Completing the jigsaw : ESL and EFL undergraduate views on interactive peer-based learning

English as a Second Language (ESL) and English as a Foreign Language (EFL) students often face incongruence with Western teaching methods and learning expectations. The aim of this paper is to explore the potential for interactive peer-based learning to engage ESL and EFL language learners provide authentic communication experiences and accelerate learning through two case studies in different contexts. A study was undertaken to investigate student ‘voice’ (Rudduck, 1999, 2005; Rudduck & Flutter, 2004) during an intervention of communicative language teaching using peer-based learning strategies. This article describes unique similarities and subtle differences between ESL and EFL undergraduate learning in two different cultural contexts, using a 'stages of learning matrix' teaching tool to encourage civic skills and self-efficacy. It also suggests ways for teachers to improve on inconsistencies in group-based learning in order to promote more inclusive and congruent learning experiences for English language learners.

Case study of collaborative learning in two contexts : what do English language learners gain?

This chapter describes the use of collaborative learning as an approach to enhance English language learning by students from non-English speaking backgrounds. Communicative Language Teaching (CLT) principles were applied to two case studies, one comprising of undergraduate English as Foreign Language Learners in Turkey and the other involved English as Second Language learners in Australia. Social constructivism inspired communicative language teaching using collaborative learning activities such as team work, interactive peer-based learning, and iterative stages of learning matrix were incorporated to enhance students' learning outcomes. Data collected after the CLT intervention was made up of field notes, reflective logs and focus group interviews which revealed complementarities, as well as subtle differences between the two cases. The findings were summarized as learning dispositions; speaking fluency and confidence; learning diagnostics and completion deficiencies; task engagement, flow theory and higher order thinking skills; in addition to self efficacy and development of student identity. CLT has the potential to provide a more inclusive and dynamic education for diverse learners through vital outcomes and benefits which resonate with the real world.

Transforming assessment practices

This research project produced a number of resources to assist faculties in reviewing their assessment practices. These resources include an Evidence of Learning Matrix, Assessment Review Flowchart and Action Plan. In completing a review of assessment practices, this would then inform a curriculum review or retreat.

Enrique Pichon-Rivière : Los orígenes de la psicología social argentina

Enrique Pichon-Rivière (1907-1977) fue uno de los fundadores de la psicología social en Argentina. En este trabajo revisaremos su biografía y trayectoria institucional junto con sus aportes teóricos y epistemológicos. Sostenemos que Enrique Pichon-Rivière dio muestra de una actitud crítica con respecto a las divisiones disciplinares y a la distancia entre conocimiento académico y praxis social. Nuestro recorte privilegiará aquellos aspectos de su obra que consideramos de mayor relevancia para las ciencias sociales, incluyendo sus nociones de necesidad social, matrices de aprendizaje, esquema conceptual referencial operativo (E.C.R.O.) y epistemología convergente

Enrique Pichon-Rivière : Los orígenes de la psicología social argentina

Enrique Pichon-Rivière (1907-1977) fue uno de los fundadores de la psicología social en Argentina. En este trabajo revisaremos su biografía y trayectoria institucional junto con sus aportes teóricos y epistemológicos. Sostenemos que Enrique Pichon-Rivière dio muestra de una actitud crítica con respecto a las divisiones disciplinares y a la distancia entre conocimiento académico y praxis social. Nuestro recorte privilegiará aquellos aspectos de su obra que consideramos de mayor relevancia para las ciencias sociales, incluyendo sus nociones de necesidad social, matrices de aprendizaje, esquema conceptual referencial operativo (E.C.R.O.) y epistemología convergente

Enrique Pichon-Rivière : Los orígenes de la psicología social argentina

Enrique Pichon-Rivière (1907-1977) fue uno de los fundadores de la psicología social en Argentina. En este trabajo revisaremos su biografía y trayectoria institucional junto con sus aportes teóricos y epistemológicos. Sostenemos que Enrique Pichon-Rivière dio muestra de una actitud crítica con respecto a las divisiones disciplinares y a la distancia entre conocimiento académico y praxis social. Nuestro recorte privilegiará aquellos aspectos de su obra que consideramos de mayor relevancia para las ciencias sociales, incluyendo sus nociones de necesidad social, matrices de aprendizaje, esquema conceptual referencial operativo (E.C.R.O.) y epistemología convergente

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

Matrix regularization techniques for online multitask learning

In this paper we examine the problem of prediction with expert advice in a setup where the learner is presented with a sequence of examples coming from different tasks. In order for the learner to be able to benefit from performing multiple tasks simultaneously, we make assumptions of task relatedness by constraining the comparator to use a lesser number of best experts than the number of tasks. We show how this corresponds naturally to learning under spectral or structural matrix constraints, and propose regularization techniques to enforce the constraints. The regularization techniques proposed here are interesting in their own right and multitask learning is just one application for the ideas. A theoretical analysis of one such regularizer is performed, and a regret bound that shows benefits of this setup is reported.

Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space -- classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -- using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

A matrix for appropriate assessment of workplace learning in stem disciplines

With increasing interest shown by Universities in workplace learning, especially in STEM disciplines, an issue has arisen amongst educators and industry partners regarding authentic assessment tasks for work integrated learning (WIL) subjects. This paper describes the use of a matrix, which is also available as a decision-tree, based on the features of the WIL experience, in order to facilitate the selection of appropriate assessment strategies. The matrix divides the WIL experiences into seven categories, based on such factors as: the extent to which the experience is compulsory, required for membership of a professional body or elective; whether the student is undertaking a project, or embedding in a professional culture; and other key aspects of the WIL experience. One important variable is linked to the fundamental purpose of the assessment. This question revolves around the focus of the assessment: whether on the person (student development); the process (professional conduct/language); or the product (project, assignment, literature review, report, software). The matrix has been trialed at QUT in the Faculty of Science and Technology, and also at the University of Surrey, UK, and has proven to have good applicability in both universities.

A new Jacobian matrix for optimal learning of single-layer neural networks

This paper investigates the learning of a wide class of single-hidden-layer feedforward neural networks (SLFNs) with two sets of adjustable parameters, i.e., the nonlinear parameters in the hidden nodes and the linear output weights. The main objective is to both speed up the convergence of second-order learning algorithms such as Levenberg-Marquardt (LM), as well as to improve the network performance. This is achieved here by reducing the dimension of the solution space and by introducing a new Jacobian matrix. Unlike conventional supervised learning methods which optimize these two sets of parameters simultaneously, the linear output weights are first converted into dependent parameters, thereby removing the need for their explicit computation. Consequently, the neural network (NN) learning is performed over a solution space of reduced dimension. A new Jacobian matrix is then proposed for use with the popular second-order learning methods in order to achieve a more accurate approximation of the cost function. The efficacy of the proposed method is shown through an analysis of the computational complexity and by presenting simulation results from four different examples.