Knowledgebase Access Methods

It is not enough to collect a lot of data and organize it nicely. We need to be able to look at the data in a useful way. Security issues issues were discussed earlier, so now we are free to concentrate on the happier aspects of a database - useful access methods.

The database must be easily searchable. A good starting point of comparison for organized knowledge is an Internet search engine such as Google, but it's not enough for complex and statistical searches. We need the database to be easily computable. An initial anchor for comparison to computable knowledge is Wolfram Alpha. The Semantic Web is not there yet, and the tools we have so far are not enough. Internet search often fails to find and identify meaningful information. Computation inquiries often fail to be computed themselves. But we can dream, and this type of dreams does tend to come true soon enough. In the mean time, we can get ready.

Visualization tools for query results must be available. From simple interactive tools such as TinkerPlots, to complex tools such as Matlab. Since the former is specializes in schools, and the latter is a specialized tool for scientists and engineers, there is a lot of room for more tools suitable for the uninitiated public. Graphs, tables, statistical analysis - all interactive and intuitive, oh my. Gapminder has some interesting data nicely visualized with Google Motion Chart. It makes one optimistic about the near future visualization capabilities.

Another requirement of the knowlwdgebase is that it will be amendable by the public. Anyone with an insight will be able to add it - wiki-style. This means that notes, comments and tags can be added to any item in the database, and immediately become a searchable and computable part of the knowledge base. Naturally, an anonymous tagging will have to be marked as less dependable - part of the Knowledgebase Data. There can also be room for opinions to be entered into the knowledgebase. Some practices may be designated as suggested "Best Practices". As long as these are clearly marked as opinions, they can sit there without interfering with the consensus.

In addition to comments and tags, any ad-hoc analysis made by a knowledgebase user (the public) may be fed back into the knowledgebase. For example, if a lay researcher (any member of the public) discovers a meaningful rise in the test scores of a certain age group in a certain school district, this fact may be recorded in the knowledgebase, so it can become a building block for subsequent queries by others - like looking into the possibility that the school district superintendent might be doing something right.

With a powerful tool of any kind, comes the danger of abuse or misuse. In the case of a tool for managing information, this danger translates into disinformation, misinformation and misinterpretation. Disinformation and misinformation are quite successfully minimized in wikipedia, so there is room for optimism. Misinterpretation is a much tougher problem, since it leaves no mark on the knowledgebase, and therefore can't be corrected by those who know better. In a computable knowledgebase, this problem is even more severe than in a merely searchable knowledgebase, such as wikipedia. The only thing the knowledgebase can do to minimize misinterpretation of information , is to discourage simplistic analysis.

The most complete way to discourage simplistic analysis is to educate the masses to such a great extent, that everybody knows very well how to perform valid analysis. Forget it. Not even when the new and improved education system is in place. The human drive towards misunderstanding and simplistic views is too great, and this isn't going to change any time soon.

A less complete way is to nurture a healthy sense of scientific doubt: To encourage people to look at any result with a critical eye, bearing in mind the fallibility of data and of opinions that can be derived from it. This should be easier, since the late 20^th century and early 21^st century did a good job shaking our belief in any authority. Some see this as part of post-modernism. There is no need - or easy way - to educate the masses in general towards this tendency. The access venues to the knowledgebase can keep reminding the user of the limitation of the analysis. The access venues can also use some of their own background analysis to emphasize the doubt when appropriate: When much of the data at the basis of the analysis is tagged as not very dependable; when the analysis is based on a small amount of data; when there is extrapolation involved; when the system has detected similar analyses producing dissimilar results; when the system detects an iterative activity that may be an effort to fine-tune the analysis results according to a preconceived target, etc.

As usual, the problems and solutions described here are just a part of what reality is. It's the part that can be seen from the outside of the Research-Based Education project, and much more will become apparent the more we go into that project.

What was unthinkable a few decades ago - freely available statistics about huge piles of information - is now a mundane fact, with computers and the Internet being widely available. We need just a bit more than that in order to achieve real research-based education.