The importance of power dynamics in the development of asynchronous online learning communities

This research explored how a more student-directed learning design can support the creation of togetherness and belonging in a community of distance learners in formal higher education. Postgraduate students in a New Zealand School of Education experienced two different learning tasks as part of their online distance learning studies. The tasks centered around two online asynchronous discussions each for the same period of time and with the same group of students, but following two different learning design principles. All messages were analyzed using a twostep analysis process, content analysis and social network analysis. Although the findings showed a balance of power between the tutor and the students in the first high e-moderated activity, a better pattern of group interaction and community feeling was found in the low e-moderated activity. The paper will discuss the findings in terms of the implications for learning design and the role of the tutor.

Lexical and nonlexical processing in developmental dyslexia: a case for different resources and different impairments

In a group of adult dyslexics word reading and, especially, word spelling are predicted more by what we have called lexical learning (tapped by a paired-associate task with pictures and written nonwords) than by phonological skills. Nonword reading and spelling, instead, are not associated with this task but they are predicted by phonological tasks. Consistently, surface and phonological dyslexics show opposite profiles on lexical learning and phonological tasks. The phonological dyslexics are more impaired on the phonological tasks, while the surface dyslexics are equally or more impaired on the lexical learning tasks. Finally, orthographic lexical learning explains more variation in spelling than in reading, and subtyping based on spelling returns more interpretable results than that based on reading. These results suggest that the quality of lexical representations is crucial to adult literacy skills. This is best measured by spelling and best predicted by a task of lexical learning. We hypothesize that lexical learning taps a uniquely human capacity to form new representations by recombining the units of a restricted set.

Color categories: confirmation of the relativity hypothesis

The question of whether language affects our categorization of perceptual continua is of particular interest for the domain of color where constraints on categorization have been proposed both within the visual system and in the visual environment. Recent research (Roberson, Davies, & Davidoff, 2000; Roberson et al., in press) found substantial evidence of cognitive color differences between different language communities, but concerns remained as to how representative might be a tiny, extremely remote community. The present study replicates and extends previous findings using additional paradigms among a larger community in a different visual environment. Adult semi-nomadic tribesmen in Southern Africa carried out similarity judgments, short-term memory and long-term learning tasks. They showed different cognitive organization of color to both English and another language with the five color terms. Moreover, Categorical Perception effects were found to differ even between languages with broadly similar color categories. The results provide further evidence of the tight relationship between language and cognition.

Teaching speaking:a holistic approach

Teaching Speaking A Holistic Approach brings together theoretical and pedagogical perspectives on teaching speaking within a coherent methodological framework. The framework combines understandings derived from several areas of speaking research and instruction including cognitive and affective processes, oracy for thinking and learning communicative competence, discourse theories, task-based language learning, and self-regulated learning. By explaining, interpreting, evaluating, and synthesizing these diverse perspectives from linguistics and language learning, the text offers a comprehensive and versatile approach for teaching speaking. Samples of authentic classroom data are used for illustrating important concepts to help readers see how theoretical perspectives can be applied in practice. It also includes a pedagogical model for sequencing learning activities with concrete guidelines on planning and conducting speaking lessons. Different types of learning tasks are explained and illustrated with examples, and each chapter includes short tasks and ends with a number of tasks that enable readers to extend their ideas.

Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.

Semi-supervised Gaussian process latent variable model with pairwise constraints

In machine learning, Gaussian process latent variable model (GP-LVM) has been extensively applied in the field of unsupervised dimensionality reduction. When some supervised information, e.g., pairwise constraints or labels of the data, is available, the traditional GP-LVM cannot directly utilize such supervised information to improve the performance of dimensionality reduction. In this case, it is necessary to modify the traditional GP-LVM to make it capable of handing the supervised or semi-supervised learning tasks. For this purpose, we propose a new semi-supervised GP-LVM framework under the pairwise constraints. Through transferring the pairwise constraints in the observed space to the latent space, the constrained priori information on the latent variables can be obtained. Under this constrained priori, the latent variables are optimized by the maximum a posteriori (MAP) algorithm. The effectiveness of the proposed algorithm is demonstrated with experiments on a variety of data sets. © 2010 Elsevier B.V.

On-line learning of unrealizable tasks

The dynamics of on-line learning is investigated for structurally unrealizable tasks in the context of two-layer neural networks with an arbitrary number of hidden neurons. Within a statistical mechanics framework, a closed set of differential equations describing the learning dynamics can be derived, for the general case of unrealizable isotropic tasks. In the asymptotic regime one can solve the dynamics analytically in the limit of large number of hidden neurons, providing an analytical expression for the residual generalization error, the optimal and critical asymptotic training parameters, and the corresponding prefactor of the generalization error decay.

Policy into practice:a study of public policy implementation and the role of learning

Public policy becomes managerial practice through a process of implementation. There is an established literature within Implementation Studies which explains the variables and some of the processes involved in implementation, but less attention has been focused upon how public service managers convert new policy initiatives into practice. The research proposes that managers and their organisations have to go through a process of learning in order to achieve the implementation of public policy. Data was collected over a five year period from four case studies of capital investment appraisal in the British National Health Service. Further data was collected from taped interviews by key actors within the case studies. The findings suggest that managers do learn to implement policy and four factors are important in this learning process. These are; (i) the nature of bureaucratic responsibility; (ii) the motivation of actors towards learning; (iii) the passage of time which allows for the development of competence and (iv) the use of project team structures. The research has demonstrated that the conversion of policy into practice occurs through the operationalisation of solutions to policy problems via job tasks. As such it suggests that in understanding how policy is implemented, technical learning is more important than cultural learning, in this context. In conclusion, a "Model of Learned Implementation" is presented, together with a discussion of some of the implications of the research. These are the possible use of more pilot projects for new policy initiatives and the more systematic diffusion of knowledge about implementation solutions.

An action research study with low proficiency learners in Japan to promote the learning of vocabulary through collocations.

This action research (AR) study explores an alternative approach to vocabulary instruction for low-proficiency university students: a change from targeting individual words from the general service list (West, 1953) to targeting frequent verb + noun collocations. A review of the literature indicated a focus on collocations instead of individual words could potentially address the students’ productive challenges with targeted vocabulary. Over the course of four reflective cycles, this thesis addresses three main aspects of collocation instruction. First, it examines if the students believe studying collocations is more useful than studying individual lexical items. Second, the thesis investigates whether a focus on collocations will lead to improvements in spoken fluency. This is tested through a comparison of a pre-intervention spoken assessment task with the findings from the same task completed 15 weeks later, after the intervention. Third, the thesis explores different procedures for the instructing of collocations under the classroom constraints of a university teaching context. In the first of the four reflective cycles, data is collected which indicates that the students believe a focus on collocations is superior to only teaching individual lexical items, that in the students’ opinion their productive abilities with the targeted structures has improved, and that delexicalized verb collocations are problematic for low-proficiency students. Reflective cycle two produces evidence indicating that productive tasks are superior to receptive tasks for fluency development. In reflective cycle three, productively challenging classroom tasks are investigated further and the findings indicate that tasks with higher productive demands result in greater improvements in spoken fluency. The fourth reflective cycle uses a different type of collocation list: frequent adjective + noun collocations. Despite this change, the findings remain consistent in that certain types of collocations are problematic for low-proficiency language learners and that the evidence shows productive tasks are necessary to improve the students’ spoken ability.

Mirror-image relations in category learning

The discrimination of patterns that are mirror-symmetric counterparts of each other is difficult and requires substantial training. We explored whether mirror-image discrimination during expertise acquisition is based on associative learning strategies or involves a representational shift towards configural pattern descriptions that permit resolution of symmetry relations. Subjects were trained to discriminate between sets of unfamiliar grey-level patterns in two conditions, which either required the separation of mirror images or not. Both groups were subsequently tested in a 4-class category-learning task employing the same set of stimuli. The results show that subjects who had successfully learned to discriminate between mirror-symmetric counterparts were distinctly faster in the categorization task, indicating a transfer of conceptual knowledge between the two tasks. Additional computer simulations suggest that the development of such symmetry concepts involves the construction of configural, protoholistic descriptions, in which positions of pattern parts are encoded relative to a spatial frame of reference.

An investigation of the factor-analytic approach to the determination of abilities involved in psychomotor learning

This research began with an attempt to solve a practical problem, namely, the prediction of the rate at which an operator will learn a task. From a review of the literature, communications with researchers in this area and the study of psychomotor learning in factories it was concluded that a more fundamental approach was required which included the development of a task taxonomy. This latter objective had been researched for over twenty years by E. A. Fleishman and his approach was adopted. Three studies were carried out to develop and extend Fleishman's approach to the industrial area. However, the results of these studies were not in accord with FIeishman's conclusions and suggested that a critical re-assessment was required of the arguments, methods and procedures used by Fleishman and his co-workers. It was concluded that Fleishman's findings were to some extent an artifact of the approximate methods and procedures which he used in the original factor analyses and that using the more modern computerised factor analytic methods a reliable ability taxonomy could be developed to describe the abilities involved in the learning of psychomotor tasks. The implications for a changing-task or changing-subject model were drawn and it was concluded that a changing task and subject model needs to be developed.

Hidden in plain sight:low-literacy adults in a developed country overcoming social and educational challenges through mobile learning support tools

Illiteracy is often associated with people in developing countries. However, an estimated 50 % of adults in a developed country such as Canada lack the literacy skills required to cope with the challenges of today's society; for them, tasks such as reading, understanding, basic arithmetic, and using everyday items are a challenge. Many community-based organizations offer resources and support for these adults, yet overall functional literacy rates are not improving. This is due to a wide range of factors, such as poor retention of adult learners in literacy programs, obstacles in transferring the acquired skills from the classroom to the real life, personal attitudes toward learning, and the stigma of functional illiteracy. In our research we examined the opportunities afforded by personal mobile devices in providing learning and functional support to low-literacy adults. We present the findings of an exploratory study aimed at investigating the reception and adoption of a technological solution for adult learners. ALEX© is a mobile application designed for use both in the classroom and in daily life in order to help low-literacy adults become increasingly literate and independent. Such a solution complements literacy programs by increasing users' motivation and interest in learning, and raising their confidence levels both in their education pursuits and in facing the challenges of their daily lives. We also reflect on the challenges we faced in designing and conducting our research with two user groups (adults enrolled in literacy classes and in an essential skills program) and contrast the educational impact and attitudes toward such technology between these. Our conclusions present the lessons learned from our evaluations and the impact of the studies' specific challenges on the outcome and uptake of such mobile assistive technologies in providing practical support to low-literacy adults in conjunction with literacy and essential skills training. © 2013 Her Majesty the Queen in Right of Canada.

“Showing off” your mobile device:adult literacy learning in the classroom and beyond

For a very large number of adults, tasks such as reading. understanding, and using everyday items are a challenge. Although many community-based organizations offer resources and support for adults with limited literacy skills. current programs have difficulty reaching and retaining those that would benefit most. In this paper we present the findings of an exploratory study aimed at investigating how a technological solution that addresses these challenges is received and adopted by adult learners. For this, we have developed a mobile application to support literacy programs and to assist low-literacy adults in today's information-centric society. ALEX© (Adult Literacy support application for Experiential learning) is a mobile language assistant that is designed to be used both in the classroom and in daily life in order to help low-literacy adults become increasingly literate and independent. Through a long-term study with adult learners we show that such a solution complements literacy programs by increasing users' motivation and interest in learning, and raising their confidence levels both in their education pursuits and in facing the challenges of their daily lives.

Learning higher-level features with convolutional restricted Boltzmann machines for sentiment analysis

In recent years, learning word vector representations has attracted much interest in Natural Language Processing. Word representations or embeddings learned using unsupervised methods help addressing the problem of traditional bag-of-word approaches which fail to capture contextual semantics. In this paper we go beyond the vector representations at the word level and propose a novel framework that learns higher-level feature representations of n-grams, phrases and sentences using a deep neural network built from stacked Convolutional Restricted Boltzmann Machines (CRBMs). These representations have been shown to map syntactically and semantically related n-grams to closeby locations in the hidden feature space. We have experimented to additionally incorporate these higher-level features into supervised classifier training for two sentiment analysis tasks: subjectivity classification and sentiment classification. Our results have demonstrated the success of our proposed framework with 4% improvement in accuracy observed for subjectivity classification and improved the results achieved for sentiment classification over models trained without our higher level features.

The political discourse and material practice of technology enhanced learning

Technology discloses man’s mode of dealing with Nature, the process of production by which he sustains his life, and thereby also lays bare the mode of formation of his social relations, and of the mental conceptions that flow from them (Marx, 1990: 372) My thesis is a Sociological analysis of UK policy discourse for educational technology during the last 15 years. My framework is a dialogue between the Marxist-based critical social theory of Lieras and a corpus-based Critical Discourse Analysis (CDA) of UK policy for Technology Enhanced Learning (TEL) in higher education. Embedded in TEL is a presupposition: a deterministic assumption that technology has enhanced learning. This conceals a necessary debate that reminds us it is humans that design learning, not technology. By omitting people, TEL provides a vehicle for strong hierarchical or neoliberal, agendas to make simplified claims politically, in the name of technology. My research has two main aims: firstly, I share a replicable, mixed methodological approach for linguistic analysis of the political discourse of TEL. Quantitatively, I examine patterns in my corpus to question forms of ‘use’ around technology that structure a rigid basic argument which ‘enframes’ educational technology (Heidegger, 1977: 38). In a qualitative analysis of findings, I ask to what extent policy discourse evaluates technology in one way, to support a Knowledge Based Economy (KBE) in a political economy of neoliberalism (Jessop 2004, Fairclough 2006). If technology is commodified as an external enhancement, it is expected to provide an ‘exchange value’ for learners (Marx, 1867). I therefore examine more closely what is prioritised and devalued in these texts. Secondly, I disclose a form of austerity in the discourse where technology, as an abstract force, undertakes tasks usually ascribed to humans (Lieras, 1996, Brey, 2003:2). This risks desubjectivisation, loss of power and limits people’s relationships with technology and with each other. A view of technology in political discourse as complete without people closes possibilities for broader dialectical (Fairclough, 2001, 2007) and ‘convivial’ (Illich, 1973) understandings of the intimate, material practice of engaging with technology in education. In opening the ‘black box’ of TEL via CDA I reveal talking points that are otherwise concealed. This allows me as to be reflexive and self-critical through praxis, to confront my own assumptions about what the discourse conceals and what forms of resistance might be required. In so doing, I contribute to ongoing debates about networked learning, providing a context to explore educational technology as a technology, language and learning nexus.