The pedagogic prosthetic: Augmented learning as content-in-motion in hybrid educational spheres

This article draws on the design and implementation of three mobile learning projects introduced by Flanagan in 2011, 2012 and 2014 engaging a total of 206 participants. The latest of these projects is highlighted in this article. Two other projects provide additional examples of innovative strategies to engage mobile and cloud systems describing how electronic and mobile technology can help facilitate teaching and learning, assessment for learning and assessment as learning, and support communities of practice. The second section explains the theoretical premise supporting the implementation of technology and promulgates a hermeneutic phenomenological approach. The third section discusses mobility, both in terms of the exploration of wearable technology in the prototypes developed as a result of the projects, and the affordances of mobility within pedagogy. Finally the quantitative and qualitative methods in place to evaluate m-learning are explained.

Problem-based learning in an e-learning environment: A case study at Griffith University School of Medicine

Increasing numbers of medical schools in Australia and overseas have moved away from didactic teaching methodologies and embraced problem-based learning (PBL) to improve clinical reasoning skills and communication skills as well as to encourage self-directed lifelong learning. In January 2005, the first cohort of students entered the new MBBS program at the Griffith University School of Medicine, Gold Coast, to embark upon an exciting, fully integrated curriculum using PBL, combining electronic delivery, communication and evaluation systems incorporating cognitive principles that underpin the PBL process. This chapter examines the educational philosophies and design of the e-learning environment underpinning the processes developed to deliver, monitor and evaluate the curriculum. Key initiatives taken to promote student engagement and innovative and distinctive approaches to student learning at Griffith promoted within the conceptual model for the curriculum are (a) Student engagement, (b) Pastoral care, (c) Staff engagement, (d) Monitoring and (e) Curriculum/Program Review. © 2007 Springer-Verlag Berlin Heidelberg.

Learning experiences in a giant interactive environment: Insights from The Cube

In November 2012, Queensland University of Technology in Australia launched a giant interactive learning environment known as The Cube. This article reports a phenomenographic investigation into visitors’ different experiences of learning in The Cube. At present very little is known about people’s learning experience in spaces featuring large interactive screens. We observed many visitors to The Cube and interviewed 26 people. Our analysis identified critical variation across the visitors’ experience of learning in The Cube. The findings are discussed as the learning strategy (in terms of Absorption, Exploration, Isolation and Collaboration); and the content learned (in terms of Technology, Skills and Topics). Other findings presented here are dimensions of the learning strategy and the content learned, with differing perspectives on each dimension. These outcomes provide early insights into the potential of giant interactive environments to enhance learning approaches and guide the design of innovative learning spaces in higher education.

An Estimator Algorithm For Learning Automata With Changing Number Of Actions

In many problems of decision making under uncertainty the system has to acquire knowledge of its environment and learn the optimal decision through its experience. Such problems may also involve the system having to arrive at the globally optimal decision, when at each instant only a subset of the entire set of possible alternatives is available. These problems can be successfully modelled and analysed by learning automata. In this paper an estimator learning algorithm, which maintains estimates of the reward characteristics of the random environment, is presented for an automaton with changing number of actions. A learning automaton using the new scheme is shown to be e-optimal. The simulation results demonstrate the fast convergence properties of the new algorithm. The results of this study can be extended to the design of other types of estimator algorithms with good convergence properties.

Learning dynamic prices in multiseller electronic retail markets with price sensitive customers, stochastic demands, and inventory replenishments

In this paper, we use reinforcement learning (RL) as a tool to study price dynamics in an electronic retail market consisting of two competing sellers, and price sensitive and lead time sensitive customers. Sellers, offering identical products, compete on price to satisfy stochastically arriving demands (customers), and follow standard inventory control and replenishment policies to manage their inventories. In such a generalized setting, RL techniques have not previously been applied. We consider two representative cases: 1) no information case, were none of the sellers has any information about customer queue levels, inventory levels, or prices at the competitors; and 2) partial information case, where every seller has information about the customer queue levels and inventory levels of the competitors. Sellers employ automated pricing agents, or pricebots, which use RL-based pricing algorithms to reset the prices at random intervals based on factors such as number of back orders, inventory levels, and replenishment lead times, with the objective of maximizing discounted cumulative profit. In the no information case, we show that a seller who uses Q-learning outperforms a seller who uses derivative following (DF). In the partial information case, we model the problem as a Markovian game and use actor-critic based RL to learn dynamic prices. We believe our approach to solving these problems is a new and promising way of setting dynamic prices in multiseller environments with stochastic demands, price sensitive customers, and inventory replenishments.

Social dynamics for sustainable food systems : Actors' orientations towards sustainability in primary production and public consumption

The modern food system and sustainable development form a conceptual combination that suggests sustainability deficits in environmental impacts and nutritional status of western populations. This study explores actors orientations towards sustainability by probing into social dynamics for sustainability within primary production and public consumption. If actors within these two worlds were to express converging orientations for sustainability, the system dynamics of the market would enable more sustainable growth in terms of production dictated by consumption. The study is based on a constructivist research approach with qualitative text analyses. The findings were validated by internal and external food system actors and are suggested to represent current social dynamics within Finnish food system. The key findings included primary producers social skilfulness, which enabled networking with other actors in very different paths of life, learning in order to promote one s trade, and trusting reflectively in partners in order to expand business. These activities extended the supply chain in a spiral fashion by horizontal and vertical forward integration, until large retailers were met for negotiations on a more equal basis. This mode of chain level coordination, typically building around the core of social and partnership relations, was coined as a socially overlaid network, and seen as sustainable coordination mode for endogenous growth. The caterers exhibited more or less committed professional identity for sustainability within their reach. The facilitating approaches for professional identities dealt successfully with local and organic food in addition to domestic food, and also imported food. The co-operation with supply chains created innovative solutions and savings for the business parties to be shared. There were also more complicated identities as juggling, critical and delimited approaches for sustainability, with less productive efforts due to restrictions such as absence of organisational sustainability strategy, weak presence of local and organic suppliers, limited understanding about sustainability and no organisational resources for informed choices for sustainability. The convergence between producers and caterers existed to an extent allowing suggestion that increased clarity about sustainable consumption and production by actors could be constructed using advanced tools. The study looks for introduction of more profound environmental and socio-economic knowledge through participatory research with supply chain actors. Learning in the workplace about food system reality in terms of supply chain co-operation may prove to be a change engine that leads to advanced network operations and a more sustainable food system.

Social dynamics for sustainable food systems. Actors’ orientations towards sustainability in primary production and public consumption

Abstract The modern food system and sustainable development form a conceptual combination that suggests sustainability deficits in the ways we deal with food consumption and production - in terms of economic relations, environmental impacts and nutritional status of western population. This study explores actors’ orientations towards sustainability by taking into account actors’ embedded positions within structures of the food system, actors’ economic relations and views about sustainability as well as their possibilities for progressive activities. The study looks particularly at social dynamics for sustainability within primary production and public consumption. If actors within these two worlds were to express converging orientations for sustainability, the system dynamics of the market would enable more sustainable growth in terms of production dictated by consumption. The study is based on a constructivist research approach with qualitative text analyses. The data consisted of three text corpora, the ‘local food corpus’, the ‘catering corpus’ and the ‘mixed corpus’. The local food actors were interviewed about their economic exchange relations. The caterers’ interviews dealt with their professional identity for sustainability. Finally, the mixed corpus assembled a dialogue as a participatory research approach, which was applied in order to enable researcher and caterer learning about the use of organic milk in public catering. The data were analysed for theoretically conceptualised relations, expressing behavioural patterns in actors’ everyday work as interpreted by the researcher. The findings were corroborated by the internal and external communities of food system actors. The interpretations have some validity, although they only present abstractions of everyday life and its rich, even opaque, fabric of meanings and aims. The key findings included primary producers’ social skilfulness, which enabled networking with other actors in very different paths of life, learning in order to promote one’s trade, and trusting reflectively in partners in order to extend business. These activities expanded the supply chain in a spiral fashion by horizontal and vertical forward integration, until large retailers were met for negotiations on a more equal or ‘other regarding’ basis. This kind of chain level coordination, typically building around the core of social and partnership relations, was coined as a socially overlaid network. It supported market access of local farmers, rooted in their farms, who were able to draw on local capital and labour in promotion of competitive business; the growth was endogenous. These kinds of chains – one conventional and one organic – were different from the strategic chain, which was more profit based and while highly competitive, presented exogenous growth as it depended on imported capital and local employees. However, the strategic chain offered learning opportunities and support for the local economy. The caterers exhibited more or less committed professional identity for sustainability within their reach. The facilitating and balanced approaches for professional identities dealt successfully with local and organic food in addition to domestic food, and also imported food. The co-operation with supply chains created innovative solutions and savings for the business parties to be shared. The rule-abiding approach for sustainability only made choices among organic supply chains without extending into co-operation with actors. There were also more complicated and troubled identities as juggling, critical and delimited approaches for sustainability, with less productive efforts due to restrictions such as absence of organisational sustainability strategy, weak presence of local and organic suppliers, limited understanding about sustainability and no organisational resources to develop changes towards a sustainable food system. Learning in the workplace about food system reality in terms of supply chain co-operation may prove to be a change engine that leads to advanced network operations and a more sustainable food system. The convergence between primary producers and caterers existed to an extent allowing suggestion that increased clarity about sustainable consumption and production by actors could be approached using advanced tools. The study looks for introduction of more profound environmental and socio-economic knowledge through participatory research with supply chain actors in order to promote more sustainable food systems. Summary of original publications and the authors’ contribution I Mikkola, M. & Seppänen, L. 2006. Farmers’ new participation in food chains: making horizontal and vertical progress by networking. In: Langeveld, H. & Röling N. (Eds.). Changing European farming systems for a better future. New visions for rural areas. Wageningen, The Netherlands. Wageningen Academic Publishers: 267–271. II Mikkola, M. 2008. Coordinative structures and development of food supply chains. British Food Journal 110 (2): 189–205. III Mikkola, M. 2009. Shaping professional identity for sustainability. Evidence in Finnish public catering. Appetite 53 (1): 56–65. IV Mikkola, M. 2009. Catering for sustainability: building a dialogue on organic milk. Agronomy Research 7 (Special issue 2): 668–676. Minna Mikkola has been responsible for developing the generic research frame, particular research questions, the planning and collection of the data, their qualitative analysis and writing the articles I, II, III and IV. Dr Laura Seppänen has contributed to the development of the generic research frame and article I by introducing the author to the basic concepts of economic sociology and by supporting the writing of article II with her critical comments. Articles are printed with permission from the publishers.

Learning Challenges in Biogas Production for Sustainability : An activity theoretical study of a network from a swine industry chain

This study is about the challenges of learning in the creation and implementation of new sustainable technologies. The system of biogas production in the Programme of Sustainable Swine Production (3S Programme) conducted by the Sadia food processing company in Santa Catarina State, Brazil, is used as a case example for exploring the challenges, possibilities and obstacles of learning in the use of biogas production as a way to increase the environmental sustainability of swine production. The aim is to contribute to the discussion about the possibilities of developing systems of biogas production for sustainability (BPfS). In the study I develop hypotheses concerning the central challenges and possibilities for developing systems of BPfS in three phases. First, I construct a model of the network of activities involved in the BP for sustainability in the case study. Next, I construct a) an idealised model of the historically evolved concepts of BPfS through an analysis of the development of forms of BP and b) a hypothesis of the current central contradictions within and between the activity systems involved in BP for sustainability in the case study. This hypothesis is further developed through two actual empirical analyses: an analysis of the actors senses in taking part in the system, and an analysis of the disturbance processes in the implementation and operation of the BP system in the 3S Programme. The historical analysis shows that BP for sustainability in the 3S Programme emerged as a feasible solution for the contradiction between environmental protection and concentration, intensification and specialisation in swine production. This contradiction created a threat to the supply of swine to the food processing company. In the food production activity, the contradiction was expressed as a contradiction between the desire of the company to become a sustainable company and the situation in the outsourced farms. For the swine producers the contradiction was expressed between the contradictory rules in which the market exerted pressure which pushed for continual increases in scale, specialisation and concentration to keep the production economically viable, while the environmental rules imposed a limit to this expansion. Although the observed disturbances in the biogas system seemed to be merely technical and localised within the farms, the analysis proposed that these disturbances were formed in and between the activity systems involved in the network of BPfS during the implementation. The disturbances observed could be explained by four contradictions: a) contradictions between the new, more expanded activity of sustainable swine production and the old activity, b) a contradiction between the concept of BP for carbon credits and BP for local use in the BPfS that was implemented, c) contradictions between the new UNFCCC1 methodology for applying for carbon credits and the small size of the farms, and d) between the technologies of biogas use and burning available in the market and the small size of the farms. The main finding of this study relates to the zone of proximal development (ZPD) of the BPfS in Sadia food production chain. The model is first developed as a general model of concepts of BPfS and further developed here to the specific case of the BPfS in the 3S Programme. The model is composed of two developmental dimensions: societal and functional integration. The dimension of societal integration refers to the level of integration with other activities outside the farm. At one extreme, biogas production is self-sufficient and highly independent and the products of BP are consumed within the farm, while at the other extreme BP is highly integrated in markets and networks of collaboration, and BP products are exchanged within the markets. The dimension of functional integration refers to the level of integration between products and production processes so that economies of scope can be achieved by combining several functions using the same utility. At one extreme, BP is specialised in only one product, which allows achieving economies of scale, while at the other extreme there is an integrated production in which several biogas products are produced in order to maximise the outcomes from the BP system. The analysis suggests that BP is moving towards a societal integration, towards the market and towards a functional integration in which several biogas products are combined. The model is a hypothesis to be further tested through interventions by collectively constructing the new proposed concept of BPfS. Another important contribution of this study refers to the concept of the learning challenge. Three central learning challenges for developing a sustainable system of BP in the 3S Programme were identified: 1) the development of cheaper and more practical technologies of burning and measuring the gas, as well as the reduction of costs of the process of certification, 2) the development of new ways of using biogas within farms, and 3) the creation of new local markets and networks for selling BP products. One general learning challenge is to find more varied and synergic ways of using BP products than solely for the production of carbon credits. Both the model of the ZPD of BPfS and the identified learning challenges could be used as learning tools to facilitate the development of biogas production systems. The proposed model of the ZPD could be used to analyse different types of agricultural activities that face a similar contradiction. The findings could be used in interventions to help actors to find their own expansive actions and developmental projects for change. Rather than proposing a standardised best concept of BPfS, the idea of these learning tools is to facilitate the analysis of local situations and to help actors to make their activities more sustainable.

A tutorial survey of reinforcement learning

This paper gives a compact, self-contained tutorial survey of reinforcement learning, a tool that is increasingly finding application in the development of intelligent dynamic systems. Research on reinforcement learning during the past decade has led to the development of a variety of useful algorithms. This paper surveys the literature and presents the algorithms in a cohesive framework.

Bayesian Learning for Joint Sparse OFDM Channel Estimation and Data Detection

The impulse response of a typical wireless multipath channel can be modeled as a tapped delay line filter whose non-zero components are sparse relative to the channel delay spread. In this paper, a novel method of estimating such sparse multipath fading channels for OFDM systems is explored. In particular, Sparse Bayesian Learning (SBL) techniques are applied to jointly estimate the sparse channel and its second order statistics, and a new Bayesian Cramer-Rao bound is derived for the SBL algorithm. Further, in the context of OFDM channel estimation, an enhancement to the SBL algorithm is proposed, which uses an Expectation Maximization (EM) framework to jointly estimate the sparse channel, unknown data symbols and the second order statistics of the channel. The EM-SBL algorithm is able to recover the support as well as the channel taps more efficiently, and/or using fewer pilot symbols, than the SBL algorithm. To further improve the performance of the EM-SBL, a threshold-based pruning of the estimated second order statistics that are input to the algorithm is proposed, and its mean square error and symbol error rate performance is illustrated through Monte-Carlo simulations. Thus, the algorithms proposed in this paper are capable of obtaining efficient sparse channel estimates even in the presence of a small number of pilots.

Reinforcement Learning With Function Approximation for Traffic Signal Control

We propose, for the first time, a reinforcement learning (RL) algorithm with function approximation for traffic signal control. Our algorithm incorporates state-action features and is easily implementable in high-dimensional settings. Prior work, e. g., the work of Abdulhai et al., on the application of RL to traffic signal control requires full-state representations and cannot be implemented, even in moderate-sized road networks, because the computational complexity exponentially grows in the numbers of lanes and junctions. We tackle this problem of the curse of dimensionality by effectively using feature-based state representations that use a broad characterization of the level of congestion as low, medium, or high. One advantage of our algorithm is that, unlike prior work based on RL, it does not require precise information on queue lengths and elapsed times at each lane but instead works with the aforementioned described features. The number of features that our algorithm requires is linear to the number of signaled lanes, thereby leading to several orders of magnitude reduction in the computational complexity. We perform implementations of our algorithm on various settings and show performance comparisons with other algorithms in the literature, including the works of Abdulhai et al. and Cools et al., as well as the fixed-timing and the longest queue algorithms. For comparison, we also develop an RL algorithm that uses full-state representation and incorporates prioritization of traffic, unlike the work of Abdulhai et al. We observe that our algorithm outperforms all the other algorithms on all the road network settings that we consider.

A reinforcement learning approach to automatic generation control

This paper formulates the automatic generation control (AGC) problem as a stochastic multistage decision problem. A strategy for solving this new AGC problem formulation is presented by using a reinforcement learning (RL) approach This method of obtaining an AGC controller does not depend on any knowledge of the system model and more importantly it admits considerable flexibility in defining the control objective. Two specific RL based AGC algorithms are presented. The first algorithm uses the traditional control objective of limiting area control error (ACE) excursions, where as, in the second algorithm, the controller can restore the load-generation balance by only monitoring deviation in tie line flows and system frequency and it does not need to know or estimate the composite ACE signal as is done by all current approaches. The effectiveness and versatility of the approaches has been demonstrated using a two area AGC model. (C) 2002 Elsevier Science B.V. All rights reserved.

Efficient algorithms for learning kernels from multiple similarity matrices with general convex loss functions

In this paper we consider the problem of learning an n × n kernel matrix from m(1) similarity matrices under general convex loss. Past research have extensively studied the m = 1 case and have derived several algorithms which require sophisticated techniques like ACCP, SOCP, etc. The existing algorithms do not apply if one uses arbitrary losses and often can not handle m > 1 case. We present several provably convergent iterative algorithms, where each iteration requires either an SVM or a Multiple Kernel Learning (MKL) solver for m > 1 case. One of the major contributions of the paper is to extend the well knownMirror Descent(MD) framework to handle Cartesian product of psd matrices. This novel extension leads to an algorithm, called EMKL, which solves the problem in O(m2 log n 2) iterations; in each iteration one solves an MKL involving m kernels and m eigen-decomposition of n × n matrices. By suitably defining a restriction on the objective function, a faster version of EMKL is proposed, called REKL,which avoids the eigen-decomposition. An alternative to both EMKL and REKL is also suggested which requires only an SVMsolver. Experimental results on real world protein data set involving several similarity matrices illustrate the efficacy of the proposed algorithms.