User roles in asynchronous distributed collaborative idea generation

This paper presents the findings of an exploratory study within a real-life context that investigates participant behaviour and emergent user roles in asynchronous distributed collaborative idea generation by a defined community of users. In the study, a high-fidelity prototype of an online virtual ideas room was built and used by a Community of Interest consisting of representatives from 10 different voluntary organisations spread across Denmark. The study revealed five user roles, which the authors propose that future asynchronous distributed collaborative idea generation platforms should consider

Building creative confidence in idea management processes to improve idea generation in new product development teams

This is a scoping paper that aims to establish effective practices and key players in the domain of Idea Management. The paper defines Idea Management as the generation, evaluation and selection of ideas. The purpose of the paper is to map the current landscape of methodologies and tools in order to identify gaps and support the development of a framework to enhance creative confidence in idea management. The study has two key research questions: (i) what factors are influencing current idea generation practices and (ii) what tools and approaches exist for idea generation. This will help identify how creative confidence can influence the idea generation processes. Creative confidence is the capability to come up with breakthrough ideas, associated with the bravery to perform. If stimulated in the right way with a valuable framework, its impact on employees’ performance is significant in improving team members’ innovation performance and quality of ideas

Rethinking the Agreement in Human Evaluation Tasks

Human evaluations are broadly thought to be more valuable the higher the inter-annotator agreement. In this paper we examine this idea. We will describe our experiments and analysis within the area of Automatic Question Generation. Our experiments show how annotators diverge in language annotation tasks due to a range of ineliminable factors. For this reason, we believe that annotation schemes for natural language generation tasks that are aimed at evaluating language quality need to be treated with great care. In particular, an unchecked focus on reduction of disagreement among annotators runs the danger of creating generation goals that reward output that is more distant from, rather than closer to, natural human-like language. We conclude the paper by suggesting a new approach to the use of the agreement metrics in natural language generation evaluation tasks

Generative Cooperative Net for Image Generation and Data Augmentation

How to build a good model for image generation given an abstract concept is a fundamental problem in computer vision. In this paper, we explore a generative model for the task of generating unseen images with desired features. We propose the Generative Cooperative Net (GCN) for image generation. The idea is similar to generative adversarial networks except that the generators and discriminators are trained to work accordingly. Our experiments on hand-written digit generation and facial expression generation show that GCN's two cooperative counterparts (the generator and the classifier) can work together nicely and achieve promising results. We also discovered a usage of such generative model as an data-augmentation tool. Our experiment of applying this method on a recognition task shows that it is very effective comparing to other existing methods. It is easy to set up and could help generate a very large synthesized dataset.Comment: 12 pages, 8 figure

The Net Generation enters university: What are the implications for Technology Enhanced Learning?

The term Net generation suggests that the generation of young people born after 1983 are different from any preceding generation because they have been exposed to digital technology in their day-to-day existence, and that this is has a profound impact on their attitudes and approach to learning. Examining the use of the terms Net generation and Digital Natives this paper reports a survey of first year undergraduate students in the UK. This paper, based on research conducted in the spring of 2008 examines whether there is a distinct Net generation amongst first year UK university students and if there are significant differences attributable to age, gender or disciplinary differences. It concludes that whilst there are significant changes taking place amongst first year undergraduate students in the UK they are far more complex than the idea of a single new generation would suggest

Inequality and Growth in a Knowledge Economy

We develop a two sector growth model to understand the relation between inequality and growth. Agents, who are endowed with different levels of knowledge, select either into a retail or a manufacturing sector. Agents in the manufacturing sector match to carry out production. A by-product of production is creation of ideas that spill over to the retail sector and improve productivity, thereby causing growth. Ideas are generated according to an idea production function that takes the knowledge of all the agents in a firm as arguments. We go on to study how an increase in the inequality of the knowledge distribution affects the growth rate. A change in the distribution not only affects the occupational choice of agents, but also the way agents match within the manufacturing sector. We show that if the idea generation function is sufficiently convex, an increase in inequality raises the growth rate of the economy.Inequality, growth, idea generation, matching, knowledge