rockidscience.com Instructional Design Basics

7Jul/13Off

Don’t Make Me Wait

waiting2

My driving routes to work and other places may appear to be random and half-hazard but this isn’t the case.  In fact these routes are often carefully setup to minimize the likelihood of traffic.  And though these routes sometimes result in longer commutes they typically lessen the amount of time spent in traffic.  This is good as waiting around drives me crazy.

Besides driving, this is a trait that is true in other areas and unfortunately for me, waiting, is a common occurrence with learning technologies. Often I have to wait on technologies to converge, infrastructures to get in place and for appropriate projects to emerge before I get to explore.

Today I’ll talk about a frustratingly long wait with mobile learning or mlearning.

You Got a Problem with Me?

Several years ago I wrote a post on issues that needed to be addressed before mobile could take off as a learning platform. For the most part those problems have been addressed but we still haven’t seen this platform really take off.

Part of this has to do with an early view of what mlearning should be.  In this regard, there was a an assumption that mlearning would be similar to elearning, that is, its content would be much like your typical online course, but just on a smaller screen.   If this assumption was true, then mlearning as a platform would have already taken off.

This hasn’t happened though as our assumptions about mlearning have evolved into something much more powerful.  Here learning professionals are looking at mobile technology in another way—they are focusing on what this technology brings to the table that the other platforms don’t and what they have found is context.

We’ve come to realize that these devices are able to determine where you are and what you are doing. In addition these devices have the computing and networking power to act on this contextual information. Here designers can use push/pull measures to deliver content that is related to a person’s immediate and future needs.

Context in this regard is an incredible performance enhancing tool, but unfortunately for me, it’s what has kept us waiting with this technology. The following table outlines some of the remaining issues with mlearning:

 

MPSStable

Where it’s At

As can be seen there are some significant barriers remaining, but that doesn’t mean it’s all bad.  In this regard there are some existing opportunities for mobile technology—two common strategies are:

Expert Access

Designers have seen the communication possibilities that are inherent in these technologies and are starting to use it as a pull tool.  Here by taking advantage of the voice, text, and video conferencing abilities that these devices offer, novice users can easily access experts in the field for questions, feedback and advice.

Designers are also looking at the push opportunities that these technologies represent—here they are using these devices to support reinforcement and practice activities. Such strategies are important as they will aid transfer and internalization of content.  In this area, having experts use curation tools can offer tremendous power as these tools can extend your learning activities.

Just-in-time Learning

Job-aids and handouts have always been popular tools to give to our students.  These tools are useful as they support our students in their working worlds.  Here when they need to perform their job, a student can easily pull out a job-aid or reference material and use it to complete the task at hand.

Designers have realized that mobile technologies can allow them to create more robust job-aids and reference materials.  And instead of simple procedural guides, checklists and handouts, we can now use Mobile Apps and ePublications to include video, augmented reality (AR) and other strategies to provide greater clarity for our students.  We can also act on the data that is inputted into these devices—such use can greatly impact ROI by increasing work productivity and decreasing error rates with some tasks.

Where it’s Going

All of this has me excited and looking for appropriate projects to explore; however, my real excitement comes when looking at the future.  The convergence of AR, wearable devices and connected profile information will create some intriguing metacognition tools.

In this regard, subject experts excel in their areas because they can apply more advanced metacognitive abilities in their field.  This allows them to focus on more stimuli as they work which in turn, also allows them to diagnose and evaluate their progress in deeper ways.  Soon these mobile tools will provide novice users with these abilities—here interfaces will allow them to focus on more things and provide them with more information on their progress.

The following video illustrates this as it demonstrates a possible Google Glass App. Today, expert runners are attuned to conditions (temperature, heartbeat, wind, pace, altitude,…) that novice runners are not. GhostRunner creates an interface that provides this information to all runners. This interface will alert runners to conditions that might impede their run and give them valuable feedback on how they are doing.

Such tools are going to offer amazing opportunities to improve performance and even though it means I have to wait a bit I’m OK with it—I hope you can stand the wait too.

GhostRunner Demo from OnTheGo Platforms on Vimeo.

Comments (2) Trackbacks (0)
  1. Wow! GhostRunner is cool.
    Watching the demo helped me to see how this next step of metacognition will allow learners to diagnose and evaluate their progress in deeper ways. Great example.

    I am willing to wait, as long as you keep up your analysis to keep people like me informed of what is happening on the mLearning front! Thanks, Steven!

  2. Here is a timely article talking about another remote access opportunity:

    http://mashable.com/2013/07/10/university-white-space-wifi/


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