The legacy of Ian Hunter's work on literature education and the history of reading practices : some preliminary remarks

Ian Hunter's early work on the history of literature education and the emergence of English as school subject issued a bold challenge to traditional accounts that have in the main focused on English either as knowledge of a particular field or as ideology. The alternative proposal put forward by Hunter and supported by detailed historical analysis is that English exists as a series of historically contingent techniques and practices for shaping the self-managing capacities of children. The challenge for the field is to advance this historical work and to examine possible implications for English teaching.

The value of books : the fantastic flying books of Mr. Morris Lessmore and the social hieroglyphic of reading

The late eighteenth century witnessed the emergence of new technologies of subjectivity and of the literary. Most obviously, “the novel as a literary form appeared to embody and turn into an object the experience of life itself” (Park), and the novel genre came to both reflect and shape notions of interiority and subjectivity. In this same period, “A shift was taking place in the way people felt and thought about children and the accoutrements of childhood, including books and toys, were implicated in this change” (Lewis). In seeking to understand the relationships between media (e.g. books and toys), genres (e.g. novels and picture books), and modes of subjectivity, Marx’s influential theory of commodity fetishism, whereby “a definite social relation between men, that assumes, in their eyes, the fantastic form of a relation between things”, has served as a productive tool of analysis. The extent to which Marx’s account of commodity fetishism continues to be of use becomes clear when the corollaries between the late eighteenth-century emergence of novels and pictures books as technologies of subjectivity and the early twenty-first century emergence of e-readers and digital texts as technologies of subjectivity are considered. This paper considers the literary technology of Apple’s iPad (first launched in 2010) as a commodity fetish, and the circulation of “apps” as texts made available by and offered as justifications for, this fetish object. The iPad is both book and toy, but is never “only” either; it is arguably a new technology of subjectivity which incorporates but also destabilises categories of reading and playing such as those made familiar by earlier technologies of literature and the self. The particular focus of this paper is on the multimodal versions (app, film, and picture book) of The Fantastic Flying Books of Mr. Morris Lessmore, which are understood here as a narrativisation of commodity fetishism, subjectivity, and the act of reading itself.

Adaptive unsupervised learning of human actions

Automatic detection of suspicious activities in CCTV camera feeds is crucial to the success of video surveillance systems. Such a capability can help transform the dumb CCTV cameras into smart surveillance tools for fighting crime and terror. Learning and classification of basic human actions is a precursor to detecting suspicious activities. Most of the current approaches rely on a non-realistic assumption that a complete dataset of normal human actions is available. This paper presents a different approach to deal with the problem of understanding human actions in video when no prior information is available. This is achieved by working with an incomplete dataset of basic actions which are continuously updated. Initially, all video segments are represented by Bags-Of-Words (BOW) method using only Term Frequency-Inverse Document Frequency (TF-IDF) features. Then, a data-stream clustering algorithm is applied for updating the system's knowledge from the incoming video feeds. Finally, all the actions are classified into different sets. Experiments and comparisons are conducted on the well known Weizmann and KTH datasets to show the efficacy of the proposed approach.

Increasing stochastic perturbations enhances acquisition and learning of complex sport movements

Traditionally, the aquisition of skills and sport movement has been characterised by numerous repetitions of presumed model movement pattern to be acquired by learners. This approach has been questioned by research identifying the presence of individualised movement patterns and the low probability of occurrence of two identical movements within and between individuals. In contrast, the differential learning approach claims advantage for incurring variability in the learning process by adding stochastic perturbations during practice. These ideas are exemplified by data from a high jump experiment which compared the effectiveness of classical and a differential training approach with pre-post test design. Results showed clear advantages for the group with additional stochastic perturbation during the aquisition phase in comparison to classically trained athletes. Analogies to similar phenomenological effects in the neurobiological literature are discussed.

Action learning of online marketing via a global contest

Introduction: The Google Online Marketing Challenge is a global competition in which student teams run advertising campaigns for small and medium-sized businesses (SMEs) using AdWords, Google’s text-based advertisements. In 2008, its inaugural year, over 8,000 students and 300 instructors from 47 countries representing over 200 schools participated. The Challenge ran in undergraduate and graduate classes in disciplines such as marketing, tourism, advertising, communication and information systems. Combining advertising and education, the Challenge gives student hands-on experience in the increasingly important field of online marketing, engages them with local businesses and motivates them through the thrill of a global competition. Student teams receive US$200 in AdWords credits, Google’s premier advertising product that offers cost-per-click advertisements. The teams then recruit and work with a local business to devise an effective online marketing campaign. Students first outline a strategy, run a series of campaigns, and provide their business with recommendations to improve their online marketing. Teams submit two written reports for judging by 14 academics in eight countries. In addition, Google AdWords experts judge teams on their campaign statistics such as success metrics and account management. Rather than a marketing simulation against a computer or hypothetical marketing plans for hypothetical businesses, the Challenges has student teams develop and manage real online advertising campaigns for their clients and compete against peers globally.

Representation and learning of visual information for pose recognition

Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.

Use of advanced computer graphics and data visualisation technologies in teaching and learning of steel structural design

A teaching and learning development project is currently under way at Queens-land University of Technology to develop advanced technology videotapes for use with the delivery of structural engineering courses. These tapes consist of integrated computer and laboratory simulations of important concepts, and behaviour of structures and their components for a number of structural engineering subjects. They will be used as part of the regular lectures and thus will not only improve the quality of lectures and learning environment, but also will be able to replace the ever-dwindling laboratory teaching in these subjects. The use of these videotapes, developed using advanced computer graphics, data visualization and video technologies, will enrich the learning process of the current diverse engineering student body. This paper presents the details of this new method, the methodology used, the results and evaluation in relation to one of the structural engineering subjects, steel structures.

Contextualising the teaching and learning of measurement within Torres Strait Islander schools

A one year mathematics project that focused on measurement was conducted with six Torres Strait Islander schools and communities. Its key focus was to contextualise the teaching and learning of measurement within the students’ culture, communities and home languages. There were six teachers and two teacher aides who participated in the project. This paper reports on the findings from the teachers’ and teacher aides’ survey questionnaire used in the first Professional Development session to identify: a) teachers’ experience of teaching in Torres Strait Islands, b) teachers’ beliefs about effective ways to teach Torres Strait Islander students, and c) contexualising measurement within Torres Strait Islander culture, Communities and home languages. A wide range of differing levels of knowledge and understanding about how to contextualise measurement to support student learning were identified and analysed. For example, an Indigenous teacher claimed that mathematics and the environment are relational, that is, they are not discrete and in isolation from one another, rather they interconnect with mathematical ideas emerging from the environment of the Torres Strait Communities.

Applying ISOMAP to the learning of hyperspectral image

In this paper, we present the application of a non-linear dimensionality reduction technique for the learning and probabilistic classification of hyperspectral image. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. It gives much greater information content per pixel on the image than a normal colour image. This should greatly help with the autonomous identification of natural and manmade objects in unfamiliar terrains for robotic vehicles. However, the large information content of such data makes interpretation of hyperspectral images time-consuming and userintensive. We propose the use of Isomap, a non-linear manifold learning technique combined with Expectation Maximisation in graphical probabilistic models for learning and classification. Isomap is used to find the underlying manifold of the training data. This low dimensional representation of the hyperspectral data facilitates the learning of a Gaussian Mixture Model representation, whose joint probability distributions can be calculated offline. The learnt model is then applied to the hyperspectral image at runtime and data classification can be performed.

Applying incremental EM to Bayesian classifiers in the learning of hyperspectral remote sensing data

In this paper, we apply the incremental EM method to Bayesian Network Classifiers to learn and interpret hyperspectral sensor data in robotic planetary missions. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. Many spacecraft carry spectroscopic equipment as wavelengths outside the visible light in the electromagnetic spectrum give much greater information about an object. The algorithm used is an extension to the standard Expectation Maximisation (EM). The incremental method allows us to learn and interpret the data as they become available. Two Bayesian network classifiers were tested: the Naive Bayes, and the Tree-Augmented-Naive Bayes structures. Our preliminary experiments show that incremental learning with unlabelled data can improve the accuracy of the classifier.

Constituting the teacher of reading in contemporary Australian literacy debates

This chapter documents the history of the National Inquiry into the Teaching of Literacy and the subsequent fate of the figure of the teacher, in terms of how the inquiry has acted to background the teacher and bring new figures into prominence. The classroom teacher is being moved out of a central role of authority in literacy education, in spite of claims about the importance of the teacher in parts of the report. Authority is now being placed in the figure of the scientific researcher who decides what the best techniques are, and develops diagnostic tools that the teacher must use in order to decide which of the techniques to apply. Specialist literacy teachers, well “trained”by these experts, are needed to ensure that teachers do what the experts recommend (evidence-based practice). Thus, the classroom literacy teacher becomes a cipher for applying expertly designed techniques and tests.