for unsupervised learning of the mean vectors of the components of a mixture of multivariate normal densities, when the number of classes is also unknown. The unsupervised learning problem is formulated here as a multi-stage quasi-supervised problem incorporating a cluster approach. The mutualistic teacher creates a quasi-supervised environment at each stage by picking out “mutual pairs” of samples and assigning identical (but unknown) labels to the individuals of each mutual pair. The number of classes, if not specified, can be determined at an intermediate stage. The risk in assigning identical labels to the individuals of mutual pairs is estimated. Results of some simulation studies are presented.

Diversity in the Early Years: Intercultural Learning and Teaching

"New global contexts are presenting new challenges and new possibilities for young children and those around them. Climate change, armed conflict and poverty combine with new frontiers of discovery in science and technology to create a paradoxical picture of both threat and opportunity for our world and our children. On the one hand, children are experiencing unprecedented patterns of disparity and inequity; yet, on the other hand, they have seemingly limitless possibilities to engage with new technologies and social processes. Seismic shifts such as these are inviting new questions about the conditions that young children need to learn and thrive. Diversity in the Early Years: Intercultural Learning and Teaching explores significant aspects of working with children and adults from diverse backgrounds. It is a valuable resource for teaching early childhood pre-service teachers to raise awareness about issues of diversity - whether diversity of culture, language, education and/or gender - and for helping them to develop their own pedagogical approaches to working with diverse populations."--Publisher website

Design a simulated multimedia enriched immersive learning environment (SMILE) for nursing care of dementia patient

This thesis is to establish a framework to guide the development of a simulated, multimedia-enriched, immersive, learning environment (SMILE) framework. This framework models essential media components used to describe a scenario applied in healthcare (in a dementia context), demonstrates interactions between the components, and enables scalability of simulation implementation. The thesis outcomes also include a simulation system developed in accordance with the guidance framework and a preliminary evaluation through a user study involving ten nursing students and practicioners. The results show that the proposed framework is feasible and effective for designing a simulation system in dementia healthcare training.

Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein-protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.