Intelligent agent supported personalization for virtual learning environments

Virtual learning environments (VLEs) are computer-based online learning environments, which provide opportunities for online learners to learn at the time and location of their choosing, whilst allowing interactions and encounters with other online learners, as well as affording access to a wide range of resources. They have the capability of reaching learners in remote areas around the country or across country boundaries at very low cost. Personalized VLEs are those VLEs that provide a set of personalization functionalities, such as personalizing learning plans, learning materials, tests, and are capable of initializing the interaction with learners by providing advice, necessary instant messages, etc., to online learners. One of the major challenges involved in developing personalized VLEs is to achieve effective personalization functionalities, such as personalized content management, learner model, learner plan and adaptive instant interaction. Autonomous intelligent agents provide an important technology for accomplishing personalization in VLEs. A number of agents work collaboratively to enable personalization by recognizing an individual's eLeaming pace and reacting correspondingly. In this research, a personalization model has been developed that demonstrates dynamic eLearning processes; secondly, this study proposes an architecture for PVLE by using intelligent decision-making agents' autonomous, pre-active and proactive behaviors. A prototype system has been developed to demonstrate the implementation of this architecture. Furthemore, a field experiment has been conducted to investigate the performance of the prototype by comparing PVLE eLearning effectiveness with a non-personalized VLE. Data regarding participants' final exam scores were collected and analyzed. The results indicate that intelligent agent technology can be employed to achieve personalization in VLEs, and as a consequence to improve eLeaming effectiveness dramatically.

Construction of an Intraplate Fault System Model of South Australia, and Simulation Tool for the iSERVO Institute Seed Project

To foster ongoing international cooperation beyond ACES (APEC Cooperation for Earthquake Simulation) on the simulation of solid earth phenomena, agreement was reached to work towards establishment of a frontier international research institute for simulating the solid earth: iSERVO = International Solid Earth Research Virtual Observatory institute (http://www.iservo.edu.au). This paper outlines a key Australian contribution towards the iSERVO institute seed project, this is the construction of: (1) a typical intraplate fault system model using practical fault system data of South Australia (i.e., SA interacting fault model), which includes data management and editing, geometrical modeling and mesh generation; and (2) a finite-element based software tool, which is built on our long-term and ongoing effort to develop the R-minimum strategy based finite-element computational algorithm and software tool for modelling three-dimensional nonlinear frictional contact behavior between multiple deformable bodies with the arbitrarily-shaped contact element strategy. A numerical simulation of the SA fault system is carried out using this software tool to demonstrate its capability and our efforts towards seeding the iSERVO Institute.

Simulation of air traffic controllers' behaviour using the operator choice model

The Operator Choice Model (OCM) was developed to model the behaviour of operators attending to complex tasks involving interdependent concurrent activities, such as in Air Traffic Control (ATC). The purpose of the OCM is to provide a flexible framework for modelling and simulation that can be used for quantitative analyses in human reliability assessment, comparison between human computer interaction (HCI) designs, and analysis of operator workload. The OCM virtual operator is essentially a cycle of four processes: Scan Classify Decide Action Perform Action. Once a cycle is complete, the operator will return to the Scan process. It is also possible to truncate a cycle and return to Scan after each of the processes. These processes are described using Continuous Time Probabilistic Automata (CTPA). The details of the probability and timing models are specific to the domain of application, and need to be specified using domain experts. We are building an application of the OCM for use in ATC. In order to develop a realistic model we are calibrating the probability and timing models that comprise each process using experimental data from a series of experiments conducted with student subjects. These experiments have identified the factors that influence perception and decision making in simplified conflict detection and resolution tasks. This paper presents an application of the OCM approach to a simple ATC conflict detection experiment. The aim is to calibrate the OCM so that its behaviour resembles that of the experimental subjects when it is challenged with the same task. Its behaviour should also interpolate when challenged with scenarios similar to those used to calibrate it. The approach illustrated here uses logistic regression to model the classifications made by the subjects. This model is fitted to the calibration data, and provides an extrapolation to classifications in scenarios outside of the calibration data. A simple strategy is used to calibrate the timing component of the model, and the results for reaction times are compared between the OCM and the student subjects. While this approach to timing does not capture the full complexity of the reaction time distribution seen in the data from the student subjects, the mean and the tail of the distributions are similar.

3D statistical shape models to embed spatial relationship information

This paper presents the creation of 3D statistical shape models of the knee bones and their use to embed information into a segmentation system for MRIs of the knee. We propose utilising the strong spatial relationship between the cartilages and the bones in the knee by embedding this information into the created models. This information can then be used to automate the initialisation of segmentation algorithms for the cartilages. The approach used to automatically generate the 3D statistical shape models of the bones is based on the point distribution model optimisation framework of Davies. Our implementation of this scheme uses a parameterized surface extraction algorithm, which is used as the basis for the optimisation scheme that automatically creates the 3D statistical shape models. The current approach is illustrated by generating 3D statistical shape models of the patella, tibia and femoral bones from a segmented database of the knee. The use of these models to embed spatial relationship information to aid in the automation of segmentation algorithms for the cartilages is then illustrated.

Automatic segmentation of the knee bones using 3D active shape models

This paper presents an automated segmentation approach for MR images of the knee bones. The bones are the first stage of a segmentation system for the knee, primarily aimed at the automated segmentation of the cartilages. The segmentation is performed using 3D active shape models (ASM), which are initialized using an affine registration to an atlas. The 3D ASMs of the bones are created automatically using a point distribution model optimization scheme. The accuracy and robustness of the segmentation approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images.