Designing and implementing a blended learning approach to executive SCM education:a case study from Ireland

The National Institute for Transport and Logistics (NITL) is Ireland’s centre of excellence for supply chain management (SCM). As part of its mission to promote the development of supply chain expertise in Irish business, it designs and delivers executive modular learning programmes. In 2004, as part of a drive to create more flexible learning opportunities for course participants, NITL designed and implemented an eLearning programme, which involved converting traditionally tutored modules to online modules. This paper describes the rationale behind this initiative and the significance of technology as an enabling tool for executive education, as well as detailing the design and implementation processes for the pilot module. The paper concludes with a critique of the expected and actual benefits realised, as well as future development considerations.

Learning task specific distributed paragraph representations using a 2-tier convolutional neural network

We introduce a type of 2-tier convolutional neural network model for learning distributed paragraph representations for a special task (e.g. paragraph or short document level sentiment analysis and text topic categorization). We decompose the paragraph semantics into 3 cascaded constitutes: word representation, sentence composition and document composition. Specifically, we learn distributed word representations by a continuous bag-of-words model from a large unstructured text corpus. Then, using these word representations as pre-trained vectors, distributed task specific sentence representations are learned from a sentence level corpus with task-specific labels by the first tier of our model. Using these sentence representations as distributed paragraph representation vectors, distributed paragraph representations are learned from a paragraph-level corpus by the second tier of our model. It is evaluated on DBpedia ontology classification dataset and Amazon review dataset. Empirical results show the effectiveness of our proposed learning model for generating distributed paragraph representations.

A gloss composition and context clustering based distributed word sense representation model

In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned representations outperform the publicly available embeddings on half of the metrics in the word similarity task, 6 out of 13 sub tasks in the analogical reasoning task, and gives the best overall accuracy in the word sense effect classification task, which shows the effectiveness of our proposed distributed distribution learning model.

Development of a work-based learning MSc course which incorporates the development and demonstration of professional engineering competence standards

UK engineering standards are regulated by the Engineering Council (EC) using a set of generic threshold competence standards which all professionally registered Chartered Engineers in the UK must demonstrate, underpinned by a separate academic qualification at Masters Level. As part of an EC-led national project for the development of work-based learning (WBL) courses leading to Chartered Engineer registration, Aston University has started an MSc Professional Engineering programme, a development of a model originally designed by Kingston University, and build around a set of generic modules which map onto the competence standards. The learning pedagogy of these modules conforms to a widely recognised experiential learning model, with refinements incorporated from a number of other learning models. In particular, the use of workplace mentoring to support the development of critical reflection and to overcome barriers to learning is being incorporated into the learning space. This discussion paper explains the work that was done in collaboration with the EC and a number of Professional Engineering Institutions, to design a course structure and curricular framework that optimises the engineering learning process for engineers already working across a wide range of industries, and to address issues of engineering sustainability. It also explains the thinking behind the work that has been started to provide an international version of the course, built around a set of globalised engineering competences. © 2010 W J Glew, E F Elsworth.

Learning sentiment classification model from labeled features

We propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon. Preferences on expectations of sentiment labels of those lexicon words are expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than exiting weakly-supervised sentiment classification methods despite using no labeled documents.

Deschooling society? A lifelong learning network for sustainable communities, urban regeneration and environmental technologies

The complexity and multifaceted nature of sustainable lifelong learning can be effectively addressed by a broad network of providers working co-operatively and collaboratively. Such a network involving the third, public and private sector bodies must realise the full potential of accredited flexible and blended formal learning, contextual opportunities offered by enablers of informal and non formal learning and the affordances derived from the various loose and open spaces that can make social learning effective. Such a conception informs the new Lifelong Learning Network Consortium on Sustainable Communities, Urban Regeneration and Environmental Technologies established and led by the Lifelong Learning Centre at Aston University. This paper offers a radical, reflective and political evaluation of its first year in development arguing that networked learning of this type could prefigure a new model for lifelong learning and sustainable education that renders the city itself a creative medium for transformative learning and sustainability.

Is there a global model of learning organizations? An empirical, cross-nation study

This paper develops and tests a learning organization model derived from HRM and dynamic capability literatures in order to ascertain the model's applicability across divergent global contexts. We define a learning organization as one capable of achieving on-going strategic renewal, arguing based on dynamic capability theory that the model has three necessary antecedents: HRM focus, developmental orientation and customer-facing remit. Drawing on a sample comprising nearly 6000 organizations across 15 countries, we show that learning organizations exhibit higher performance than their less learning-inclined counterparts. We also demonstrate that innovation fully mediates the relationship between our conceptualization of the learning organization and organizational performance in 11 of the 15 countries we examined. It is the first time in our knowledge that these questions have been tested in a major, cross-global study, and our work contributes to both HRM and dynamic capability literatures, especially where the focus is the applicability of best practice parameters across national boundaries.

Network learning:an empirically-derived conceptual model of learning by groups of organizations

Building on a previous conceptual article, we present an empirically derived model of network learning - learning by a group of organizations as a group. Based on a qualitative, longitudinal, multiple-method empirical investigation, five episodes of network learning were identified. Treating each episode as a discrete analytic case, through cross-case comparison, a model of network learning is developed which reflects the common, critical features of the episodes. The model comprises three conceptual themes relating to learning outcomes, and three conceptual themes of learning process. Although closely related to conceptualizations that emphasize the social and political character of organizational learning, the model of network learning is derived from, and specifically for, more extensive networks in which relations among numerous actors may be arms-length or collaborative, and may be expected to change over time.

Personal knowledge development in online learning:a model for measuring externalisation, combination and internalisation

This paper presents a model for measuring personal knowledge development in online learning environments. It is based on Nonaka‘s SECI model of organisational knowledge creation. It is argued that Socialisation is not a relevant mode in the context of online learning and was therefore not covered in the measurement instrument. Therefore, the remaining three of SECI‘s knowledge conversion modes, namely Externalisation, Combination, and Internalisation were used and a measurement instrument was created which also examines the interrelationships between the three modes. Data was collected using an online survey, in which online learners report on their experiences of personal knowledge development in online learning environments. In other words, the instrument measures the magnitude of online learners‘ Externalisation and combination activities as well as their level of internalisation, which is the outcome of their personal knowledge development in online learning.

Evolution, recurrency and kernels in learning to model inflation

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. We use non-linear, artificial intelligence techniques, namely, recurrent neural networks, evolution strategies and kernel methods in our forecasting experiment. In the experiment, these three methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. There is evidence in the literature that evolutionary methods can be used to evolve kernels hence our future work should combine the evolutionary and kernel methods to get the benefits of both.

Using observation ageing to improve Markovian model learning in QoS engineering

Markovian models are widely used to analyse quality-of-service properties of both system designs and deployed systems. Thanks to the emergence of probabilistic model checkers, this analysis can be performed with high accuracy. However, its usefulness is heavily dependent on how well the model captures the actual behaviour of the analysed system. Our work addresses this problem for a class of Markovian models termed discrete-time Markov chains (DTMCs). We propose a new Bayesian technique for learning the state transition probabilities of DTMCs based on observations of the modelled system. Unlike existing approaches, our technique weighs observations based on their age, to account for the fact that older observations are less relevant than more recent ones. A case study from the area of bioinformatics workflows demonstrates the effectiveness of the technique in scenarios where the model parameters change over time.

The effectiveness of workplace coaching:A meta-analysis of learning and performance outcomes; scale development; theoretical model of individual differences and longitudinal study

The extant literature on workplace coaching is characterised by a lack of theoretical and empirical understanding regarding the effectiveness of coaching as a learning and development tool; the types of outcomes one can expect from coaching; the tools that can be used to measure coaching outcomes; the underlying processes that explain why and how coaching works and the factors that may impact on coaching effectiveness. This thesis sought to address these substantial gaps in the literature with three linked studies. Firstly, a meta-analysis of workplace coaching effectiveness (k = 17), synthesizing the existing research was presented. A framework of coaching outcomes was developed and utilised to code the studies. Analysis indicated that coaching had positive effects on all outcomes. Next, the framework of outcomes was utilised as the deductive start-point to the development of the scale measuring perceived coaching effectiveness. Utilising a multi-stage approach (n = 201), the analysis indicated that perceived coaching effectiveness may be organised into a six factor structure: career clarity; team performance; work well-being; performance; planning and organizing and personal effectiveness and adaptability. The final study was a longitudinal field experiment to test a theoretical model of individual differences and coaching effectiveness developed in this thesis. An organizational sample of 84 employees each participated in a coaching intervention, completed self-report surveys, and had their job performance rated by peers, direct reports and supervisors (a total of 352 employees provided data on participant performance). The results demonstrate that compared to a control group, the coaching intervention generated a number of positive outcomes. The analysis indicated that coachees’ enthusiasm, intellect and orderliness influenced the impact of coaching on outcomes. Mediation analysis suggested that mastery goal orientation, performance goal orientation and approach motivation in the form of behavioural activation system (BAS) drive, were significant mediators between personality and outcomes. Overall, the findings of this thesis make an original contribution to the understanding of the types of outcomes that can be expected from coaching, and the magnitude of impact coaching has on outcomes. The thesis also provides a tool for reliably measuring coaching effectiveness and a theoretical model to understand the influence of coachee individual differences on coaching outcomes.