Hilary Tunley

Using Mobile Technology to Create Flexible Learning Contexts

This paper discusses the importance of learning context with a particular focus upon the educational application of mobile technologies. We suggest that one way to understand a learning context is to perceive it as a Learner Centric Ecology of Resources. These resources can be deployed variously but with a concern to promote and support different kinds of mediations, including those of the teacher and learner. Our approach is informed by sociocultural theory and is used to construct a framework for the evaluation of learning experiences that encompass various combinations of technologies, people, spaces and knowledge. The usefulness of the framework is tested through two case studies that evaluate a range of learning contexts in which mobile technologies are used to support learning. We identify the benefits and challenges that arise when introducing technology across multiple locations. An analytical technique mapped from the Ecology of Resources framework is presented and used to identify the ways in which different technologies can require learners to adopt particular roles and means of communication. We illustrate how we involve participants in the analysis of their context and highlight the extent to which apparently similar contexts vary in ways that are significant for learners. The use of the Ecology of Resources framework to evaluate a range of learning contexts has demonstrated that technology can be used to provide continuity across locations: the appropriate contextualization of activities across school and home contexts, for example. It has also provided evidence to support the use of technology to identify ways in which resources can be adapted to meet the needs of a learner.

First order optic flow from log-polar sampled images

The first-order spatial derivatives of optic flow — dilation, shear and rotation — provide powerful information about motion and surface layout. The log-polar sampled image (LSI) is of increasing interest for active vision, and is particularly well-suited to the measurement of local first-order flow. We explain why this is, propose a simple least-squares method for measuring first-order flow in an LSI sequence, and demonstrate that the method works well when applied to real images.

Dynamic fixation of a moving surface using log polar sampling

We describe the development and testing of a first-order motion estimation algorithm which maintains accurate fixation of features on surfaces undergoing three-dimensional motion, and determines the local affine motion parallax. The accuracy of the first-order flow estimation is much improved by the use of log-polar sampling. We investigate the contribution of fixation to this accuracy using synthetic flow, and demonstrate the performance on affine tracking in real image sequences.

Coherence Compilation: Applying AIED Techniques to the Reuse of Educational TV Resources

The HomeWork project is building an exemplar system to provide individualised experiences for individual and groups of children aged 6-7 years, their parents, teachers and classmates at school. It employs an existing set of broadcast video media and associated resources that tackle both numeracy and literacy at Key Stage 1. The system employs a learner model and a pedagogical model to identify what resource is best used with an individual child or group of children collaboratively at a particular learning point and at a particular location. The Coherence Compiler is that component of the system which is designed to impose an overall narrative coherence on the materials that any particular child is exposed to. This paper presents a high level vision of the design of the Coherence Compiler and sets its design within the overall framework of the HomeWork project and its learner and pedagogical models.

Temporal filtering enhances direction discrimination in random-dot patterns

In conventional presentations of random-dot kinematograms, two frames of random dots are presented in temporal sequence, separated by a blank inter-stimulus interval, and a coherent offset in spatial position is added to dots in one frame relative to dots in the other frame. Direction discrimination performance is limited temporally to inter-stimulus intervals below about 100 msec (Tmax). Experiments are described in which temporal smoothing was applied to the onset and offset of each frame in the kinematogram. Tmax was found to increase in proportion with the time constant of the temporal smoothing function. An explanation based on contrast-dependent responses in simple motion detectors cannot accommodate the results. Instead, the increase in Tmax with temporal smoothing, and analogous increase in spatial limit (Dmax) with spatial blurring, can be related to the spatiotemporal frequency content of the stimulus. Random-dot kinematograms can be viewed as continuously drifting patterns that have been discretely sampled at regular spatiotemporal intervals. Sampling introduces artefacts (alias signals), which become more intrusive as sampling rate declines (i.e. inter-stimulus interval or spatial displacement increases) and consequently limit discrimination performance. Temporal smoothing or spatial blurring extends performance because it removes alias signals generated by high spatiotemporal frequencies in the pattern. Computational modelling to estimate the Fourier energy available in random-dot kinematograms confirmed that the sampling account can predict the proportional increase in Tmax and Dmax limits as filter time or space constant increases.

Motion detection in interleaved random dot patterns: Evidence for a rectifying nonlinearity preceding motion analysis

Three experiments examined direction discrimination in temporally interleaved random dot patterns. The stimulus consisted of two or more uncorrelated random patterns presented in a repeating temporal sequence, so that each pattern appeared only once every n frames, separated by uncorrelated patterns. Each pattern shifted either leftward or rightward at each re-appearance (all patterns shifted in the same direction in any one presentation). Subjects could specify shift direction correctly even when eight different patterns were interleaved, provided that the duration of each frame was brief. An explanation based on responses in first-order motion energy detectors tuned to low spatiotemporal frequencies (effectively summating the interleaved patterns over time) was tested using a stimulus in which each pattern inverted in contrast mid-way through each frame. Contrary to predictions based on temporal summation, performance with contrast-inverting patterns was only slightly lower than with non-inverting patterns. An alternative explanation was examined, based on responses in motion detectors that full-wave rectify image contrast before extracting motion energy. Computed responses from such detectors successfully predicted psychophysical performance with interleaved random patterns. Implications for models of motion analysis are discussed.

Up and down the number-line: modelling collaboration in contrasting school and home environments

This paper is concerned with user modelling issues such as adaptive educational environments, adaptive information retrieval, and support for collaboration. The HomeWork project is examining the use of learner modelling strategies within both school and home environments for young children aged 5 – 7 years. The learning experience within the home context can vary considerably from school especially for very young learners, and this project focuses on the use of modelling which can take into account the informality and potentially contrasting learning styles experienced within the home and school.

Distributed Dynamic Processing for Edge Detection

This paper discusses a dynamic method of edge detection which works from a sequence of frames. Most edge detection algorithms process image information statically, regardless of whether the application is static — i.e. whether the input is a singular, unique image — or a dynamic sequence of frames used for tracking or optic flow extraction. As many applications are dynamic, such as robotics, autonomous vehicle control and satellite tracking, it makes sense for edge detection processes to exploit dynamic phenomena.

Iris localisation for a head-mounted eye tracker

This paper discusses the image processing techniques needed to localise the iris outline in images of the eye produced by a novel, portable eye tracker. The tracker allows gaze direction to be measured by supplying simultaneous views of a subjects eye and of the world from a headmounted camera. Finding gaze direction relative to the head requires accurate and robust measurement of the iris outline under a wide range of lighting conditions, in the presence of highlights, and when the iris is in an extreme position. We describe a reliable method to solve this problem, which achieves a gaze direction accuracy of under 2 degrees.