A novel framework of training hidden Markov support vector machines from lightly-annotated data

Natural language understanding (NLU) aims to map sentences to their semantic mean representations. Statistical approaches to NLU normally require fully-annotated training data where each sentence is paired with its word-level semantic annotations. In this paper, we propose a novel learning framework which trains the Hidden Markov Support Vector Machines (HM-SVMs) without the use of expensive fully-annotated data. In particular, our learning approach takes as input a training set of sentences labeled with abstract semantic annotations encoding underlying embedded structural relations and automatically induces derivation rules that map sentences to their semantic meaning representations. The proposed approach has been tested on the DARPA Communicator Data and achieved 93.18% in F-measure, which outperforms the previously proposed approaches of training the hidden vector state model or conditional random fields from unaligned data, with a relative error reduction rate of 43.3% and 10.6% being achieved.

Learning to share knowledge in the Italian motorsport industry

The paper outlines a perspective on learning how to share knowledge in the context of inter-firm networks and highlights the essential role of participation in collaborative activities. This perspective suggests that knowledge sharing is not something achieved through the simple transfer of resources, but rather is an ongoing social accomplishment in which network firms constitute and re-constitute knowledge while engaging in collaborative activities. Empirical support for this view is offered by an in-depth and multiyear study of the development of collaborative relationships between a leading racing car manufacturer and its suppliers in the Italian motorsport industry. The study shows that knowledge is generated over time through the instigation of three knowledge sharing processes: the promotion of a culture of working together, co-location and the use of resident engineers, and shared education and training.

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.

Understanding institutional conversion:the case of the National Reporting and Learning System

This article focuses on one type of institutional change: conversion. One innovative approach to institutional change, the “political-coalitional approach”, acknowledges that: institutions can have unintended effects, which may privilege certain groups over others; institutions are often created and sustained through compromise with external actors; and institutions’ external context can vary significantly over time, as different coalitions’ power waxes and wanes. This approach helps explain the conversion of one institution drawn from the UK National Health Service, the National Reporting and Learning System. However, the shift of this system from producing formative information to facilitate learning to promote safer care, towards producing summative information to support resource allocation decisions, cannot be explained merely by examining the actions of external power coalitions. An internal focus, which considers factors that are normally viewed as “organisational” (such as leadership and internal stability), is also required.

Evaluating the skills strategy through a Graduate Certificate in Management:an experiential learning theory approach

Purpose – The purpose of this paper is to evaluate how a UK business school is addressing the Government's skills strategy through its Graduate Certificate in Management, and to identify good practice and development needs and to clarify how the Graduate Certificate is adapting to the needs of Generation X and Millennial students. The paper also aims to test Kolb and Kolb's experiential learning theory (ELT) in a business school setting. Design/methodology/approach – A case study methodology was adopted. In order to get a cross-section of views and triangulate the data, three focus groups were held, supported by reading documentation about the programme of study. Findings – The skills strategy is not just an ambition for some business schools, but is already part of the curriculum. Generation X and the Millennials have more in common with the positive attitudes associated with older generations than stereotyped views might allow. ELT provides a useful theoretical framework for evaluating a programme of study and student attitudes. Research limitations/implications – The research findings from one case study are reported, limiting the generalisability of the study. Practical implications – Good practice and development needs are identified which support the implementation of the Government's skills strategy and address employer concerns about student skills. Originality/value – New empirical data are reported which supports the use of ELT in evaluating programmes of study and student attitudes to work.

Social software in Higher Education:the diversity of applications and their contributions to students' learning experiences

Higher education institutions are increasingly using social software tools to support teaching and learning. Despite the fact that social software is often used in a social context, these applications can significantly contribute to the educational experience of a student. However, as the social software domain comprises a considerable diversity of tools, the respective tools can be expected to differ in the way they can contribute to teaching and learning. In this review on the educational use of social software, we systematically analyze and compare the diverse social software tools and identify their contributions to teaching and learning. By integrating established learning theory and the extant literature on the individual social software applications we seek to contribute to a theoretical foundation for social software use and the choice of tools. Case vignettes from several UK higher education institutions are used to illustrate the different applications of social software tools in teaching and learning.

IT and learning in Higher Education

Educational institutions are under pressure to provide high quality education to large numbers of students very efficiently. The efficiency target combined with the large numbers generally militates against providing students with a great deal of personal or small group tutorial contact with academic staff. As a result of this, students often develop their learning criteria as a group activity, being guided by comparisons one with another rather than the formal assessments made of their submitted work. IT systems and the World Wide Web are increasingly employed to amplify the resources of academic departments although their emphasis tends to be with course administration rather than learning support. The ready availability of information on the World Wide Web and the ease with which is may be incorporated into essays can lead students to develop a limited view of learning as the process of finding, editing and linking information. This paper examines a module design strategy for tackling these issues, based on developments in modules where practical knowledge is a significant element of the learning objectives. Attempts to make effective use of IT support in these modules will be reviewed as a contribution to the development of an IT for learning strategy currently being undertaken in the author’s Institution.

Engaging employers as partners in work-based learning assessment:proposal for a quality enhancement framework

This paper seeks to advance research and practice related to the role of employers in all stages of the assessment process of work-based learning (WBL) within a tripartite relationship of higher education institution (HEI), student and employer. It proposes a research-informed quality enhancement framework to develop good practice in engaging employers as partners in assessment. The Enhancement Framework comprises three dimensions, each of which includes elements and questions generated by the experiences of WBL students, HEI staff and employers. The three dimensions of the Enhancement Framework are: 1. ‘premises of assessment’ encompassing issues of learning, inclusion, standards and value; 2. ‘practice’, encompassing stages of assessment made up of course design, assessment task, responsibilities, support, grading and feedback; 3. ‘communication of assessment’ with the emphasis on role clarity, language and pathways. With its prompt questions, the Enhancement Framework may be used as a capacity-building tool for promoting, sustaining, benchmarking and evaluating productive dialogue and critical reflection about assessment between WBL partners. The paper concludes by emphasising the need for professional development as well as policy and research development, so that assessment in WBL can more closely correspond to the potentially transformative nature of the learning experience.

“Showing off” your mobile device:adult literacy learning in the classroom and beyond

For a very large number of adults, tasks such as reading. understanding, and using everyday items are a challenge. Although many community-based organizations offer resources and support for adults with limited literacy skills. current programs have difficulty reaching and retaining those that would benefit most. In this paper we present the findings of an exploratory study aimed at investigating how a technological solution that addresses these challenges is received and adopted by adult learners. For this, we have developed a mobile application to support literacy programs and to assist low-literacy adults in today's information-centric society. ALEX© (Adult Literacy support application for Experiential learning) is a mobile language assistant that is designed to be used both in the classroom and in daily life in order to help low-literacy adults become increasingly literate and independent. Through a long-term study with adult learners we show that such a solution complements literacy programs by increasing users' motivation and interest in learning, and raising their confidence levels both in their education pursuits and in facing the challenges of their daily lives.

Looking under the bonnet:factors affecting student adoption of e-learning systems in Jordan

The primary questions addressed in this paper are the following: what are the factors that affect students’ adoption of an e-learning system and what are the relationships among these factors? This paper investigates and identifies some of the major factors affecting students’ adoption of an e-learning system in a university in Jordan. E-learning adoption is approached from the information systems acceptance point of view. This suggests that a prior condition for learning effectively using e-learning systems is that students must actually use them. Thus, a greater knowledge of the factors that affect IT adoption and their interrelationships is a pre-cursor to a better understanding of student acceptance of e-learning systems. In turn, this will help and guide those who develop, implement, and deliver e-learning systems. In this study, an extended version of the Technology Acceptance Model (TAM) was developed to investigate the underlying factors that influence students’ decisions to use an e-learning system. The TAM was populated using data gathered from a survey of 486 undergraduate students using the Moodle based e-learning system at the Arab Open University. The model was estimated using Structural Equation Modelling (SEM). A path model was developed to analyze the relationships between the factors to explain students’ adoption of the e-learning system. Whilst findings support existing literature about prior experience affecting perceptions, they also point to surprising group effects, which may merit future exploration.

Disabling a family? Emotional dilemmas experienced in becoming a parent of a child with learning disabilities

Becoming the parent of a child diagnosed with learning disabilities can have a dramatic impact. Chrissie Rogers, the author of this article, is both a lecturer in education studies at Keele University and the mother of a daughter who has learning disabilities. She argues here that the pressures on mothers to produce ‘perfect’ babies and to meet all their needs are immense. These pressures arise from both internalised norms and societal expectations and, in the face of these pressures, parents may feel shock, loss and disappointment. These feelings may lead, in turn, to denial, anxiety and conflict affecting both the parents and the professionals involved with the family. Drawing on a series of in-depth interviews and personal narratives, Chrissie Rogers makes a powerful case for the importance of support, whether that support is formal or informal. She suggests that, without the right levels of support and understanding, having a child with a diagnosis of learning disability can disable the whole family.

Semisupervised learning of hierarchical latent trait models for data visualization

Recently, we have developed the hierarchical Generative Topographic Mapping (HGTM), an interactive method for visualization of large high-dimensional real-valued data sets. In this paper, we propose a more general visualization system by extending HGTM in three ways, which allows the user to visualize a wider range of data sets and better support the model development process. 1) We integrate HGTM with noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM). This enables us to visualize data of inherently discrete nature, e.g., collections of documents, in a hierarchical manner. 2) We give the user a choice of initializing the child plots of the current plot in either interactive, or automatic mode. In the interactive mode, the user selects "regions of interest," whereas in the automatic mode, an unsupervised minimum message length (MML)-inspired construction of a mixture of LTMs is employed. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. 3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualization plots, since they can highlight the boundaries between data clusters. We illustrate our approach on a toy example and evaluate it on three more complex real data sets. © 2005 IEEE.

Semi-supervised learning of statistical models for natural language understanding

Natural language understanding is to specify a computational model that maps sentences to their semantic mean representation. In this paper, we propose a novel framework to train the statistical models without using expensive fully annotated data. In particular, the input of our framework is a set of sentences labeled with abstract semantic annotations. These annotations encode the underlying embedded semantic structural relations without explicit word/semantic tag alignment. The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). Our experimental results on the DARPA communicator data show that both CRFs and HM-SVMs outperform the baseline approach, previously proposed hidden vector state (HVS) model which is also trained on abstract semantic annotations. In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved in F-measure.

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.

Accommodating special needs users in the evaluation of an m-Learning application:a case study

Adult illiteracy rates are alarmingly high worldwide. The portability, affordability, and ease of use of mobile (or handheld) devices offer a realistic opportunity to provide novel, context-sensitive literacy resources to adults with limited literacy skills. To this end, we developed the concept of ALEX – a mobile Adult Literacy support application for EXperiential learning (Lumsden et al., 2005). On the basis of a medium-fidelity prototype of this application, we conducted an evaluation of ALEX using participants from our in tended user group. This evaluation had two goals: (a) to assess the usefulness of the ALEX concept and the usability of its current design; and (b) to reflect on the appropriateness of our evaluation process given the literacy-related needs of our participants. This paper outlines our approach to this evaluation as well as the results we obtained and our reflections on the process.