Cognitive trait model for persistent and fine-tuned student modelling in adaptive virtual learning environments : a thesis presented in partial fulfilment of the requirements for the degree of Master of Information Science in Information Systems at Massey University

The increasing need for individualised instructional in both academic and corporate training environment encourages the emergence and popularity of adaptivity in virtual learning environments (VLEs). Adaptivity can be applied in VLEs as adaptivity content presentation, which generates the learning content adaptively to suit the particular learner's aptitude, and as adaptive navigational control, which dynamically modifies the structure of the virtual learning environment presented to the learner in order to prevent overloading the learner's cognitive load. Techniques for both adaptive content presentation and adaptive navigational control need to be integrated in a conceptual framework so their benefits can be synthesised to obtain a synergic result. Exploration space control (ESC) theory attempts to adjust the learning space, called exploration space, to allow the learners to reach an adequate amount of information that their cognitive load is not overloaded. Multiple presentation (MR) approach provides guidelines for the selection of multimedia objects for both the learning content presentation and as navigational links. ESC is further formalised by including the consideration of individual learner's cognitive traits, which are the cognitive characteristics and abilities the learner relevant in the process of learning. Cognitive traits selected in the formalisation include working memory capacity, inductive reasoning skill, associative learning skill, and information processing speed. The formalisation attempts to formulate a guideline on how the learning content and navigational space should be adjusted in order to support a learner with a particular set of cognitive traits. However, in order to support the provision of adaptivity, the learners and their activities in the VLEs need to be profiled; the profiling process is called student modelling. Student models nowadays can be categorised into state models, and process models. State models record learners' progress as states (e.g. learned, not learned), whereas a process model represents the learners in term of both the knowledge they learned in the domain, and the inference procedures they used for completing a process (task). State models and process models are both competence-based, and they do not provide the information of an individual's cognitive abilities required by the formalisation of exploration space control. A new approach of student modelling is required, and this approach is called cognitive trait model (CTM). The basis of CTM lies in the field of cognitive science. The process for the creation of CTM includes the following subtasks. The cognitive trait under inquiry is studied in order to find its indicative signs (e.g. sign A indicates high working memory capacity). The signs are called the manifests of the cognitive trait. Manifests are always in pairs, i.e. if manifest A indicates high working memory capacity, A's inverse, B, would indicates low working memory capacity. The manifests are then translated into implementation patterns which are observable patterns in the records of learner-system interaction. Implementation patterns are regarded as machine-recognisable manifests. The manifests are used to create nodes in a neural network like structure called individualised temperament network (ITN). Every node in the ITN has its weight that conditions and is conditioned by the overall result of the execution of ITN. The output of the ITN's execution is used to update the CTM. A formative evaluation was carried out for a prototype created in this work. The positive results of the evaluation show the educational potential of the CTM approach. The current CTM only cater for the working memory capacity, in the future research more cognitive traits will be studied and included into the CTM

Learning Robust Object Recognition Using Composed Scenes from Generative Models

Recurrent feedback connections in the mammalian visual system have been hypothesized to play a role in synthesizing input in the theoretical framework of analysis by synthesis. The comparison of internally synthesized representation with that of the input provides a validation mechanism during perceptual inference and learning. Inspired by these ideas, we proposed that the synthesis machinery can compose new, unobserved images by imagination to train the network itself so as to increase the robustness of the system in novel scenarios. As a proof of concept, we investigated whether images composed by imagination could help an object recognition system to deal with occlusion, which is challenging for the current state-of-the-art deep convolutional neural networks. We fine-tuned a network on images containing objects in various occlusion scenarios, that are imagined or self-generated through a deep generator network. Trained on imagined occluded scenarios under the object persistence constraint, our network discovered more subtle and localized image features that were neglected by the original network for object classification, obtaining better separability of different object classes in the feature space. This leads to significant improvement of object recognition under occlusion for our network relative to the original network trained only on un-occluded images. In addition to providing practical benefits in object recognition under occlusion, this work demonstrates the use of self-generated composition of visual scenes through the synthesis loop, combined with the object persistence constraint, can provide opportunities for neural networks to discover new relevant patterns in the data, and become more flexible in dealing with novel situations.Comment: Accepted by 14th Conference on Computer and Robot Visio

MEMS flow sensors for nano-fluidic applications

This paper presents micromachined thermal sensors for measuring liquid flow rates in the nanoliter-per-minute range. The sensors use a boron-doped polysilicon thinfilm heater that is embedded in the silicon nitride wall of a microchannel. The boron doping is chosen to increase the heater’s temperature coefficient of resistance within tolerable noise limits, and the microchannel is suspended from the substrate to improve thermal isolation. The sensors have demonstrated a flow rate resolution below 10 nL/min, as well as the capability for detecting micro bubbles in the liquid. Heat transfer simulation has also been performed to explain the sensor operation and yielded good agreement with experimental data

Cracking pressure control of parylene checkvalve using slanted tensile tethers

MEMS check valves with fixed cracking pressures are important in micro-fluidic applications where the pressure, flow directions and flow rates all need to be carefully controlled. This work presents a new surface-micromachined parylene check valve that uses residual thermal stress in the parylene to control its cracking pressure. The new check valve uses slanted tethers to allow the parylene tensile stress to apply a net downward force on the valving seat against the orifice. The angle of the slanted tethers is made using a gray-scale mask to create a sloped sacrificial photoresist with the following tether parylene deposition. The resulted check valves have both the cracking pressures and flow profiles agreeable well with our theoretical analysis

Dynamic simulation of a peristaltic micropump considering coupled fluid flow and structural motion

This paper presents lumped-parameter simulation of dynamic characteristics of peristaltic micropumps. The pump consists of three pumping cells connected in series, each of which is equipped with a compliant diaphragm that is electrostatically actuated in a peristaltic sequence to mobilize the fluid. Diaphragm motion in each pumping cell is first represented by an effective spring subjected to hydrodynamic and electrostatic forces. These cell representations are then used to construct a system-level model for the entire pump, which accounts for both cell- and pump-level interactions of fluid flow and diaphragm vibration. As the model is based on first principles, it can be evaluated directly from the device's geometry, material properties and operating parameters without using any experimentally identified parameters. Applied to an existing pump, the model correctly predicts trends observed in experiments. The model is then used to perform a systematic analysis of the impact of geometry, materials and pump loading on device performance, demonstrating its utility as an efficient tool for peristaltic micropump design