Motivating students to learn through good and helpful coursework feedback

The 2008 National Student Survey revealed that: 44% of full-time students in England did not think that the feedback on their work had been prompt nor did they agree that the feedback on their work helped them clarify things that they did not understand (HEFCE, 2008). Computer Science and Engineering & Technology have been amongst the poorest performers in this aspect as they ranked in the lower quartile (Surridge, 2007, p.32). Five years since the first NSS survey, assessment and feedback remains the biggest concern. Dissatisfaction in any aspect of studies demotivates students and can lead to disengagement and attrition. As the student number grows, the situation can only get worse if nothing is done about it. We have conducted a survey to investigate views on assessment and feedback from Engineering, Mathematics and Computing students. The survey aims at investigating the core issues of dissatisfaction in assessment and feedback and ways in which UK Engineering students can learn better through helpful feedback. The study focuses on collecting students' experiences with feedback received in their coursework, assignments and quizzes in Computing Science modules. The survey reveals the role of feedback in their learning. The results of the survey help to identify the forms of feedback that are considered to be helpful in learning and the time frame for timely feedback. We report on the findings of the survey. We also explore ways to improve assessment and feedback in a bid to better engage engineering students in their studies.

Learning to research - researching to learn

Learning to Research Researching to Learn explores the integration of research into teaching and learning at all levels of higher education. The chapters draw on the long and ongoing debate about the teaching research nexus in universities. Although the vast majority of academics believe that there is an important and valuable link between teaching and research, the precise nature of this relationship continues to be contested. The book includes chapters that showcase innovative ways of learning to research; how research is integrated into coursework teaching; how students learn the processes of research, and how universities are preparing students to engage with the world. The chapters also showcase innovative ways of researching to learn, exploring how students learn through doing research, how they conceptualise the knowledge of their fields of study through the processes of doing research, and how students experiment and reflect on the results produced. These are the key issues addressed by this anthology, as it brings together analyses of the ways in which university teachers are developing research skills in their students, creating enquiry-based approaches to teaching, and engaging in education research themselves. The studies here explore the links between teaching, learning and research in a range of contexts, from pre-enrolment through to academic staff development, in Australia, the UK, the US, Singapore and Denmark. Through a rich array of theoretical and methodological approaches, the collection seeks to further our understanding of how universities can play an effective role in educating graduates suited to the twenty-first century

To what extent and in what manner do Chinese and Indian contract research organisation learn and upgrade with the unbundling the global pharmaceutical R&D value chain?

The paper applies the GVC framework to analyse the organisational and geographical reconfiguration of the global R&D function of leading US and European pharmaceutical MNCs. Though pharmaceutical MNCs have been outsourcing clinical trial activities since the mid-1990s, the outsourcing of discovery research tasks is a phenomenon of the 2000s (Ramirez 2013). Moreover, in the context of a crisis of R&D productivity and increasing pressure from shareholders, a number of US and European pharmaceutical MNCs are breaking up their R&D function in an attempt to increase flexibility and reduce risk as well as costs and are thereby restructuring the global architecture of their R&D function. This break-up, or unbundling (Sako 2006), of the R&D function is particularly interesting given the prevalence of market failure in innovation (Howells et al 2008), the non-modular nature of the R&D process in this industry (Pisano 2006) and the strategic important of this activity to the core competence and long-term competitive advantage of firms in this sector. The focus of this paper is on the outsourcing of R&D activities to Chinese and Indian independently-owned contract research organisations (CROs) and the way these firms are becoming integrated as service providers into the global R&D function (or R&D value chain) of pharmaceutical MNCs. Above all the paper is concerned with the development of capabilities of CROs from these two countries and the dynamics of upgrading in GVCs in knowledge-intensive functions. The paper therefore discusses the role of both knowledge flows within global pharmaceutical R&D value chains as well as national innovation systems on the development of capabilities of Chinese and Indian CROs. Our analysis is based on data from semi-structured interviews collected from senior R&D managers from a sample of ten US and European pharmaceutical MNCs and owners and senior R&D managers from five Chinese and five Indian CROs who are providing research services to MNCs in this industry. We discuss the emergence of R&D outsourcing in this industry and the nature and mechanisms of knowledge flows within R&D value chains. The embeddedness of CROS in the national innovation systems of their home countries is also discussed.

Can a student learn optimally from two different teachers

We explore the effects of over-specificity in learning algorithms by investigating the behavior of a student, suited to learn optimally from a teacher B, learning from a teacher B' ? B. We only considered the supervised, on-line learning scenario with teachers selected from a particular family. We found that, in the general case, the application of the optimal algorithm to the wrong teacher produces a residual generalization error, even if the right teacher is harder. By imposing mild conditions to the learning algorithm form, we obtained an approximation for the residual generalization error. Simulations carried out in finite networks validate the estimate found.

Efforts towards adaptability enhancement:how manufacturing and service industries can learn from each other

This paper discusses the recent emerging efforts for adaptability enhancement of Japanese industries to cope with a volatile demand environment. It is based on an analysis of data obtained from respondent companies. The analysis is focused on both manufacturing and manufacturing- related service industries such as construction/maintenance, software supply, manufacturing consultation and logistics industries to highlight their current situation, the sense of crisis in Japanese companies and possible future directions in relation to the two industry sectors. The principal conclusion is that for most companies consideration of a revision or modification to its cost structure is an essential requirement for survival in the global competitive environment.

The use of electronic techniques in the construction of machines which can learn

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Probability distribution modelling to improve stability in nonlinear MIMO control

We consider the direct adaptive inverse control of nonlinear multivariable systems with different delays between every input-output pair. In direct adaptive inverse control, the inverse mapping is learned from examples of input-output pairs. This makes the obtained controller sub optimal, since the network may have to learn the response of the plant over a larger operational range than necessary. Moreover, in certain applications, the control problem can be redundant, implying that the inverse problem is ill posed. In this paper we propose a new algorithm which allows estimating and exploiting uncertainty in nonlinear multivariable control systems. This approach allows us to model strongly non-Gaussian distribution of control signals as well as processes with hysteresis. The proposed algorithm circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider.

Responding to crisis through strategic knowledge management

Purpose - Many managers would like to take a strategic approach to preparing the organisation to avoid impending crisis but instead find themselves fire-fighting to mitigate its impact. This paper seeks to examine an organisation which made major strategic changes in order to respond to the full effect of a crisis which would be realised over a two to three year period. At the root of these changes was a strategic approach to managing knowledge. The paper's purpose is to reflect on managers' views of the impact this strategy had on preparing for the crisis and explore what happened in the organisation during and after the crisis. Design/methodology/approach - The paper examines a case-study of a financial services organisation which faced the crisis of its impending dissolution. The paper draws upon observations of change management workshops, as well as interviews with organisational members of a change management task force. Findings - The response to the crisis was to recognise the importance of the people and their knowledge to the organisation, and to build a strategy which improved business processes and communication flow across the divisions, as well as managing the departure of knowledge workers from an organisation in the process of being dissolved. Practical implications - The paper demonstrates the importance of building a knowledge management strategy during times of crisis, and draws out important lessons for organisations facing organisational change. Originality/value - The paper represents a unique opportunity to learn from an organisation adopting a strategic approach to managing its knowledge during a time of crisis. © Emerald Group Publishing Limited.

Introduction to the theory and application of data envelopment analysis:a foundation text with integrated software

The book aims to introduce the reader to DEA in the most accessible manner possible. It is specifically aimed at those who have had no prior exposure to DEA and wish to learn its essentials, how it works, its key uses, and the mechanics of using it. The latter will include using DEA software. Students on degree or training courses will find the book especially helpful. The same is true of practitioners engaging in comparative efficiency assessments and performance management within their organisation. Examples are used throughout the book to help the reader consolidate the concepts covered. Table of content: List of Tables. List of Figures. Preface. Abbreviations. 1. Introduction to Performance Measurement. 2. Definitions of Efficiency and Related Measures. 3. Data Envelopment Analysis Under Constant Returns to Scale: Basic Principles. 4. Data Envelopment Analysis under Constant Returns to Scale: General Models. 5. Using Data Envelopment Analysis in Practice. 6. Data Envelopment Analysis under Variable Returns to Scale. 7. Assessing Policy Effectiveness and Productivity Change Using DEA. 8. Incorporating Value Judgements in DEA Assessments. 9. Extensions to Basic DEA Models. 10. A Limited User Guide for Warwick DEA Software. Author Index. Topic Index. References.

Moving from HRM to knowledge productivity:a study of whether and how organizations become learning organizations

Practitioners and academics are in broad agreement that, above all, organizations need to be able to learn, to innovate and to question existing ways of working. This thesis develops a model to take into account, firstly, what determines whether or not organizations endorse practices designed to facilitate learning. Secondly, the model evaluates the impact of such practices upon organizational outcomes, measured in terms of products and technological innovation. Researchers have noted that organizations that are committed to producing innovation show great resilience in dealing with adverse business conditions (e.g. Pavitt, 1991; Leonard Barton, 1998). In effect, such organizations bear many of the characteristics associated with the achievement of ‘learning organization’ status (Garvin, 1993; Pedler, Burgoyne & Boydell, 1999; Senge, 1990). Seven studies are presented to support this theoretical framework. The first empirical study explores the antecedents to effective learning. The three following studies present data to suggest that people management practices are highly significant in determining whether or not organizations are able to produce sustained innovation. The thesis goes on to explore the relationship between organizational-level job satisfaction, learning and innovation, and provides evidence to suggest that there is a strong, positive relationship between these variables. The final two chapters analyze learning and innovation within two similar manufacturing organizations. One manifests relatively low levels of innovation whilst the other is generally considered to be outstandingly innovative. I present the comparative framework for exploring the different approaches to learning manifested by the two organizations. The thesis concludes by assessing the extent to which the theoretical model presented in the second chapter is borne out by the findings of the study. Whilst this is a relatively new field of inquiry, findings reveal that organizations have a much stronger chance of producing sustained innovation where they manage people proactively where people process themselves to be satisfied at work. Few studies to date have presented empirical evidence to substantiate theoretical endorsements to engage in higher order learning, so this research makes an important contribution to existing literature in this field.

An investigation of the factor-analytic approach to the determination of abilities involved in psychomotor learning

This research began with an attempt to solve a practical problem, namely, the prediction of the rate at which an operator will learn a task. From a review of the literature, communications with researchers in this area and the study of psychomotor learning in factories it was concluded that a more fundamental approach was required which included the development of a task taxonomy. This latter objective had been researched for over twenty years by E. A. Fleishman and his approach was adopted. Three studies were carried out to develop and extend Fleishman's approach to the industrial area. However, the results of these studies were not in accord with FIeishman's conclusions and suggested that a critical re-assessment was required of the arguments, methods and procedures used by Fleishman and his co-workers. It was concluded that Fleishman's findings were to some extent an artifact of the approximate methods and procedures which he used in the original factor analyses and that using the more modern computerised factor analytic methods a reliable ability taxonomy could be developed to describe the abilities involved in the learning of psychomotor tasks. The implications for a changing-task or changing-subject model were drawn and it was concluded that a changing task and subject model needs to be developed.