How will the Internet of Things affect your training design?

The term Internet of Things (IoT) was coined by Kevin Ashton in 1999, according to Wikipedia. Some see it as a framework for a futuristic world where everything (including us) is connected across and through the Internet. I would argue that that futuristic world is here, with objects controlled and surfacing data over intercontinental distances. This is no longer science fiction, it is the stuff of situation comedies. While the IoT already affects many aspects of our lives, the world of business training has been slow to embrace its potential, unlike it siblings; the worlds of K-x level and higher education. Schools and universities are already utilising data that is fed onto the Internet from a  variety of non-human sources, as seen in the Technology Strategy Board project in schools and Kingston University medical research. Of course the business world has been leading the way on gathering information and using it to drive growth for some time, with approaches from the very basic loyalty card to more sophisticated in-store analytics. (My favourite application is the must have Wireless Key Locator).

But business training still lags behind. 

It is widely held that, the best training benefits from being scenario-based. I would imagine that a good number of instructional designers (me included) have spent many hours trying to develop believable and applicable training scenarios and the datasets that support exercises, to enhance our training materials. However, no matter how hard one tries there is always a feeling that…well…it’s not quite the real thing.

In our increasingly connected world we seem to have overlooked the potential of training materials that incorporate real-time information from the Internet. The IoT offers us information that has:

• Spatial existence (place)
• Temporal existence (time)
• Persistence (history)

These three features mean that the data that the IoT makes available can be anchored to a place and a time and that you can compare the current output to historical data. These are the three key elements of any data that we want to turn into information for a training course. (Clearly we need the data to be correct, but I will assume that…though I guess you could learn a lot from corrupt or inaccurate data).

This type of information package is usable across any training scenario, from the school children who study the requirements of plants to mining engineers who need to track and interpret geological activity. The logs from an IoT object—an under-watered plant or a mine-shaft—can transmit data across the Internet as a real-life, even real-time, training scenario or as a Performance Support source.

While this is clearly immensely useful in classroom and virtual or elearning environments, I would 

argue that it becomes even more intriguing when you look at mlearning. The student can become part of the online network that includes data, training content and learner…maybe even the instructor. We get a 360°, real-world, real-time environment where the student can benefit from actual business information, rather than a prepared dataset. I am not saying that prepared exercise materials don’t need to exist; without samples a student cannot gain the experience that will help them to interpret the real thing. The addition of IoT object input lets you take the next, natural step in training, especially where time-critical interpretation is a feature of expertise. You can take your students beyond the safety-netted world of a made up scenario to the more urgent one of real-time information.

Of course, the majority of real-time information is incredibly dull. Take your pulse, for example. You hope that it maintains a very boring rate of around 60 beats per minute. For medical students it would be unlikely that they would get real-time experience of all of the multitude of ECG outputs that may be the symptoms of an even wider range of health issues. However, if you have the ECG machine connected to your network as an IoT object you can alert students to an interesting cardiac output event when it happens. Clearly the students will not always be able to engage with the ongoing events, but the beauty of mlearning and IoT object logs is that you can maintain a history, so students who are not available in real-time can replay the event and experience it at a time that works for them. They can see what is happening; see the protocols for diagnosis and management unfold and even question or comment on what they are seeing, through social media.

Less dramatically, you may be working in finance or retail, where you could watch and interpret actual business activity, from the movement of stocks, through online exchange feeds, to the movement of stock, through RFID product tags. Have a think about how your students or staff could benefit from the simple introduction of real-time data from IoT objects in your training. 

One of the facets of business training that has, to some extent, hampered the development of IoT-driven scenarios is the structured nature of our tracking of Personal Development. The learning management system (LMS) is on our network rather than the Internet and we manage all training events through it. If your training is going to embrace the immediacy of IoT object data, you need greater flexibility in tracking training events. It may be that the Experience API and next generation LMSs may enable real IoT integration and propel business training to the next level.