Going beyond the official domain in the search for the culture of employee learning : The case of junior support staff at a South African university

Abstract: Based on HCT (human capital theory), employee learning and the culture associated with it in South Africa and globally have generally been researched from the perspective of the normative government or employer-initiated policies and programmes. Using Bernstein’s (2000) theory of the pedagogic device, this paper suggests the existence of different domains of learning with respect to junior support staff at a South African university. The paper also borrows from critical realism to advocate an approach which asks questions pertaining to the influence of structure and agency on the form of the culture of employee learning in different domains with respect to the junior support staff members. The answers to these questions, the paper suggests, would help with a holistic characterisation of the culture of employee learning associated with this category of employees at the South African university

Analytical learning and term-rewriting systems

Analytical learning is a set of machine learning techniques for revising the representation of a theory based on a small set of examples of that theory. When the representation of the theory is correct and complete but perhaps inefficient, an important objective of such analysis is to improve the computational efficiency of the representation. Several algorithms with this purpose have been suggested, most of which are closely tied to a first order logical language and are variants of goal regression, such as the familiar explanation based generalization (EBG) procedure. But because predicate calculus is a poor representation for some domains, these learning algorithms are extended to apply to other computational models. It is shown that the goal regression technique applies to a large family of programming languages, all based on a kind of term rewriting system. Included in this family are three language families of importance to artificial intelligence: logic programming, such as Prolog; lambda calculus, such as LISP; and combinatorial based languages, such as FP. A new analytical learning algorithm, AL-2, is exhibited that learns from success but is otherwise quite different from EBG. These results suggest that term rewriting systems are a good framework for analytical learning research in general, and that further research should be directed toward developing new techniques

Engaged in Learning: The ArtsSmarts Model

Approximately a dozen internal research studies into student learning and program effectiveness were conducted during ArtsSmarts' first eight years. In the spring of 2006, we compiled the results of those studies, along with a like number of reports by outside researchers, to create a synthesis of possible directions for future work. Although we used a small sample of available outside studies, it was immediately and glaringly evident that the arts and educational communities are hungering for research that will "help us understand what the arts learning experience is for children, and what characteristics of that experience are likely to travel across domains of learning" (Deasy, 2002:99). It was equally evident to all ArtsSmarts partners that, while future ArtsSmarts research could be taken in any number of directions, it made the most sense to identify and build from ArtsSmarts' own strengths and successes. We also felt the need to align the research direction and the methods of data collection with our intended audiences.Different groups would find different aspects of ArtsSmarts compelling, and distinctly different types of data would be required for each. Partners identified educators (teachers, administrators, and senior Board office personnel) as the audience they most wanted to reach.With that in mind, the decision was made to develop a theory of learning that would serve the dual purposes of explaining ArtsSmarts' impact in Canadian classrooms and framing the research work of the next few years. We felt that establishing an ArtsSmarts theory of learning would help to answer the question, "If ArtsSmarts didn't exist, what would be lost?" Further, a theory of learning would assist teachers, artists and partners in identifying key, essential components of the ArtsSmarts experience, and would also prevent ArtsSmarts from being viewed as a pleasant but unnecessary add-on to classroom activity. The paper that follows develops an ArtsSmarts theory of learning centred on the concept of student engagement

Domain-Adversarial Training of Neural Networks

We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains. The approach implements this idea in the context of neural network architectures that are trained on labeled data from the source domain and unlabeled data from the target domain (no labeled target-domain data is necessary). As the training progresses, the approach promotes the emergence of features that are (i) discriminative for the main learning task on the source domain and (ii) indiscriminate with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation and stochastic gradient descent, and can thus be implemented with little effort using any of the deep learning packages. We demonstrate the success of our approach for two distinct classification problems (document sentiment analysis and image classification), where state-of-the-art domain adaptation performance on standard benchmarks is achieved. We also validate the approach for descriptor learning task in the context of person re-identification application.Comment: Published in JMLR: http://jmlr.org/papers/v17/15-239.htm