The businessenvironment, learner profiles, training environment, and IT infrastructure areall things that instructional designers consider in their design plans. Formany in the instructional design space, the term big data is something that is probably neither interesting nor relevantto the craft of design.
In the coursework leadingto a master’s in educational technology, any discussion about using data toinform the design process is generally tied to creating courses that improvetest scores. There is nothing about designing experiences to generate a certainand specific type of data.
Where does bigdata fit in?
Too much and too fastfor us to keep up
Let’sbegin with a definition of big data:
“Big data is data that exceeds the processingcapacity of conventional database systems. The data is too big, moves too fast,or doesn’t fit the strictures of your database architectures.” (Source: Edd Dumbill, writing in O’Reilly Radar)
Inother words, big data is the term the technology world uses to describe theproblem of data gathering tools amassing data at rates, volumes, and speeds thatcannot be parsed and analyzed in intelligent ways by our current set of tools.
Bigdata is akin to a production and distribution process where production ismoving faster than distribution. What’s left is overflowing shelves and storageunits full of product that continues to amass at exponential rates, with nomeans to organize and distribute that product.
Sohow does this problem affect instructional design? Part of our job has been to assess theeffectiveness of our designs in helping to achieve the desired outcomes of ourinitiatives. We have traditionally done this by measuring whether we were ableto affect test scores (beginning with a pre-test and concluding with apost-test), whether learners were happy with their learning experience(measured through smile sheets), and, in the ideal fictional world, whether ourinitiatives impacted business results.
Whydo I call this last element the “ideal fictional world?” It’s mostly because,aside from a few rare cases, training initiatives are hard to isolate as asignificant factor in business result transformations. Rarely is trainingsomething that a manager can isolate as a factor. It’s also important to notethat all of these data points are “after the fact” touch points. None of thesedata points are meant to be collected during the learning experience itself.
Dealing with big data inlearning initiatives
Intoday’s digital business, the use of computer-generated data to supportbusiness decisions is the norm. Enter the problem of big data, where there issimply too much data being gathered too quickly and being sent too quickly tomake the “right” decisions. All that being said, the science of analytics anddata science is helping businesses understand what data to focus on, given acompany’s key initiatives.
Solutionsto big data, regardless of the tools you’re using, will always require astrategy to mine the data you need to make the right business decisions. Thesheer volume of data available, and the representation of its currency (realtime), makes the science of analytics that much more exciting and relevant tomaking intelligent business decisions.
Giventhe power of big data to inform business decisions, posting web content thatfeeds the data stream you’re mining is a skill that will rise in demand. Forthe most part, web content creators develop their content and then try to findthe data streams that give them the closest match to their intentions forposting as possible.
Feed the data stream
Thealternative to that approach is to design web content specific to the data youare looking for. This goes back to the old adage of not collecting data thatyou’re not going to use. Given the power of the technology and toolsetsavailable, we are now able to design content and build user experiences to feeda specific data stream that is aligned with the measurement of business goalsand the data feed that informs business decisions.
Use the right tools:take control
Theintroduction of TinCan to the technology-based learning industry gives thelearning and development world an infrastructure with which to begin thecollection of data in real time (this is key) that we deem relevant to ourbusinesses. If you quickly read the last sentence, the important part is the phrase“that we deem relevant.”
Inother words, we have the opportunity to begin collecting real time data during(that’s right—during) user experiences of our content in much the same way websiteanalytics collects real-time data. Before TinCan there were industry-specificanalytic standards; for example, the aviation industry had a standard thatallowed it to collect real-time data during flight simulations and match thatsimulation-generated data to in-flight data to identify performance gaps inpilots.
ButTinCan is the first infrastructure that gives anybody who designs webexperiences for teaching and learning (including mobile web) the opportunity todictate what data they intentionally want to gather, as opposed to simplytrying to figure out how to use whatever data they happen to gather. It’s areversal of the process.
How do you know they’reengaged? An example
Now,where it gets interesting is the idea that we can include in our designstrategy the notion of certain activities or experiences delivering data to astream that feeds our goals.
Ina recent blogpost I gave a very simple example of building an online orientation coursethat feeds a data stream dedicated to measuring learner “engagement.” A goalfor most orientation programs is to engage new hires with the company—to getthem to appreciate a company’s history so that they feel that they are part ofa legacy.
Ifyou’re having someone read content online, there is no way to tell if theyactually read the content or merely stare blankly at it. The same is true forpaper-based reading, of course. So how can we get real-time data that feeds astream measuring engagement? It’s good to note that we don’t have to “prove”engagement; we simply want to create data that supports the case forengagement. In other words, we are not trying to prove causation, justcorrelation.
Well,one approach that might help measure whether someone is reading and engagedwith content is to: 1) Take off any parameters that force somebody to readanything because that just forces them to click “Next.” Make reading optional.2) Separate content into small chunks that require navigating from one contentpiece to the next as an optional navigation element. 3) Attach data gatheringto the action of moving from one content piece to the next. 4) Analyze thingslike, How deep the users go into the content? How much time for each piece ofcontent matched to their navigation? Is it all relatively the same, or areusers spending a decreasing amount of time per content piece? As a collectionof data, this set is looking at the real-time interests of the users. Are theyinterested in moving forward? How much? Are they as enthusiastic at the beginningas they are at the end? Do some content pieces pique more interest than others?
Create intentionaldesigns
Theexample given isn’t a recommendation for designing orientation programs. Theexample is to illustrate an intentional design that feeds real-time datamatched to a specific goal. The picture gets increasingly complex when you takesomething like new hire engagement and correlate that with worker performanceand potentially even something like correlating new-hire engagement withcompany brand recognition. If the reason you’ve hired marketing people is toget better brand recognition on the Web, then designing learning experiencesthat feed a set of data streams that will allow you to analyze the success ofthat correlation ought to be as important to an instructional designer aschoosing the right visuals.
Herelies the intersection of instructional systems design and big data. Now thatnew emerging standards like TinCan are being set in motion, there can be no excusesabout the lack of value from learning analytics. Now the instructional designercan build content in a very specific way, to generate very specific sets ofdata that THEY determine are beneficial and valuable to the organizations theywork for.
Thetrick for new and old instructional designers is stop trying to “prove” thattraining and learning do x, y, and z. Instead, build a supporting case thattraining and learning were systematically part of the business achieving itsobjectives. In other words, we didn’t cause the business to achieve itsobjectives, but our data supports the system for helping the business achieveits objectives. You can do this by designing your interventions to work withinthe system (I’m not talking LMS, or any technology, for that matter) and generatingdata that’s important for the business.
Doyou need engaged employees? Generate real time data for engagement!
