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The crowdsourced feedback model provides a community setting for customers to share their ideas and expand on the ideas of other customers. The IdeaScale incentive and voting process is extremely important in harnessing this flow of customer ideas and making it an integral component of product innovation strategies.

Full online book. Reader feedback forms.

Using segments of rich media makes it possible to aggregate context and meaning on these chunks by using a number of different mechanisms. Starting with a granular node -- be it a sound bite, visual clip or written fact -- it is possible to aggregate contextual metadata through a series of steps that emergently progress from: * Starting with thousands of defined Audio Sound Bites & visual clips * Rating sound bites and clustering them with folksonomy tags * Sequencing audio sound bites within playlists * Collaboratively building larger sequences with nested playlists * Independently controlling the video & audio tracks with 2-dimensional nested playlists * Evaluating Multiple Storylines and Hypotheses with a 2-dimensional playlist matrix * Visualizing complex networks by mapping out feedback loop relationships between nodes

It seems likely that the university grew out of the apprenticeship model of an expert working closely with an apprentice, assigning them challenging tasks and then providing guidance as needed to carry out those tasks, as well as offering ongoing feedback on their work. This model, or its modern day embodiment of "the expert individual tutor," remains the most effective demonstrated approach to education.

The HuWY project aims to support young people's eParticipation in policies about the Internet and its governance, through a distributed discussion. HuWY partners provide information, support and organise influential audiences for young people's suggestions. The Hub websites hold supporting information and structured space for results and feedback from policy-makers. Young people choose the topics and questions, host the discussions on their web pages and post the results on the Hubs.

This blog is a place for readers to discuss the beta version of our new book, "Evolving Collective Intelligence" by Douglas C. Engelbart, Valerie Landau, and Eileen Clegg. After reading the beta version, Do you have a quote to contribute? Clarifications on the content? An idea for another essay? Feedback will be input for our improved first edition, in keeping with Doug's philosophy of continuous improvement and collaboration. Please go to the "about" page.

I have written a wrapper to expose openlibrary.org api data as RDF. It is written in Python and deployed on Google App Engine. It is only an illustration of Linked Data publication, and in order to get a first feedback, so do not rely on it in your application since the URL or the content will change in the future.

An unbook is more like software: 1. An unbook is never finished, but rather continually updated, based on feedback from users andtheir evolving needs. 2. An unbook is released in versions. As in open source software, version 1.0 of an unbook is a significant milestone, indicating that it is stable and reliable enough for use by the general public. The significance of a new release is indicated by the size of the gap: For example, the difference between 1.1 and 1.1.3 is minor, while the difference between 1.1 and 2.0 is major. 3. An unbook is supported by a community of users who share their experiences and best practices with each other, and help each other troubleshoot problems encountered in their practice areas. An unbook's community is a very real part of the unbook's development team. An unbook is mindware: software for the mind.

MindCanvas is a online research service to gather insights about your customers' thoughts & feelings. Online surveys require users to complete boring html forms. You can use our Game-like Elicitation Methods (GEMs) to gather user feedback. At the end of the study, you can use our Visual Analysis Engine to order rich visualizations of your data.

Database systems are notorious for being hard to use. It is even more difficult to integrate data from multiple sources and collaborate on large data sets with people outside your organization. Without an easy way to offer all the collaborators access to the same server, data sets get copied, emailed and ftp'd--resulting in multiple versions that get out of sync very quickly. Today we're introducing Google Fusion Tables on Labs, an experimental system for data management in the cloud. It draws on the expertise of folks within Google Research who have been studying collaboration, data integration, and user requirements from a variety of domains. Fusion Tables is not a traditional database system focusing on complicated SQL queries and transaction processing. Instead, the focus is on fusing data management and collaboration: merging multiple data sources, discussion of the data, querying, visualization, and Web publishing. We plan to iteratively add new features to the systems as we get feedback from users.

I've set up this site to provide space for me to share ideas with a "Board of Directors", so I can get feedback on refining the ideas, and advice on how and when to share these ideas. Bootstrapping Collective Intelligence: This represents my response to Christina Engelbart's challenge during her presentation, and the Program For The Future challenge. GTD.brain. This is my entry to the contest for how people are using the PersonalBrain to implement David Allen's Getting Things Done methods: RESOLVING: This is a 3-D framework I've been developing, most influenced by Bill's Appreciation-Influence-Control philosophy. Combinatorial Modeling: applying combinatorial theory to assigning values based on models of N-node systems