Building the computer mediated distance learning community: Issues and observations drawn from teaching and learning

This paper discusses computer mediated distance learning on a Master's level course in the UK and student perceptions of this as a quality learning environment.

Unsupervised online learning of condition-invariant images for place recognition

This paper presents an online, unsupervised training algorithm enabling vision-based place recognition across a wide range of changing environmental conditions such as those caused by weather, seasons, and day-night cycles. The technique applies principal component analysis to distinguish between aspects of a location’s appearance that are condition-dependent and those that are condition-invariant. Removing the dimensions associated with environmental conditions produces condition-invariant images that can be used by appearance-based place recognition methods. This approach has a unique benefit – it requires training images from only one type of environmental condition, unlike existing data-driven methods that require training images with labelled frame correspondences from two or more environmental conditions. The method is applied to two benchmark variable condition datasets. Performance is equivalent or superior to the current state of the art despite the lesser training requirements, and is demonstrated to generalise to previously unseen locations.

Critical success factors in distance learning construction programs at Central Queensland University : students’ perspective

Distance education has gone through rapid expansion over the years. Many Australian universities are pushing the use of distance education in delivering construction education programs. However, the critical success factors (CSFs) in distance learning construction programs (DLCPs) are not fully understood. More importantly, students’ demographic features may affect the selection of distance education technologies. Situation-matching strategies should therefore be taken by universities or institutions with different student cohorts. A survey is adopted in Central Queensland University (CQU) to identify and rank the critical success factors in a DLCP in Australia where there is a significant number of earner-learners and students with low socioeconomic background. The findings suggest that the most important CSFs include access to computers and internet, reliability of web-based learning sites, high relevance and clarity of learning materials and assessment items, the availability of web-based learning sites that can be easily manipulated, and the capability of the instructors to provide well-structured courses. The findings also suggest that students with low socioeconomic background have more rigorous requirements on interface design, instructors’ support, and the integration of practical components into courses. The results provide good guidance of the design and delivery of DLCPs and will be useful for universities and institutions that are seeking to implement the distance mode in construction education.

Improving deep convultional neural networks with unsupervised feature learning

The latest generation of Deep Convolutional Neural Networks (DCNN) have dramatically advanced challenging computer vision tasks, especially in object detection and object classification, achieving state-of-the-art performance in several computer vision tasks including text recognition, sign recognition, face recognition and scene understanding. The depth of these supervised networks has enabled learning deeper and hierarchical representation of features. In parallel, unsupervised deep learning such as Convolutional Deep Belief Network (CDBN) has also achieved state-of-the-art in many computer vision tasks. However, there is very limited research on jointly exploiting the strength of these two approaches. In this paper, we investigate the learning capability of both methods. We compare the output of individual layers and show that many learnt filters and outputs of the corresponding level layer are almost similar for both approaches. Stacking the DCNN on top of unsupervised layers or replacing layers in the DCNN with the corresponding learnt layers in the CDBN can improve the recognition/classification accuracy and training computational expense. We demonstrate the validity of the proposal on ImageNet dataset.

The judicious utilization of new technologies through authentic learning in higher education : a case study

Though technology holds significant promise for enhanced teaching and learning it is unlikely to meet this promise without a principled approach to course design. There is burgeoning discourse about the use of technological tools and models in higher education, but much of the discussion is fixed upon distance learning or technology based courses. This paper will develop and propose a balanced model for effective teaching and learning for “on campus” higher education, with particular emphasis on the opportunities for revitalisation available through the judicious utilisation of new technologies. It will explore the opportunities available for the creation of more authentic learning environments through the principled design. Finally it will demonstrate with a case study how these have come together enabling the creation of an effective and authentic learning environment for one pre-service teacher education course at the University of Queensland.

Design and delivery of a distance education programme : educating Vietnamese nurse academics from Australia

In 2008 a move away from medical staff providing nursing education in Vietnam saw the employment of many new nurse academics. To assist in the instruction of these novice academics and provide them with sound teaching and learning practice as well as curriculum design and implementation skills, Queensland University of Technology (QUT) successfully tendered an international grant. One of QUT’s initiatives in educating the Vietnamese academics was a distance learning programme. Developed specifically for Vietnamese nurse academics, the programme was designed for Australian based delivery to academics in Vietnam. This paper will present an overview of why four separate modules were utilised for the delivery of content (modules were delivered at a rate of one per semester). It will address bilingual online discussion boards which were used in each of the modules and the process of moderating these given comments were posted in both Vietnamese and English. It will describe how content was scaffolded across four modules and how the modules themselves modelled new teaching delivery strategies. Lastly, it will discuss the considerations of programme delivery given the logistics of an Australian based delivery. Feedback from the Vietnamese nurse academics across their involvement in the programme (and at the conclusion of their fourth and final module) has been overwhelmingly positive. Feedback suggests the programme has altered teaching and assessment approaches used by some Vietnamese nurse academics. Additionally, Vietnamese nurse academics are reporting that they are engaging more with the application of their content indicating a cultural shift in the approach taken in Vietnamese nurse education.

Distance education and the application of academagogy : a case study

The education sector has dramatically changed in the past half decade. In a time of globalisation of education and tightening budgets, various paradigm shifts and challenges have rapidly changed learning and teaching. These include: meeting student expectation for more engaging, more interactive learning experiences, the increased focus to deliver content online, and the complexities of fast-changing technologies. Rising to these challenges and responding to them is a complex and multi-faceted task. This paper discusses educational theories and issues and explores current educational practices in the context of teaching undergraduate students via distance education in the university context. A case study applies a framework drawn from engineering education using the learner-centric concept of academagogy. Results showed that academagogy actively empowers students to build effective learning, and engages facilitators in meaningful teaching and delivery methods.

Analysis of distance learner value assessment of distance education in engineering

Numerous research studies have evaluated whether distance learning is a viable alternative to traditional learning methods. These studies have generally made use of cross-sectional surveys for collecting data, comparing distance to traditional learners with intent to validate the former as a viable educational tool. Inherent fundamental differences between traditional and distance learning pedagogies, however, reduce the reliability of these comparative studies and constrain the validity of analyses resulting from this analytical approach. This article presents the results of a research project undertaken to analyze expectations and experiences of distance learners with their degree programs. Students were given surveys designed to examine factors expected to affect their overall value assessment of their distance learning program. Multivariate statistical analyses were used to analyze the correlations among variables of interest to support hypothesized relationships among them. Focusing on distance learners overcomes some of the limitations with assessments that compare off- and on-campus student experiences. Evaluation and modeling of distance learner responses on perceived value for money of the distance education they received indicate that the two most important influences are course communication requirements, which had a negative effect, and course logistical simplicity, which revealed a positive effect. Combined, these two factors accounted for approximately 47% of the variability in perceived value for money of the educational program of sampled students. A detailed focus on comparing expectations with outcomes of distance learners complements the existing literature dominated by comparative studies of distance and nondistance learners.

Transnational distance education : cultural and quality considerations

There have now been two decades of rhetoric on the need for culturally and contextually appropriate perspectives in international education. However, the extent to which courses, provision and pedagogy have truly reflected differences in cultural characteristics and learning preferences is still open to question. Little attention has been paid to these matters in quality assurance frameworks. This chapter discusses these issues and draws upon Hofstede’s cultural dimensions framework and studies into Asian pedagogy and uses of educational technology. It proposes a benchmark and performance indicators for assuring cultural, contextual, educational and technological appropriateness in the provision of transnational distance education in Asia by Australian universities.

Blended learning extension in a global collaborative learning environment

The advancement of technology and the internet have created global collaborative learning opportunities and changed learning and teaching in any discipline around the world, including in developing countries. The availability of web-based resources and high-speed internet infrastructure has extended the opportunities to conduct blended learning and new ways of distance learning beyond virtual class room webinars. The aim of this exploratory paper is to review the challenges and opportunities for increasing student engagement in virtual learning. A reflective analysis of international collaborative learning case studies, published articles and practices in virtual learning is used to explore the extension of blended learning organised and participated by institutions from multiple countries. The lessons learnt from flexible learning delivery in professional practice courses in Property Education are used to evaluate potential extensions of blended learning implementation in a global context.

The development and evaluation of an E-learning package to enhance interprofessional learning

Experience’s of VLE - In some cases was used as little more than a “dumping” ground for lecture/tutorial material. - Allowed easy access to material for staff and students alike. - Attempted use as an interactive teaching and learning platform predominately from a distance learning perspective. Students worked very much in isolation. - Support from Academic and Clinical staff adhoc at times.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

Research Monograph: Building Online Participatory Systems: Towards the Community Based Interactive Environmental Decision Support Systems

The importance of broadening community participation in environmental decision-making is widely recognized and lack of participation in this process appears to be a perennial problem. In this context, there have been calls from some academics for the more extensive use of geographic information systems (GIS) and distance learning technologies, accessible via the Internet, as a possible means to inform and empower communities. However, a number of problems exist. For instance, at present the scope for online interaction between policy-makers and citizens is currently limited. Contemporary web-based environmental information systems suffer from this lack of interactivity on the one hand and on the other hand from the apparent complexity for the lay user. This paper explores the issue of online community participation at the local level and attempts to construct a framework for a new (and potentially more effective) model of online participatory decision-making. The key components, system architecture and stages of such a model are introduced. This model, referred to as a ‘Community Based Interactive Environmental Decision Support System’, incorporates advanced information technologies, distance learning and community involvement tools which will be applied and evaluated in the field through a pilot project in Tokyo in the summer of 2002.

Planning online : a community-based interactive decision-making model

The first use of computing technologies and the development of land use models in order to support decision-making processes in urban planning date back to as early as mid 20th century. The main thrust of computing applications in urban planning is their contribution to sound decision-making and planning practices. During the last couple of decades many new computing tools and technologies, including geospatial technologies, are designed to enhance planners' capability in dealing with complex urban environments and planning for prosperous and healthy communities. This chapter, therefore, examines the role of information technologies, particularly internet-based geographic information systems, as decision support systems to aid public participatory planning. The chapter discusses challenges and opportunities for the use of internet-based mapping application and tools in collaborative decision-making, and introduces a prototype internet-based geographic information system that is developed to integrate public-oriented interactive decision mechanisms into urban planning practice. This system, referred as the 'Community-based Internet GIS' model, incorporates advanced information technologies, distance learning, sustainable urban development principles and community involvement techniques in decision-making processes, and piloted in Shibuya, Tokyo, Japan.