There is a new approach to education in this country where data is used to accelerate learning; great examples include Apps4VA, EverFi, Unbound Concepts, and New York's Education Data Portal. Unfortunately this same trend is not being as widely applied to professional education (seminars, conferences, meetups, etc.). Why the double standard? Why can't data be used to accelerate professional education, especially at a time when job hopping is the new normal? BLUF: Data Visualization is a new tool to engage event attendees for more successful communication, education, and building of anti-fragile professional networks.
Education Evolution: From Lectures to Data
We are all familiar with the classic Elizabethan style educational format, where the teacher or professor lectures for some period of time, then tests us or gives us independent exercises. Many of the people reading this blog are also familiar with the recently emerged "Data Driven Education", where information is gathered about the student while studying in a number of formats (lecture, tutoring, peer-to-peer, independent, etc.), and that information is used in an algorithm to steer the student toward their most effective combination of educational formats. There are a number of good arguments for this new education format, such as the fact that lectures can be pre-recorded so students' time with educators can be more interactive and allow supervised learning independently or with peers. Data Driven Education also signifies a psychological shift by recognizing that not all students learn in the same way or at the same rate. Students need to discover and learn from their point of view.
Despite the connectivity of our new technological and mobile world, most of the events we attend as professionals still feature an industry leader in a lecture format. Why is this? Under classic educational systems gathering students' data is easier primarily because the students are a captive audience, they are required to provide the data, however as adults and as professionals we choose when and where to give feedback, if any. In addition, there are privacy concerns as those providing feedback don't necessarily want their feedback data to be widely associated with their name. We may want to migrate from classic lecture style education to data driven education, but the collection of professionals' data means they must be continually convinced to volunteer their data.
Passive vs Active Data Gathering
Making the argument to volunteer data puts the pressure on the educators, which can be a good thing. With a captive audience we can use the students' time to gather data through testing, whether the testing is appealing or not (usually not). With volunteered data, if the process isn't appealing to the professional/student, then the data will not be gathered; the best approach is to collect data as a natural course of events. Data can come naturally from web activity (http logging, cookies, link traces, etc.), during events (mobile app 'like' button during presentations, use of accelerometers to capture 'restlessness', pedometers and/or GPS during an all day/week conference), or from business operations (survey feedback, investigations, reports, purchase orders, etc.). In any case, we must avoid data gathering distractions during valuable event time at all costs.
Knowing that someone was restless during a boring presentation is not the same as testing subject proficiency, but the goal is not just to achieve a minimal test score, it is impossible to know everything at all times anyway; Today's open source approach requires an anti-fragile community that quickly supports outreach when it's sought (e.g. good coders are also good 'Googlers'). The goal is two-fold: to educate the group and partner need with ability (i.e. demand with supply), and this is where Data Visualization is important. People communicate through objects, especially objects of common interest, the data collected describes those objects, and data visualization naturally presents those objects, clearing the way for discussion and discovery. Great examples include The London Cycle Hire Journeys, National Gun Flow, and Microsoft ViralSearch. Having data visualizations like these, specific to your topic, and stationed throughout an event creates pockets of conversation, allows people to migrate between stations, allows others to answer questions through the interactivity of the data, and frees the speaker to focus on conversations where they're most needed, democratizing the entire experience. Data Visualization is a new tool to engage event attendees for more successful communication, education, and building of anti-fragile professional networks.