If 70 percent of all communication is nonverbal, then training managers must pay close attention to the subtle cues that come from expressions and body language. When classroom training was the only option, this was fairly easy to do. Slumped shoulders, a downward gaze, or the “deer in headlights” look were all cues that people weren’t paying attention or didn’t understand the tenets being discussed. Instructors who are adept at noticing such cues could adapt the material or ask students for feedback on the spot.
When training began to splinter into other formats, such as live Web training and on-demand e-learning programs, gathering that nonverbal feedback became almost impossible. Trainers in a live Web training can ask participants for their feedback, and through an online tool, participants may choose to share their thoughts in real time. Yet that’s not nonverbal—and people don’t always share exactly what they’re thinking in writing for fear of being judged. Users of e-learning or on-demand learning can share feedback through quizzes within the course and through surveys and ad hoc conversations afterward. Yet such feedback still lacks the honest, spontaneous feelings that emerge at the point of learning: a smile, laugh, a nod, or a grimace.
This is one drawback of online learning, despite its convenience, flexibility, scalability, and affordability: User feedback is incomplete. Those nonverbal messages are important for improving courses and affirming that something is working. As a result, developers of online courses and software have been working diligently the last few years to solve the problem. One answer is embedded technology that can measure and report on non-verbal feedback through analyzing facial expressions and physical movements.
Analyzing Micro-Expressions
The technology behind this involves facial expression tracking, and it’s already being used in industries such as digital advertising and customer research. Companies such as Affectiva, Sension and Emotient are a few pioneers developing and applying it to analyze consumer engagement with advertising content and products. Facial tracking tools analyze more than 100 micro-expressions and then categorize those into a wide range of emotions, such as joy, frustration, sadness, disgust, boredom, and curiosity. This technology also now is being used in online learning: If a student looks away from the screen for longer than several seconds, a course will pause and restart the content when the individual reconnects her gaze with the screen. This helps learners not miss critical information due to distractions.
Additionally, facial tracking feedback helps trainers improve courses. Measured in aggregate by compiling the results of many participants, a trainer could research whether a particular slide was ineffective, if, say, 80 percent of learners looked away or registered a negative emotion while viewing it. Combining that information with quiz scores, surveys, and in-person conversations, a trainer might decide to change the content and then look at the data again to see if the change registered higher engagement levels or more positive emotions.
Getting Beyond Big Brother
A common concern with tracking technology is the question of privacy. Employees may balk at the possibility that that they are taking a course at home, in their pajamas, and somebody back at corporate headquarters is reviewing it later. They don’t want to feel on the hook for every moment they look away from the screen during a course or if they are caught scowling. Yet the point of facial tracking technology for online learning is not to analyze individual responses and then punish people as a result. The point is to collect and review aggregate, anonymous data to ascertain big picture trends. Individual learning styles can differ greatly by gender, personality, and even culture. One person who’s looking away from the screen is disengaged, while someone else actually is focusing intently. To avoid backlash about “big brother” tracking, it’s wise to not record individual sessions and only collect data across a population. While companies must make the call on how to use the technology, it’s important to realize the ramifications of individual tracking, namely, the likelihood of employee distrust of online training and their resistance to using it.
There are other hurdles to overcome still until we see widespread adoption of tracking technology in online learning. Accuracy improves with more data and it’s early days yet, even for the pioneers in this field. Accuracy also may be hindered by environmental conditions, such as a low light situation in somebody’s home office. Then there are the valid challenges of addressing ethnic and cultural differences. The online training industry must incorporate variations in expressions from country to country. A raised eyebrow in one region may mean something entirely different in another.
Yet the long-term outlook for integrating nonverbal feedback into online training is much more positive than negative. It has the potential to close the gap between classrooms and online learning. With far fewer barriers for online learning, we expect that the technology will become more mainstream so companies are able to support greater education opportunities for their workforce. Skills and knowledge are, after all, driving competitive advantage in the digital economy. Smaller companies, too, will benefit. Sophisticated nonverbal analysis was in the past something a large company possibly could outsource to a specialized training company. Today, these capabilities are available for any sized company, thanks to the accessibility and affordability of cloud-based online learning platforms.
Randhir Vieira is vice president of Product and Marketing at Mindflash, a leader in online business training.
