Identifying optimal human-human connections for learning
Algorithmic determination+suggestion of the most optimal human connection that I should learn from
1/3

Other (People working in a common location, e.g. building.)

Observation

It is said that people don't quit companies, they quit bad managers. Similarly, some of you might agree that in school it was not a subject that was boring, but the way the teacher taught it that made all the difference!

In organizations too, Who one learns best from depends on how well the learner and the teacher interact, find common ground, etc. And these factors also affect the motivation of the learner to learn, as well as the motivation of the teacher to share selflessly.

Conclusion

With the advent of smart badges (or, at a lower-budget outlay, the smartphones of employees), it has become possible for companies to track the frequency, duration, and some outcomes of the interactions between employees. At a very granular 1:1 level. This data can be put through mathematical algorithmic analyses, e.g. organizational network analysis ONA ( http://www.informationarchitected.com/blog/using-organizational-network-analysis/ ) to identify optimal teacher:learner matchups.

Solution

ONA of employee interactions, cohesion & outcomes will be used in conjunction with existing data on each employee's skills (obtained via human resources, supervisor evaluations, self-reported, etc). These inputs shall be used to identify the most optimal human connection C that employee E may learn best from. Maybe connection A, B are also qualified, but if E is predicted to learn best from C, then E & C should do it! Give people choices of course + ONA insights to choose smartly!

How would you stage or advertise your hack?

I would launch an internal campaign (or a limited internal trial) that invites employees to actively upgrade their knowledge and skills and to earn prizes or other incentives for doing so. One group of employees is allowed to choose which "teachers" they approach (e.g. A, B, or C), whereas another group of employees is specifically asked to seek learning from designated teachers (e.g. C, in the case of employee E, as in the scenario I described previously). All learners later complete feedback forms. They are also tested on their learning, and the results are correlated with the learners' own reports regarding their learning experience as well as how the learner:teacher teaming up had been done. These analyses will be used to fine tune the ONA and matching algorithms. The pleasure/confidence that employees derive from new learning (initially for incentives/prizes) will reinforce their desire to seek more learning (with or without incentives).

Other entries in this project