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.

Creating and managing value in collaborative networks

This is a theoretical paper that examines the interplay between individual and collective capabilities and competencies and value transactions in collaborative environments. The theory behind value creation is examined and two types of value are identified, internal value (Shareholder value) and external value (Value proposition). The literature on collaborative enterprises/network is also examined with particular emphasis on supply chains, extended/virtual enterprises and clusters as representatives of different forms and maturities of collaboration. The interplay of value transactions and competencies and capabilities are examined and discussed in detail. Finally, a model is presented which consists of value transactions and a table which compares the characteristics of different types of collaborative enterprises/networks. It is proposed that this model presents a platform for further research to develop an in-depth understanding into how value may be created and managed in collaborative enterprises/networks.

Retail multinational learning:a case study of Tesco

Purpose - This article examines the internationalisation of Tesco and extracts the salient lessons learned from this process. Design/methodology/ approach - This research draws on a dataset of 62 in-depth interviews with key executives, sell- and buy-side analysts and corporate advisers at the leading investment banks in the City of London to detail the experiences of Tesco's European expansion. Findings - The case study of Tesco illuminates a number of different dimensions of the company's international experience. It offers some new insights into learning in international distribution environments such as the idea that learning is facilitated by uncertainty or "shocks" in the international retail marketplace; the size of the domestic market may inhibit change and so disable international learning; and learning is not necessarily facilitated by step-by-step incremental approaches to expansion. Research limitations/implications - The paper explores learning from a rather broad perspective, although it is hoped that these parameters can be used to raise a new set of more detailed priorities for future research on international retail learning. It is also recognised that the data gathered for this case study focus on Tesco's European operations. Practical implications - This paper raises a number of interesting issues such as whether the extremities of the business may be a more appropriate place for management to experiment and test new retail innovations, and the extent to which retailers take self-reflection seriously. Originality/value - The paper applies a new theoretical learning perspective to capture the variety of experiences during the internationalisation process, thus addressing a major gap in our understanding of the whole internationalisation process. © Emerald Group Publishing Limited.

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.

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.

Noise, regularizers, and unrealizable scenarios in online learning from restricted training sets

We study the dynamics of on-line learning in multilayer neural networks where training examples are sampled with repetition and where the number of examples scales with the number of network weights. The analysis is carried out using the dynamical replica method aimed at obtaining a closed set of coupled equations for a set of macroscopic variables from which both training and generalization errors can be calculated. We focus on scenarios whereby training examples are corrupted by additive Gaussian output noise and regularizers are introduced to improve the network performance. The dependence of the dynamics on the noise level, with and without regularizers, is examined, as well as that of the asymptotic values obtained for both training and generalization errors. We also demonstrate the ability of the method to approximate the learning dynamics in structurally unrealizable scenarios. The theoretical results show good agreement with those obtained by computer simulations.

International technology transfer and collaborative new product development:evidence and a case from the machine tool industry

In recent years, it has become increasingly common for companies to improve their competitiveness and find new markets by extending their operations through international new product development collaborations involving technology transfer. Technology development, cost reduction and market penetration are seen as the foci in such collaborative operations with the aim being to improve the competitive position of both partners. In this paper, the case of technology transfer through collaborative new product development in the machine tool sector is used to provide a typical example of such partnerships. The paper outlines the links between the operational aspects of collaborations and their strategic objectives. It is based on empirical data collected from the machine tool industries in the UK and China. The evidence includes longitudinal case studies and questionnaire surveys of machine tool manufacturers in both countries. The specific case of BSA Tools Ltd and its Chinese partner the Changcheng Machine Tool Works is used to provide an in-depth example of the operational development of a successful collaboration. The paper concludes that a phased coordination of commercial, technical and strategic interactions between the two partners is essential for such collaborations to work.

An exploratory framework for analysing international retail learning

It is argued that international retail research has overlooked an essential component of the retail internationalization process, notably learning. This paper proposes an exploratory framework that enables the application of learning theory to the study of international retailing. The paper provides a meaningful starting point for developing an overarching framework which would represent one sort of re-conceptualization of the retail internationalization process, and arguably a new perspective for reinterpreting, re-evaluating and refining the existing literature on international retailing. Alongside this exploratory framework, we present a series of research propositions that might serve as an agenda for research into international retail learning. The paper concludes with a summary of the key themes and ways in which the area of international retail learning may be investigated.

International retail joint venture learning

Where retail entry mode decision-making is examined in the literature, it has almost exclusively focused upon international store acquisitions and franchising growth and expansion. In contrast, international joint venture decision-making processes are visibly absent in the international retail literature. This article explores three retail multinationals' international retail joint venturing experiences, extracting some of the salient lessons learned at each stage of the joint venture development process and their concurrent impact on the whole internationalisation process. Suggestions for further research are made on the basis of gaps in the international retail literature and the lessons extracted from the cases under investigation. © 2006 Taylor & Francis.

Bayesian online algorithms for learning in discrete Hidden Markov Models

We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.

The whats, whys and hows of supporting dyslexic students. A review of ‘Learning Disabilities in Higher Education and Beyond: An International Perspective’ edited by Susan A. Vogel, Gila Vogel, Varda Sharoni & Orit Dahan, USA: York Press, 2003, 384 pages + xv, ISBN 0912752742

Book review

Intercultural training for international placements

Despite its increasing popularity, much intercultural training is not developed with the same level of rigour as training in other areas. Further, research on intercultural training has brought inconsistent results about the effectiveness of such training. This PhD thesis develops a rigorous model of intercultural training and applies it to the preparation of British students going on work/study placements in France and Germany. It investigates the reasons for inconsistent training success by looking at the cognitive learning processes in intercultural training, relating them to training goals, and by examining the short- and long-term transfer of intercultural training into real-life encounters with people from other cultures. Two cognitive trainings based on critical incidents were designed for online delivery. The training content relied on cultural practice dimensions from the GWBE study (House, Hanges, Javidan, Dorfman & Gupta, 2004). Of the two trainings, the 'singlemode training' aimed to develop declarative knowledge, which is necessary to analyse and understand other cultures. The 'concurrent training' aimed to develop declarative and procedural knowledge, which is needed to develop skills for dealing with difficult situations in a culturally appropriate way. Participants (N-48) were randomly assigned to one of the two training conditions. Declarative learning appeared as a process of steady knowledge increase, while procedural learning involved cognitive re-categorisation rather than knowledge increase. In a negotiation role play with host-country nationals directly after the online training, participants of the concurrent training exhibited a more initiative negotiation style than participants of the single-mode training. Comparing cultural adjustment and performance of training participants during their time abroad with an untrained control group, participants of the concurrent training showed the qualitatively best development in adjustment and performance. Besides intercultural training, multicultural personality traits were assessed and proved to be a powerful predictor of adjustment and, indirectly, of performance abroad.