Towards a collective knowledge system
As illustrated in Figure 1, the DRIVER toolset has all of the properties of a Collective Knowledge System:
- User generated content - most of the content is created by the community. Whether the documentation artefacts in the wiki or the learning path knowledge-base, the information always originates from the users and can be evolved by them.
- Human-machine synergy - Automation of tasks such as collecting tags from the artefacts during browsing, presenting similar learning paths to the one just captured or providing recommendations, spares the user from collecting this information on her own.
- Increasing returns with scale - as more learners rate existing learning paths, the search tool brings up the most used and better rated results, contributing with higher valued information to the user.
- Emergent knowledge - the system offers recommendations for guidance through the artefacts, based on existing learning paths in the knowledge-base, inferring new knowledge from the existing data. Of course, the recommendation heuristic can be extended to improve the results and to more suitably provide effective recommendations. Nevertheless, the basis is there.
In short, the proposed collaborative environment provides an extensible scaffold for building and extending a collective knowledge acquisition system, where the community of learners can share their insight without too much effort and benefit from its collective wisdom.