The roles of inducer size and distance in the Ebbinghaus illusion (Titchener circles)

Although the Ebbinghaus illusion is commonly used as an example of a simple size-contrast effect, previous studies have emphasised its complexity by identifying many factors that potentially influence the magnitude of the illusion. Here, in a series of three experiments, we attempt to simplify this complexity. In each trial, subjects saw a display comprising, on one side, a target stimulus surrounded by inducers and, on the other, an isolated probe stimulus. Their task was to indicate whether the probe appeared larger or smaller than the target. Probe size was adjusted with a one-up, one-down staircase procedure to find the point of subjective equality between probe and target. From these experiments, we argue that the apparent effects of inducer size are often confounded by the relative completeness of the inducing surround and that factors such as the similarity of the inducers and target are secondary. We suggest a simple model that can explain most of the data in terms of just two primary and independent factors: the relative size of the inducers and target, and the distance between the inducers and the target. The balance between these two factors determines whether the size of the target is underestimated or overestimated. © 2005 a Pion publication.

Exploring the relationships between network change and network learning

This paper relates the concept of network learning - learning by a group of organizations as a group - to change and notions of change management. Derived initially from a review of literature on organizational learning (OL) and interorganizational networks, and secondary cases of network learning, the concept was evaluated and developed through empirical investigation of five network learning episodes in the group of organizations that comprises the English prosthetics service. We argue that the notion of network learning enables a richer understanding of developments in networks over extended periods of time than can be afforded through more established concepts of change and change management alone.

Learning to collaborate:a study of individual and organizational learning, and interorganizational relationships

This paper examines learning to collaborate in the context of industrial supply relationships. Evidence of collaboration, and individual and organizational learning, from an in-depth case study of a large organization and its relations with two key suppliers is discussed. Analytic methods developed to elicit such evidence and provide insights into learning processes and outcomes are presented. It is argued that it is possible for an organization and individuals to learn to develop resilient collaborative relationships, but this requires a more thorough consideration and understanding of issues such as trust, commitment and teamwork than has been typical to date. Suggestions for future practice and research are presented.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Collaborative filtering and deep learning based hybrid recommendation for cold start problem

Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned from a deep learning neural network and applies them to the timeSVD++ CF model. Extensive experiments are run on a large Netflix rating dataset for movies. Experiment results show that the proposed hybrid recommendation model provides a good prediction for cold start items, and performs better than four existing recommendation models for rating of non-cold start items.

Dynamic learning as entrepreneurial action in the context of open innovation:an instrumental case from a communities-of-practice perspective

Recent scholarly discussion on open innovation put forward the notion that an organisation's ability to internalise external knowledge and learn from various sources in undertaking new product development is crucial to its competitive performance. Nevertheless, little attention has been paid to how growth-oriented small firms identify and exploit entrepreneurial opportunities (i.e. take entrepreneurial action) related to such development, in an open innovation context, from a social learning perspective. This chapter, based on an instrumental case-firm, demonstrates analytically how learning as entrepreneurial action takes place, drawing on situated learning theory. It is argued that such learning is dynamic in nature and is founded on specific organising principles that foster both inter- and intracommunal learning. © 2012, IGI Global.

The state of the nations:current and future developments in technology enhanced learning

Right across Europe technology is playing a vital part in enhancing learning for an increasingly diverse population of learners. Learning is increasingly flexible, social and mobile and supported by high quality multi-media resources. Institutional VLEs are seeing a shift towards open source products and these core systems are supplemented by a range of social and collaborative learning tools based on web 2.0 technologies. Learners undertaking field studies and those in the workplace are coming to expect that these off-campus experiences will also be technology-rich whether supported by institutional or user-owned devices. As well as keeping European businesses competitive, learning is seen as a means of increasing social mobility and supporting an agenda of social justice. For a number of years the EUNIS E-Learning Task Force (ELTF) has conducted snapshot surveys of e-learning across member institutions, collected case studies of good practice in e-learning see (Hayes, et al., 2009) in references, supported a group looking at the future of e-learning, and showcased the best of innovation in its e-learning Award. Now for the first time the ELTF membership has come together to undertake an analysis of developments in the member states and to assess what this might mean for the future. The group applied the techniques of World Café conversation and Scenario Thinking to develop its thoughts. The analysis is unashamedly qualitative and draws on expertise from leading universities across eight of the EUNIS member states. What emerges is interesting in terms of the common trends in developments in all of the nations and similarities in hopes and concerns about the future development of learning.

The design and evaluation of distributed virtual environment to support learning in global operations management

The primary goal of this research is to design and develop an education technology to support learning in global operations management. The research implements a series of studies to determine the right balance among user requirements, learning methods and applied technologies, on a view of student-centred learning. This research is multidisciplinary by nature, involving topics from various disciplines such as global operations management, curriculum and contemporary learning theory, and computer aided learning. Innovative learning models that emphasise on technological implementation are employed and discussed throughout this research.

Visual field and structural alterations in age-related macular degeneration

The thesis investigated progression of the central 10° visual field with structural changes at the macula in a cross-section of patients with varying degrees of agerelated macular degeneration (AMD). The relationships between structure and function were investigated for both standard and short-wavelength automated perimetry (SWAP). Factors known to influence the measure of visual field progression were considered, including the accuracy of the refractive correction on SWAP thresholds and the learning effect. Techniques of assessing the structure to function relationships between fundus images and the visual field were developed with computer programming and evaluated for repeatability. Drusen quantification of fundus photographs and retro-mode scanning laser ophthalmoscopic images was performed. Visual field progression was related to structural changes derived from both manual and automated methods. Principal Findings: • Visual field sensitivity declined with advancing stage of AMD. SWAP showed greater sensitivity to progressive changes than standard perimetry. • Defects were confined to the central 5°. SWAP defects occurred at similar locations but were deeper and wider than corresponding standard perimetry defects. • The central field became less uniform as severity of AMD increased. SWAP visual field indices of focal loss were of more importance when detecting early change in AMD, than indices of diffuse loss. • The decline in visual field sensitivity over stage of severity of AMD was not uniform, whereas a linear relationship was found between the automated measure of drusen area and visual field parameters. • Perimetry exhibited a stronger relationship with drusen area than other measures of visual function. • Overcorrection of the refraction for the working distance in SWAP should be avoided in subjects with insufficient accommodative facility. • The perimetric learning effect in the 10° field did not differ significantly between normal subjects and AMD patients. • Subretinal deposits appeared more numerous in retro-mode imaging than in fundus photography.

Comparing different approach and avoidance models of learning and personality in the prediction of work, university, and leadership outcomes

Jackson (2005) developed a hybrid model of personality and learning, known as the learning styles profiler (LSP) which was designed to span biological, socio-cognitive, and experiential research foci of personality and learning research. The hybrid model argues that functional and dysfunctional learning outcomes can be best understood in terms of how cognitions and experiences control, discipline, and re-express the biologically based scale of sensation-seeking. In two studies with part-time workers undertaking tertiary education (N=137 and 58), established models of approach and avoidance from each of the three different research foci were compared with Jackson's hybrid model in their predictiveness of leadership, work, and university outcomes using self-report and supervisor ratings. Results showed that the hybrid model was generally optimal and, as hypothesized, that goal orientation was a mediator of sensation-seeking on outcomes (work performance, university performance, leader behaviours, and counterproductive work behaviour). Our studies suggest that the hybrid model has considerable promise as a predictor of work and educational outcomes as well as dysfunctional outcomes.

Studies on the generalisation of Gaussian processes and Bayesian neural networks

The assessment of the reliability of systems which learn from data is a key issue to investigate thoroughly before the actual application of information processing techniques to real-world problems. Over the recent years Gaussian processes and Bayesian neural networks have come to the fore and in this thesis their generalisation capabilities are analysed from theoretical and empirical perspectives. Upper and lower bounds on the learning curve of Gaussian processes are investigated in order to estimate the amount of data required to guarantee a certain level of generalisation performance. In this thesis we analyse the effects on the bounds and the learning curve induced by the smoothness of stochastic processes described by four different covariance functions. We also explain the early, linearly-decreasing behaviour of the curves and we investigate the asymptotic behaviour of the upper bounds. The effect of the noise and the characteristic lengthscale of the stochastic process on the tightness of the bounds are also discussed. The analysis is supported by several numerical simulations. The generalisation error of a Gaussian process is affected by the dimension of the input vector and may be decreased by input-variable reduction techniques. In conventional approaches to Gaussian process regression, the positive definite matrix estimating the distance between input points is often taken diagonal. In this thesis we show that a general distance matrix is able to estimate the effective dimensionality of the regression problem as well as to discover the linear transformation from the manifest variables to the hidden-feature space, with a significant reduction of the input dimension. Numerical simulations confirm the significant superiority of the general distance matrix with respect to the diagonal one.In the thesis we also present an empirical investigation of the generalisation errors of neural networks trained by two Bayesian algorithms, the Markov Chain Monte Carlo method and the evidence framework; the neural networks have been trained on the task of labelling segmented outdoor images.

A techno-economic assessment of the use of fast pyrolysis bio-oil from UK energy crops in the production of electricity and combined heat and power

This thesis investigates the cost of electricity generation using bio-oil produced by the fast pyrolysis of UK energy crops. The study covers cost from the farm to the generator’s terminals. The use of short rotation coppice willow and miscanthus as feedstocks was investigated. All costs and performance data have been taken from published papers, reports or web sites. Generation technologies are compared at scales where they have proved economic burning other fuels, rather than at a given size. A pyrolysis yield model was developed for a bubbling fluidised bed fast pyrolysis reactor from published data to predict bio-oil yields and pyrolysis plant energy demands. Generation using diesel engines, gas turbines in open and combined cycle (CCGT) operation and steam cycle plants was considered. The use of bio-oil storage to allow the pyrolysis and generation plants to operate independently of each other was investigated. The option of using diesel generators and open cycle gas turbines for combined heat and power was examined. The possible cost reductions that could be expected through learning if the technology is widely implemented were considered. It was found that none of the systems analysed would be viable without subsidy, but with the current Renewable Obligation Scheme CCGT plants in the 200 to 350 MWe range, super-critical coal fired boilers co-fired with bio-oil, and groups of diesel engine based CHP schemes supplied by a central pyrolysis plant would be viable. It was found that the cost would reduce with implementation and the planting of more energy crops but some subsidy would still be needed to make the plants viable.

Accountability, organisational learning and risks to patient safety in England: conflict or compromise?

This article examines the current risk regulation regime, within the English National Health Service (NHS), by investigating the two, sometimes conflicting, approaches to risk embodied within the field of policies towards patient safety. The first approach focuses on promoting accountability and is built on legal principles surrounding negligence and competence. The second approach focuses on promoting learning from previous mistakes and near-misses, and is built on the development of a ‘safety culture’. Previous work has drawn attention to problems associated with risk-based regulation when faced with the dual imperatives of accountability and organisational learning. The article develops this by considering whether the NHS patient safety regime demonstrates the coexistence of two different risk regulation regimes, or merely one regime with contradictory elements. It uses the heuristic device of ‘institutional logics’ to examine the coexistence of and interrelationship between ‘organisational learning’ and ‘accountability’ logics driving risk regulation in health care.

The influence of learning styles, enrolment status and gender on academic performance of optometry undergraduates

Purpose: This cross-sectional study was designed to determine whether the academic performance of optometry undergraduates is influenced by enrolment status, learning style or gender. Methods: Three hundred and sixty undergraduates in all 3 years of the optometry degree course at Aston University during 2008–2009 were asked for their informed consent to participate in this study. Enrolment status was known from admissions records. An Index of Learning Styles (http://www4.nscu.edu/unity/lockers/users/f/felder/public/Learning-Styles.html) determined learning style preference with respect to four different learning style axes; active-reflective, sensing-intuitive, visual-verbal and sequential-global. The influence of these factors on academic performance was investigated. Results: Two hundred and seventy students agreed to take part (75% of the cohort). 63% of the sample was female. There were 213 home non-graduates (entrants from the UK or European Union without a bachelor’s degree or higher), 14 home graduates (entrants from the UK or European Union with a bachelor’s degree or higher), 28 international non-graduates (entrants from outside the UK or European Union without a bachelor’s degree or higher) and 15 international graduates (entrants from outside the UK or European Union with a bachelor’s degree or higher). The majority of students were balanced learners (between 48% and 64% across four learning style axes). Any preferences were towards active, sensing, visual and sequential learning styles. Of the factors investigated in this study, learning styles were influenced by gender; females expressed a disproportionate preference for the reflective and visual learning styles. Academic performance was influenced by enrolment status; international graduates (95% confidence limits: 64–72%) outperformed all other student groups (home non graduates, 60–62%; international non graduates, 55–63%) apart from home graduates (57–69%). Conclusion: Our research has shown that the majority of optometry students have balanced learning styles and, from the factors studied, academic performance is only influenced by enrolment status. Although learning style questionnaires offer suggestions on how to improve learning efficacy, our findings indicate that current teaching methods do not need to be altered to suit varying learning style preferences as balanced learning styles can easily adapt to any teaching style (Learning Styles and Pedagogy in Post-16 Learning: A Systematic and Critical Review. London, UK: Learning and Skills Research Centre, 2004).

Does reviewing lead to better learning and decision making? Answers from a randomized stock market experiment

Background - The literature is not univocal about the effects of Peer Review (PR) within the context of constructivist learning. Due to the predominant focus on using PR as an assessment tool, rather than a constructivist learning activity, and because most studies implicitly assume that the benefits of PR are limited to the reviewee, little is known about the effects upon students who are required to review their peers. Much of the theoretical debate in the literature is focused on explaining how and why constructivist learning is beneficial. At the same time these discussions are marked by an underlying presupposition of a causal relationship between reviewing and deep learning. Objectives - The purpose of the study is to investigate whether the writing of PR feedback causes students to benefit in terms of: perceived utility about statistics, actual use of statistics, better understanding of statistical concepts and associated methods, changed attitudes towards market risks, and outcomes of decisions that were made. Methods - We conducted a randomized experiment, assigning students randomly to receive PR or non–PR treatments and used two cohorts with a different time span. The paper discusses the experimental design and all the software components that we used to support the learning process: Reproducible Computing technology which allows students to reproduce or re–use statistical results from peers, Collaborative PR, and an AI–enhanced Stock Market Engine. Results - The results establish that the writing of PR feedback messages causes students to experience benefits in terms of Behavior, Non–Rote Learning, and Attitudes, provided the sequence of PR activities are maintained for a period that is sufficiently long.