Month: November 2001

As background, I posted a link recently to an article by Peter Drucker that talked about how we are moving to a highly competitive knowledge society.  Education, in order to better serve the needs of this society, must adapt.  How?  It must help people create and maintain a continuous learning cycle.  Knowledge goes stale over time and knowledge workers, in order to continue to be productive at their jobs, need to constantly improve their domain expertise.

This is something K-Logs can help with.  Most people, when they leave school, take nothing with them besides what is between their ears and a few text books that are quickly put out of date.  Our current system forces people to go back to a classroom setting to rejuvenate their knowledge set.  Most people can't afford this.  Particularly given Drucker's predictions of the level of market competition there will be.

If students were required to build and maintain a K-Log during their years of residence at school, they would leave with: 1) a strong habit of continuous analysis and writing, 2) subscriptions to data streams (articles, documents, and other relevant data — both free and for fee $$),  3) living connections to teachers and students they met,  and 4) a chronicle of their learning process at school.

From the school's perspective, K-Logs could improve the economics of the relationship.  It could charge its students for RSS subscriptions to the Weblogs of teachers at the school (a continuous stream of insight provided by teachers that are constantly reading and analyzing the newest information available in the field of study) and other data streams.  It would also create a new channel for relationships with alumni that would provide a backchannel for insight on how knowledge they are learning in school is being applied in the real world.  Finally, it puts a whole new spin on what it means by going to a school — in this new world you just don't attend, rather you “join” the schools knowledge sharing community.

From the student's perspective, he/she could claim not only having attended a good school but also that they are continuously connected to that school's knowledge stream/system.    Would that be a benefit in a job interview?  You bet.  I always want to hire people that are always at the top of their game.  Also, a well maintained K-Log would provide potential job seekers with a living, breathing resume about what they have learned.  In a job interview, people often ask about the details of specific things people have learned or done.  It would be much more valuable to read about the experience  in a K-Log (you can use categories to limit access to K-Log data).

1) K-Log referrers (referers for techies).  Who linked to my K-Log?  Where is my traffic coming from?

2) Page views.  How much traffic is my K-Log getting and when is it getting it?

3) Top 100 list.  Who is has the most interesting K-logs?  Who is a trusted resource?  (note: Despite the potential problems, this is key to the identification of good resources.  Also, it points people to examples of good K-Logs they can emulate.)

4) Most popular links.  Which links are the most popular?  What are other people in the company looking at?  (note: this should include the time line for who posted this link first and so on until the last post.  This allows people to follow a thread of an idea across K-Logs)

5) Recent Updates.  Which K-logs have been recently updated and when?  (note:  if a favorites feature is deployed you can answer — which of my favorite K-logs have been recently updated?)

6) Search.  How can I find recently updated K-logs that contain this keyword?  What are the most popular search terms?

Additionally community functionality that may help, but I have not seen deployed yet:

7) Organizational directory of K-logs.  Who in the organization has a K-log?  When was it last updated?

8) K-log communities.  Which K-logs routinely link to each other (usually using a static blogrolling list)?  (note:  this would make a nice affiliation map that could work better and more simply than collaborative filtering).

9) Key word maps.  Which K-logs are most tightly associated with certain keywords?  How do they rank based on popularity?  (note:  this could help locate key domain experts)

10)  Most often cited K-logs by specific K-log.  Who are certain K-Loggers pointing to on a regular basis?  (note: this may be esoteric but it answers the question:  who are smart people I have identified pointing to?)

11) Project-based K-Log rings.  Which K-Logs are affiliated with specific projects?  How is a specific project progressing?  (note:  this is similar to the organization map, but can be set-up to allow ad-hoc groups to be set up)