A hybrid ART-GRNN online learning neural network with a ε-insensitive loss function

In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical studies to assess the effectiveness of GART in classification, regression, and time series prediction tasks is conducted. The results demonstrate that GART is able to produce good performances as compared with those of other methods, including the online sequential extreme learning machine (OSELM) and sequential learning radial basis function (RBF) neural network models.

Leading the evaluation of institutional online learning environments for quality enhancement in times of change

This paper reports on findings from a nationally funded project which aims to design and implement a quality management framework for online learning environments (OLEs). Evaluation is a key component of any quality management system and it is this aspect of the framework that is the focus of this paper. In developing the framework initial focus groups were conducted at the five participating institutions. These revealed that, although regarded as important, there did not appear to be a shared understanding of the nature and purpose of evaluation. A second series of focus groups revealed there were multiple perspectives arising from those with a vested interest in online learning. These perspectives will be outlined. Overall, how evaluation was undertaken was highly variable within and across the five institutions reflecting where they were at in relation to the development of their OLE.

Leading the quality management of online learning environments in Australian higher education

The paper presents the findings of the first year of a nationally funded Australian Learning and Teaching Council (ALTC) project on the quality management of online learning environments by and through distributed leadership. The project is being undertaken by five Australian universities with major commitments to online and distance education. Each university, however, has a distinctive location, history and profile in the sector. The first year of the project has seen the development of a quality management framework with six interrelated elements. The framework is being applied, refined and validated in the second year of the project. Allied with the development of the framework, was the conduct of focus groups at each of the five partner institutions in the middle of the first year. These focus groups composed a range of staff involved collectively in the leadership of the organisation's online learning environment. Prominence was given to the nature and value of strategic planning, due diligence conducted in selecting and mainstreaming technologies, evaluation approaches informing decision making, and the various relationships between different leadership levels and domains. A number of key issues which emerged relating to the elements identified in the framework are examined.

Analysis of psycholinguistic processes and topics in online autism communities

Current growth of individuals on the autism spectrum disorder (ASD) requires continuous support and care. With the popularity of social media, online communities of people affected by ASD emerge. This paper presents an analysis of these online communities through understanding aspects that differentiate such communities. In this paper, the aspects given are not expressed in terms of friendship, exchange of information, social support or recreation, but rather with regard to the topics and linguistic styles that people express in their on-line writing. Using data collected unobtrusively from LiveJournal, we analyze posts made by ten autism communities in conjunction with those made by a control group of standard communities. Significant differences have been found between autism and control communities when characterized by latent topics of discussion and psycholinguistic features. Latent topics are found to have greater predictive power than linguistic features when classifying blog posts as either autism or control community. This study suggests that data mining of online blogs has the potential to detect clinically meaningful data. It opens the door to possibilities including sentinel risk surveillance and harnessing the power in diverse large datasets.

Exploring distributed leadership for the quality management of online learning environments

Online learning environments (OLEs) are complex information technology (IT) systems that intersect with many areas of university organisation. Distributed models of leadership have been proposed as appropriate for the good governance of OLEs. Based on theoretical and empirical research, a group of Australian universities proposed a framework for the quality management of OLEs, and sought to validate the model via a survey of Australasian university representatives with OLE leadership responsibility. For the framework elements: Planning and Resourcing were rated most important; Organisational structure was rated least important; Technologies were rated low in importance and high in satisfaction; Resourcing and Evaluation were rated low in satisfaction; and Resourcing had the highest rating of importance coupled with low satisfaction. Considering distributed leadership in their institution, respondents reported that the organisational alignments represented by 'official' reporting and peer relationships were significantly more important and more effective than the organisational alignments linking the formal and informal leaders. From a range of desirable characteristics of distributed leadership, 'continuity and sustainability' received the highest rating of importance and a low rating of 'in evidence' - there are concerns about the sustainability of distributed leadership for the governance of OLEs in universities.

Quality management of online learning environments : final report of the project : Building distributed leadership in designing and implementing a quality management framework for online learning environments

 The project developed and disseminated, through a distributed leadership approach, an overall framework for the quality management of online learning environments (OLEs) in Australian higher education. The Six Elements of the Online Learning Environment (6EOLE) Quality Management Framework and its guidelines was constructed based on various data collection methods deployed in the project.

The 6EOLE Quality Management Framework, displayed on page six, and accompanying guidelines (i.e. An evidence-based approach to implementation, and A condensed guide) can be used to guide management action to assure and continuously improve the quality of an organisation’s OLE where environmental factors are relatively stable, at least for a period....

Occasional paper : Towards the development of an online learning environment (OLE) quality management framework

The project is committed to understanding, recognising and developing various forms of institutionally relevant distributed leadership in developing and trialling various components of a quality management framework for online learning environments in Australian higher education. This paper provides an overview of issues related to the management and improvement of quality, including in the context of higher education. In response to the complex and multi-dimensional nature of both quality and online learning environments (OLEs), the concept of a framework for organising policies, procedures and actions relating to the good governance of OLEs can be found in the literature. Such frameworks vary in scope, format and title, and a (non-exhaustive) sample is presented in summary here. Key learnings that can be drawn from the exemplars frameworks and the related literature include:
- the processes for the design of such frameworks;
- the components of such frameworks;
- the measurement mechanisms and metrics employed in such frameworks; and
- the validation of such frameworks.