Mathemagenic

Matt Mover cites Listening Leader newsletter:

Continuing success comes from value-creating innovation stimulated by disciplined listening. Occasional surveys are insufficient. Organizations need to build listening systems that capture, summarize, and disseminate the unmet dreams and unfulfilled wants of multiple customer groups, including existing, prospective, and internal customers (employees).

[...]Listening leads to learning, which sets the stage for innovation. Innovation is more likely when employees are well informed about the customer, unafraid to try something new, and committed to the organization's success.

Sebastian Fiedler comments (bold is mine)

I believe that any learning environment design should address this issue too. A network of learning project (action and reflection) logs can provide a powerful "listening system" for everybody involved. Facilitators can use it as a diagnostic device that allows them to step in with counselling and mentoring offers, learners can tune in to the projects of their peers, project members can listen to their team members's contributions and comments, people on the periphery can tune in for a variety of reason, lurk, listen, and make themselves heard whenever they feel they could contribute.

Again, the biggest issue are the "learning myths" of everybody involved. Parents who think they cannot contribute much, learners who believe they should only listen to a single educational authority, domain experts who feel they cannot learn from people who are novice to a given domain, peers who do not expect to gain from looking at each others efforts, ... these are the people we have to deal with in almost any given context. ... no matter what tools and technologies you would like to apply. We suffer from a general lack of reflection and activity on personal learning.

Do Completion Rates Matter? by Will Thalheimer

When knowledge is applied immediately after learning, completion-rates don’t matter, but ease-of-access and simplicity do. When the on-the-job performance situation follows the learning by more than a few hours, additional instructional supports are needed to ensure that knowledge and skills are retrievable from memory. By completing a well-designed e-learning course, learners provide themselves with the instructional supports they’ll need to maintain their learning until they can use it on the job.

As usually Will provides good input for thinking (you can subscribe to the newsletter too). The arguments provided in the article can help to make choices between e-learning and KM tools.

• In most cases KM systems usually support access to information, sometimes help to understand it, but usually do not have more advanced instructional support to help long-term remembering or practicing with application. So, I would say that KM systems are good for those learning situations when learner have a problem to solve.
• E-learning systems provide more instructional support, so they would be better choice for those case when learning is needed for a future goals.

This also explains why corporate on-line communities work so well in Q&A mode or to provide awareness of what's going on, but fail when it comes to support longer-term learning. E.g. orientation training for newcomers would work better than hope that they can find out about certain topic from community discussions. (In this piece I talk only about learning about certain topic, not co-creation in dialogue, apprenticeship or building own network).

Gallery of Data Visualization: The Best and Worst of Statistical Graphics [via McGee's Musings]. Good source to reflect on the use graphics in your own work :)

Also: ACCENT Principles for effective graphical display

The ACCENT principles emphasize, or accent, six aspects which determine the effectiveness of a visual display for portraying data.

Apprehension:

• Ability to corectly perceive relations among variables.
• Does the graph maximize apprehension of the relations among variables?

Clarity:

• Ability to visually distinguish all the elements of a graph.
• Are the most important elements or relations visually most prominent?

Consistency:

• Ability to interpret a graph based on similarity to previous graphs.
• Are the elements, symbol shapes and colors consistent with their use in previous graphs?

Efficiency:

• Ability to portray a possibly complex relation in as simple a way as possible.
• Are the elements of the graph economically used? Is the graph easy to interpret?

Necessity:

• The need for the graph, and the graphical elements.
• Is the graph a more useful way to represent the data than alternatives (table, text)? Are all the graph elements necessary to convey the relations?

Truthfulness:

• Ability to determine the true value represented by any graphical element by its magnitude relative to the implicit or explicit scale.
• Are the graph elements accurately positioned and scaled?