Mathematical tasks and learning goals: examples from Japanese lesson study

This paper explores how to select, or design, the best mathematical task for a given learning goal. Examples are taken from a recent project in Victorian primary schools that employed Japanese lesson study as the means to provide teachers with professional learning within their own classrooms. The discussions by participating teachers and researchers provide some insights into the difficulties and solutions facing teachers intending to improve the critical thinking skills of their pupils. Examples of tasks and goals are provided.

In search of best practices in responsible gambling ( RG ) : a report on a comparative study of RG among Macau, Las Vegas, and Melbourne, presented to the MSAR Bureau of Gaming Inspection and Coordination (DICJ).

This is a project sponsored by the Asia Pacific Association for Gambling Studies (APAGS) and supported by funds from the MSAR’s Bureau of Gambling Inspection and Coordination (DICJ). The research team comprises as Chief Investigators: Prof. Zhidong Hao of the University of Macau; Prof. Linda Hancock of Deakin University, Australia, and Prof. William Thompson, University of Las Vegas (UNLV). The project research was conducted between the end of December 2012 and July 2013.
The starting point for the research was to select four out of the six casino companies licensed to operate in Macau that also operate transnationally, that is, either in Las Vegas or Melbourne. Hence, the Venetian, Wynn, MGM, and the Melco-Crown Entertainment are the focus of research. The main objectives of the project are to explore how responsible gambling is framed in each of the three jurisdictions (Macau, Las Vegas and Melbourne); how it is approached cross-jurisdictionally by each of the companies; and to assess current approaches within a broader comparative context against international best practice. The research explores Responsible Gambling measures taken by a range of stakeholders including the government/regulators in each of the three jurisdictions, casino managements, problem gambling counselling services, unions and community organizations. The research emphasizes what problems prevail, and the implications of this research for enhancing Responsible Gambling in Macau.

Adapting the task : what preservice teachers notice when adapting mathematical tasks

This paper reports on an in-depth study that explores preservice teachers’ pedagogical adaptations to a rich mathematical task. Data were collected from six elementary preservice teachers working in pairs to first solve a mathematics problem and then design adaptations to make the problem more accessible and more challenging for diverse learners. Results indicate that preservice teachers are able to draw upon a range of strategies to vary the mathematical content, the context, and the question asked. However, they also did not notice or attend to how their adaptations changed the mathematical structure of the problem. This study provides insights into what is involved in learning to adapt classroom mathematical tasks as an important pedagogical practice.

An improved Gbest guided artificial bee colony (IGGABC) algorithm for classification and prediction tasks

Artificial Neural Networks (ANN) performance depends on network topology, activation function, behaviors of data, suitable synapse's values and learning algorithms. Many existing works used different learning algorithms to train ANN for getting high performance. Artificial Bee Colony (ABC) algorithm is one of the latest successfully Swarm Intelligence based technique for training Multilayer Perceptron (MLP). Normally Gbest Guided Artificial Bee Colony (GGABC) algorithm has strong exploitation process for solving mathematical problems, however the poor exploration creates problems like slow convergence and trapping in local minima. In this paper, the Improved Gbest Guided Artificial Bee Colony (IGGABC) algorithm is proposed for finding global optima. The proposed IGGABC algorithm has strong exploitation and exploration processes. The experimental results show that IGGABC algorithm performs better than that standard GGABC, BP and ABC algorithms for Boolean data classification and time-series prediction tasks.

Improved processing speed: online computer-based cognitive training in older adults

In an increasingly aging population, a number of adults are concerned about declines in their cognitive abilities. Online computer-based cognitive training programs have been proposed as an accessible means by which the elderly may improve their cognitive abilities; yet, more research is needed in order to assess the efficacy of these programs. In the current study, a commercially available 21-day online computer-based cognitive training intervention was administered to 34 individuals aged between 53 and 75 years. The intervention consisted of computerized training in reaction time, inspection time, short-term memory for words, executive function, visual spatial acuity, arithmetic, visual spatial memory, visual scanning/discrimination, and n-back working memory. An active solitaire control group was also included. Participants were tested at baseline, posttraining and at three-weeks follow-up using a battery of neuropsychological outcome measures. These consisted of simple reaction time, complex reaction time, digit forwards and backwards, spatial working memory, digit symbol substitution, RAVLT, and trail making. Significant improvement in simple reaction time and choice reaction time task was found in the cognitive training group both posttraining and at three-weeks follow-up. However, no significant improvements on the other cognitive tasks were found. The training program was found to be successful in achieving transfer of trained cognitive abilities in speed of processing to similar untrained tasks. © 2012 Copyright Taylor and Francis Group, LLC.

Quantitative assessment of ADL: a pilot study of upper extremity reaching tasks

Effective telerehabilitation technologies enable patients with certain physiological disabilities to engage in rehabilitative exercises for performing Activities of Daily Living (ADLs). Therefore, training and assessment scenarios for the performance of ADLs are vital for the promotion for telerehabilitation. In this paper we investigate quantitatively and automatically assessing patient's kinematic ability to perform functional upper extremity reaching tasks. The shape of the movement trajectory and the instantaneous acceleration of kinematically crucial body parts, such as wrists, are used to compute the approximate entropy of the motions to represent stability (smoothness) in addition to the duration of the activity. Computer simulations were conducted to illustrate the consistency, sensitivity and robustness of the proposed method. A preliminary experiment with kinematic data captured from healthy subjects mimicking a reaching task with dyskinesia showed a high degree of correlation (Cohen's kappa 0.85 with p < 0.05) between a human observer and the proposed automatic classification tool in terms of assigning the datasets to various levels to represent the subjects' kinematic abilities to perform reaching tasks. This study supported the use of Microsoft Kinect to quantitatively evaluate the ability of individuals with involuntary movements to perform an upper extremity reaching task.

Attachment, developmental tasks and coping in the transition to university

 Students commencing university were surveyed three times to identify what individual variables facilitated positive adjustment experiences. Student’s attachment orientations were found to be strongly associated with their university adjustment, and this was mediated by students’ use of different coping strategies and their negotiation of the developmental tasks of young adulthood.

The effects of military body armour on trunk and hip kinematics during performance of manual handling tasks

Musculoskeletal injuries are reported as burdening the military. An identified risk factor for injury is carrying heavy loads; however, soldiers are also required to wear their load as body armour. To investigate the effects of body armour on trunk and hip kinematics during military-specific manual handling tasks, 16 males completed 3 tasks while wearing each of 4 body armour conditions plus a control. Three-dimensional motion analysis captured and quantified all kinematic data. Average trunk flexion for the weightiest armour type was higher compared with control during the carry component of the ammunition box lift (p < 0.001) and sandbag lift tasks (p < 0.001). Trunk rotation ROM was lower for all armour types compared with control during the ammunition box place component (p < 0.001). The altered kinematics with body armour occurred independent of armour design. In order to optimise armour design, manufacturers need to work with end-users to explore how armour configurations interact with range of personal and situational factors in operationally relevant environments. Practitioner Summary: Musculoskeletal injuries are reported as burdening the military and may relate to body armour wear. Body armour increased trunk flexion and reduced trunk rotation during military-specific lifting and carrying tasks. The altered kinematics may contribute to injury risk, but more research is required.

ANGEL: agent-based scheduling for real-time tasks in virtualized clouds

The success of cloud computing makes an increasing number of real-time applications such as signal processing and weather forecasting run in the cloud. Meanwhile, scheduling for real-time tasks is playing an essential role for a cloud provider to maintain its quality of service and enhance the system's performance. In this paper, we devise a novel agent-based scheduling mechanism in cloud computing environment to allocate real-time tasks and dynamically provision resources. In contrast to traditional contract net protocols, we employ a bidirectional announcement-bidding mechanism and the collaborative process consists of three phases, i.e., basic matching phase, forward announcement-bidding phase and backward announcement-bidding phase. Moreover, the elasticity is sufficiently considered while scheduling by dynamically adding virtual machines to improve schedulability. Furthermore, we design calculation rules of the bidding values in both forward and backward announcement-bidding phases and two heuristics for selecting contractors. On the basis of the bidirectional announcement-bidding mechanism, we propose an agent-based dynamic scheduling algorithm named ANGEL for real-time, independent and aperiodic tasks in clouds. Extensive experiments are conducted on CloudSim platform by injecting random synthetic workloads and the workloads from the last version of the Google cloud tracelogs to evaluate the performance of our ANGEL. The experimental results indicate that ANGEL can efficiently solve the real-time task scheduling problem in virtualized clouds.

FESTAL: fault-tolerant elastic scheduling algorithm for real-time tasks in virtualized clouds

As clouds have been deployed widely in various fields, the reliability and availability of clouds become the major concern of cloud service providers and users. Thereby, fault tolerance in clouds receives a great deal of attention in both industry and academia, especially for real-time applications due to their safety critical nature. Large amounts of researches have been conducted to realize fault tolerance in distributed systems, among which fault-tolerant scheduling plays a significant role. However, few researches on the fault-tolerant scheduling study the virtualization and the elasticity, two key features of clouds, sufficiently. To address this issue, this paper presents a fault-tolerant mechanism which extends the primary-backup model to incorporate the features of clouds. Meanwhile, for the first time, we propose an elastic resource provisioning mechanism in the fault-tolerant context to improve the resource utilization. On the basis of the fault-tolerant mechanism and the elastic resource provisioning mechanism, we design novel fault-tolerant elastic scheduling algorithms for real-time tasks in clouds named FESTAL, aiming at achieving both fault tolerance and high resource utilization in clouds. Extensive experiments injecting with random synthetic workloads as well as the workload from the latest version of the Google cloud tracelogs are conducted by CloudSim to compare FESTAL with three baseline algorithms, i.e., Non-M igration-FESTAL (NMFESTAL), Non-Overlapping-FESTAL (NOFESTAL), and Elastic First Fit (EFF). The experimental results demonstrate that FESTAL is able to effectively enhance the performance of virtualized clouds.