Hierarchical Bayesian models of subtask learning

 The current study used Bayesian hierarchical methods to challenge and extend previous work on subtask learning consistency. A general model of individual-level subtask learning was proposed focusing on power and exponential functions with constraints to test for inconsistency. To study subtask learning, we developed a novel computer-based booking task, which logged participant actions, enabling measurement of strategy use and subtask performance. Model comparison was performed using deviance information criterion (DIC), posterior predictive checks, plots of model fits, and model recovery simulations. Results showed that although learning tended to be monotonically decreasing and decelerating, and approaching an asymptote for all subtasks, there was substantial inconsistency in learning curves both at the group- and individual-levels. This inconsistency was most apparent when constraining both the rate and the ratio of learning to asymptote to be equal across subtasks, thereby giving learning curves only 1 parameter for scaling. The inclusion of 6 strategy covariates provided improved prediction of subtask performance capturing different subtask learning processes and subtask trade-offs. In addition, strategy use partially explained the inconsistency in subtask learning. Overall, the model provided a more nuanced representation of how complex tasks can be decomposed in terms of simpler learning mechanisms.

Picturing evaporation : learning science literacy through a particle representation

Evaporation is mostly taught in primary schools through a water cycle representation. This has its limitations in explaining mechanisms and local effects such as drops drying in a closed room, condensation on cold surfaces, or how we smell liquids. In this paper the authors describe a classroom sequence of activities for Grade 5 students that explored the use of a particle model in conjunction with a range of representational modes, to explain evaporation phenomena. In interviews the authors explored with students their visual and verbal accounts of particles, modelling a process of teacher-mediated negotiation of multiple representations. From the evidence, the authors argue that difficulties in understanding evaporation are inherently representational, and that by engaging with the multiple literacies of science teachers can support significant advances in conceptual learning.

Creating and sustaining quality e-learning environments of enduring value for teachers and learners

Australian universities continue staking a claim on the future of e-learning, acquiring Learning Management Systems (LMS) as rapidly as universities overseas. Much is published on processes and criteria for selecting the best LMS for an organisation's needs and attempts to establish training and support mechanisms for deploying these systems. Beyond initial efforts to commission these technologies, particularly in the hands of teachers and students, what should happen to ensure these commitments yield real educational value in the long term? The search for and realisation of systemic and substantial new value requires a more profound reconceptualisation of what it means to design and work within contemporary learning environments, incorporating e-learning, in support of excellence in educational outcomes. This demands the foregrounding of the role of the academic teacher in the system in relation to other parties who can make important educational contributions in support of student learning. Central to new strategies is a transformation of the role of academic teacher, but on terms understood by them and supportive of their educational values. Six areas of value creation for teachers and learners are considered in relation to this transformation.

Academic social networks affecting the adoption of e-learning in Turkey

This article reports findings from an ethnographic study of e-leaming adopters in Turkey and examines ways in which cultural factors shape the adoption and use of infonnation technology for online teaching. This research focuses on influential early adopters in the tertiary education sector in Turkey who have become change agents by inspiring small networks of their peers into e-learning. The study examines the operation of trust and inspiration in networking and teamwork in the Asian academic environment. A key finding of this research is that the early adopters of e-Ieaming tend to become change agents in small groups and networks. This research sheds light on the mechanisms by which the process of e-Ieaming adoption relies on social networks and connections.

Learning through research : a regional university and its community

Regional universities bring a research capacity to their home locations that is rarely available through other mechanisms in the region. University initiated research projects conducted locally can provide an opportunity for regional communities to examine their practices through a different lens. Through these projects, researchers in regional universities whose research includes sites internal and external to the region are able to connect their region to national and global contexts. Research presents many opportunities for regional universities and their communities to learn together.

There is some evidence that policy-makers are aware of the importance of behavioural relationships in the engagement of regional universities with communities. Policy documents tend to focus on the macro, institutional level benefits, structural incentives and impediments to university and community engagement. This paper examines research from one faculty based on a regional university campus: the Faculty of Education at the University of Tasmania in Launceston in Australia. It takes a micro view, considering benefits and factors influencing success for small research teams and individual researchers and their community research associates. A learning community approach, where synergies from collaboration can generate new knowledge for the benefit of all university and community players, emerges as an effective model for regional engagement through research.

Complexity and learning behaviours in product innovation

Successful product innovation and the ability of companies to continuously improve their innovation processes are rapidly becoming essential requirements for competitive advantage and long-term growth in both manufacturing and service industries. It is now recognized that companies must develop innovation capabilities across all stages of the product development, manufacture, and distribution cycle. These Continuous Product Innovation (CPI) capabilities are closely associated with a company’s knowledge management systems and processes. Companies must develop mechanisms to continuously improve these capabilities over time.  Using results of an international survey on CPI practices, sets of companies are identified by similarities in specific contingencies related to their complexity of product, process, technological, and customer interface. Differences between the learning behaviors found present in the company groups and in the levers used to develop and support these behaviors are identified and discussed. This paper also discusses appropriate mechanisms for firms with similar complexities, and some approaches they can use to improve their organizational learning and product innovation.

High accuracy context recovery using clustering mechanisms

This paper examines the recovery of user context in indoor environmnents with existing wireless infrastructures to enable assistive systems. We present a novel approach to the extraction of user context, casting the problem of context recovery as an unsupervised, clustering problem. A well known density-based clustering technique, DBSCAN, is adapted to recover user context that includes user motion state, and significant places the user visits from WiFi observations consisting of access point id and signal strength. Furthermore, user rhythms or sequences of places the user visits periodically are derived from the above low level contexts by employing state-of-the-art probabilistic clustering technique, the Latent Dirichiet Allocation (LDA), to enable a variety of application services. Experimental results with real data are presented to validate the proposed unsupervised learning approach and demonstrate its applicability.

Procedural learning deficits in specific language impairment (SLI): a meta-analysis of serial reaction time task performance

Meta-analysis and meta-regression were used to evaluate whether evidence to date demonstrates deficits in procedural memory in individuals with specific language impairment (SLI), and to examine reasons for inconsistencies of findings across studies. The Procedural Deficit Hypothesis (PDH) proposes that SLI is largely explained by abnormal functioning of the frontal-basal ganglia circuits that support procedural memory. It has also been suggested that declarative memory can compensate for at least some of the problems observed in individuals with SLI. A number of studies have used Serial Reaction Time (SRT) tasks to investigate procedural learning in SLI. In this report, results from eight studies that collectively examined 186 participants with SLI and 203 typically-developing peers were submitted to a meta-analysis. The average mean effect size was .328 (CI95: .071, .584) and was significant. This suggests SLI is associated with impairments of procedural learning as measured by the SRT task. Differences among individual study effect sizes, examined with meta-regression, indicated that smaller effect sizes were found in studies with older participants, and in studies that had a larger number of trials on the SRT task. The contributions of age and SRT task characteristics to learning are discussed with respect to impaired and compensatory neural mechanisms in SLI.

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.

Capacity-building in nonprofit organizations : Is there a blended learning paradigm for the sector?

Blended learning models are widely-used, successful training vehicles for e-learning and workplace training, in corporate as well as higher education environments. Increasingly, Web 2.0 applications, imbedded within blended learning models, are being recognized for their utility in these settings. Concern for the sustainability and relevance of nonprofit organizations has sharpened the interest in building effective capacity-building models for the sector. Can Web 2.0 technologies enhance capacity-building models in the Third Sector? Because blended learning is a remarkably adaptable and fluid model, its potential for transforming capacity-building models in the nonprofit sector is thought to be significant. This paper introduces the concept of transformational approaches to capacity-building and asks if blended learning paradigms that incorporate interactive next-generation technologies might strike a responsive chord in the sector. The authors present research to date on blended learning and capacity-building to lay the foundation for the introduction of one blended learning model for training and education in the nonprofit sector. While the authors suggest that blended learning, as it is evolving, is the key to driving innovation in capacity-building models, they recognize that tailoring blended learning to the audience is critical in achieving success. It is suggested that for optimal results, capacity-building efforts should be built on holistic approaches to the integration of individual self-actualization goals with mechanisms for organizational and sector empowerment, using the technologies imbedded with blended learning. © 2011 Springer-Verlag.

Impact of Assurance of Learning (AOL) in programming course for novices

One of the aims of any higher education institution is to align its curriculum with program learning goals. Programs which ensure proper learning have positive effects on students, instructors, departments and also on the higher education institution itself. This paper discusses the implementation and effects of Assurance Of Learning (AOL) processes on introductory programming (IP) courses. It elaborates five stages of AOL to align program learning goals with IP curriculum. Then, it discusses how the AOL process identifies shortcomings in the assessment methods of IP courses. Furthermore, it enlightens how the assessment findings, as a result of the AOL process, provide mechanisms to address the drawbacks during the delivery of such courses. Feedback on

Evidencing learning outcomes: a multi-level, multi-dimensional course alignment model

This conceptual framework proposes a multi-level, multi-dimensional course alignment model to implement a contextualised constructive alignment of rubric design that authentically evidences and assesses learning outcomes. By embedding quality control mechanisms at each level for each dimension, this model facilitates the development of an aligned curriculum. The use of rubrics is a crucial theme for many higher education institutions owing to the binding requirement by universities to provide evidence to quality assurance agencies. The success of evidencing learning outcomes through rubrics, however, is only one piece of the puzzle. The other is the contextualised constructive alignment of intertwined factors. Despite the significance of embedding these factors, there has been little, if any, systematic framework in this area. The two key instrumental forces underpinning the conception of this model are: seeking external accreditation and the implementation of programme enhancement thus realising the strategic agenda for an Australian university.

Asymmetric self-learning for tackling Twitter spam drift

Spam has become a critical problem on Twitter. In order to stop spammers, security companies apply blacklisting services to filter spam links. However, over 90% victims will visit a new malicious link before it is blocked by blacklists. To eliminate the limitation of blacklists, researchers have proposed a number of statistical features based mechanisms, and applied machine learning techniques to detect Twitter spam. In our labelled large dataset, we observe that the statistical properties of spam tweets vary over time, and thus the performance of existing ML based classifiers are poor. This phenomenon is referred as 'Twitter Spam Drift'. In order to tackle this problem, we carry out deep analysis of 1 million spam tweets and 1 million non-spam tweets, and propose an asymmetric self-learning (ASL) approach. The proposed ASL can discover new information of changed tweeter spam and incorporate it into classifier training process. A number of experiments are performed to evaluate the ASL approach. The results show that the ASL approach can be used to significantly improve the spam detection accuracy of using traditional ML algorithms.

Saliency detection using hierarchical manifold learning

Saliency detection is critical to many applications in computer vision by eliminating redundant backgrounds. The saliency detection approaches can be divided into two categories, i.e., top-down and bottom-up. Among them, bottom-up models have attracted more attention due to their simple mechanisms. However, many existing bottom-up models are not robust to crowded backgrounds because of missing salient regions within feedforward frameworks which is often not effective for complex scenes. We tackle these problems by modifying and extending a bottom-up saliency detection model through three phases, (1) constructing a hierarchical sequence of images from the perspective of entropy, (2) estimated mid-level cues are used as feedback information, (3) subsequently generating saliency maps by global context and local uniqueness in a graph-based framework. We also compare the proposed bottom-up model with state-of-the-art approaches on two benchmark datasets to evaluate its saliency detection performance. The experimental results demonstrate that the proposed bottom-up saliency detection approach is not only robust to both cluttered and clean scenes, but also able to obtain objects with different scales.

A performance evaluation of machine learning-based streaming spam tweets detection

The popularity of Twitter attracts more and more spammers. Spammers send unwanted tweets to Twitter users to promote websites or services, which are harmful to normal users. In order to stop spammers, researchers have proposed a number of mechanisms. The focus of recent works is on the application of machine learning techniques into Twitter spam detection. However, tweets are retrieved in a streaming way, and Twitter provides the Streaming API for developers and researchers to access public tweets in real time. There lacks a performance evaluation of existing machine learning-based streaming spam detection methods. In this paper, we bridged the gap by carrying out a performance evaluation, which was from three different aspects of data, feature, and model. A big ground-truth of over 600 million public tweets was created by using a commercial URL-based security tool. For real-time spam detection, we further extracted 12 lightweight features for tweet representation. Spam detection was then transformed to a binary classification problem in the feature space and can be solved by conventional machine learning algorithms. We evaluated the impact of different factors to the spam detection performance, which included spam to nonspam ratio, feature discretization, training data size, data sampling, time-related data, and machine learning algorithms. The results show the streaming spam tweet detection is still a big challenge and a robust detection technique should take into account the three aspects of data, feature, and model.