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.

How Do We Ensure That Students Remember What We Teach Them? (Part 3 of 3)

Minimizing the Forgetting Curve and Improving Learner Retention (2)

Forgetting, or more correctly minimising the rate at which we forget, is a function of several facets of learning. To maximise retention your design should:

• Ensure the student is engaged by the subject matter.
• Know what you want the student to remember.
• Design in-built course motivation.
• Create cohesive, linked content.
• Design to use a range of content delivery styles.
• Provide pre-course preparatory content.
• Enable discovery learning.
• Provide effective knowledge checks.
• Provide post-course performance support content.

The first points were covered in my previous blog, How Do We Ensure That Students Remember What We Teach Them? (Part 2 of 3).   

Pre-Learning Preparation

The Ebbinghaus approach to memory retention analysis was based on new and random information, which enabled him to abstract the tests from as much pre-learned information as possible. Had he used three letter sets that were all common words, such as CAT or DOG rather than GIW or QOH (he didn’t publish the exact letter sets that he used), he would have found it far easier to memorise the sets. This is because the memory will fix on familiar patterns or create picture mnemonics to aid memory. 

In most circumstances, you need to employ the reverse methodology, by incorporating pre-learning preparatory content for students, both in classroom or digital learning environments. You have to determine what a student needs know to get the most benefit from the upcoming training. Do not fall into the trap of just sending out the complete course prior to the training event. This can produce two unhelpful results; the student doesn’t bother to do the prerequisite learning because they know it will be covered in the training, or the student will go through the course and be bored by the repetition.

I would recommend that you tell the students that there will be a review of the pre-course content and then make sure that it is reviewed. This may be a discussion or a set of review questions. There will always be some students who do not complete the preparation (for legitimate or other reasons) but a kick-off review will ensure that this group will prepare thoroughly for your training in future.

 

Enable Personal Discovery

Discovery learning is an effective way to help students to retain what they learn through, perhaps because of the enhanced strength of episodic memories as discussed in A Theoretical Foundation for Discovery Learning (Svinicki, 1998); of course you have to make sure that they discover what you want them to learn as part of this. The search for a solution creates an enriched experience for the learner and will improve the level of retention by increasing the level of engagement and creating memory links around the topic.  In a moderated environment, such as a classroom or virtual learning (vlearning) environment, this is managed by a teacher or facilitator. In such a managed situation, the teacher can use spontaneous events to trigger such research. 

In a digital environment the use of discovery learning has to be driven by the student, but you must insert these opportunities into your design. The Internet is, to all intents and purposes, an unlimited source of discovery. Where possible, you should include research options in your training content; “Click here to find out more about …” or “You may want to look on the Web for more information on this. A good place to start is …” This approach has inherent dangers in self-study world of elearning, as the student may find browsing the Internet more intriguing than completing your course. Here are some practical options for mitigating this danger:

• Your links should open a browser that does not include an address bar, so that students are not encouraged to wander off on their own.
• Your links should always open in a separate window, so that the training materials remain on screen.
• You should suggested a time limit or start time outside the current training: “You may want to spend five minutes reviewing …” or “When you have finished this section, try looking at …”

 

Provide Assessment and Knowledge Check Feedback

Assessment is important in any measurement of knowledge retention; if you don’t ask questions about the learning how will you know how much has been retained and for how long? Assessment can vary from in-course knowledge checks to accreditation or certification exams, where answers are tracked and maintained in a learning management system (LMS). In this section I will concentrate on knowledge checks, as these are designed to encourage, rather than test, knowledge retention.

We can differentiate between moderated training and self-study. In a moderated environment we must assume that the teacher provides on-going assessment through questioning and discussion. In an instructor-led course, you should include suggested questions or discussion topics and key learning points to ensure that the students are assessed on the information that you have identified as being key to the learning.

In the self-study world the onus for assessment is on you, the course designer. You should include knowledge checks, in the form of questions, social media discussion topics, or games in all training. Without these it is harder for the student to assess their own understanding and retention of the content.

You should always provide feedback on any question or task, as it provides reinforcement and an opportunity to review content. When a student does not provide the required answer I recommend that you tell them it is not correct, tell them the correct answer and identify where in your training they can find the information necessary to answer the question. If possible, provide a link to the page or section where they can review the content. If a student gives a correct answer you must also provide feedback. “That’s right. Well done” is fine, but you can reinforce learning by providing some additional information on the question topic or a link to some external content related to the topic. The latter provides an implicit reward—a sort of “You have done so well that you are ready to do some discovery learning on the topic”.

 

Provide Post-Event Learning

Many practitioners suggest that to avoid the dangers of the Forgetting Curve you should provide revision sessions or additional information to keeping the content in memory. While this may work neatly for remembering a series of random letter sets in a research study, it is less applicable for the world of training. Realistically, can you see a student reviewing everything from a training event with this regularity? Even if they did, do you really imagine that this will maintain 95% recall for anything but the most basic of knowledge? 

It is a good idea to provide post-course reinforcement to encourage knowledge retention. When you do this, you have to make sure that it is attractive to students, as there is seldom an on-going element of compulsion in attending post-training events. To encourage continued learning and reinforcement you can continue to provide additional revision content or you get the students to interact with each other about the topic. The former will require effort from you, while the latter is not as easy to track, so you cannot be certain of success. 

If you decide to create additional materials—and remember that just providing the same materials that were in the training is almost certain to be ignored by students—you must ensure that they focus on the key learning requirements, but with the flexibility to encourage additional research. The research has to be challenging but achievable, if there is no instructor to support student efforts.

Providing a social, learning community has the advantage of being self-sustaining. You can do something as simple as creating a common Twitter hash tag or a Pinterest or Google+ community and let it flourish or wither as the students see fit. The outcome of this approach is very much dependent on the students’ attitude towards such environments.

The third option is to provide a moderated revision area, where an instructor is present, synchronously or asynchronously, to help and advise the students. This is possible in the sphere of education (universities and colleges) but it can be expensive for businesses, though it has potential to encourage an acceptance of a life-long learning philosophy.

 

And in conclusion…

Retention is not simply a matter of telling students the same thing on a recurring basis, because being told the same thing every day is dull. You need to inspire your students by making your learning content:

• Applicable to their working lives.
• Focused on what you need them to know.
• Linkable, so that the students mind can build complex information into a manageable and memorable story.
• Multi-modal.
• Part of a learning and subject continuum—pre- and post-event.
• Motivating.
• Extensible through their own effort.
• Supported by checks and feedback.