ver the last year-or-so, some of the things that I’ve been doing is exploring instructional design and its application to online learning with a particular focus to gamification. That is, how can games and game-based approaches make us excited about learning?
This stuff is so cool. Why? So many LMS have reporting systems that aren’t really conducive to manipulation (or even sometimes solid analysis). They tend to be passive and, if they don’t user Tin Can/xAPI, provide data that is based more on results rather than process. And when it comes to ID, the process can be really important. It not only gives you the data about what is successful and unsuccessful in terms of learning objects and pathways, but it also allows you to assess learners along multiple axes.
I’m hoping that I can explore some of these in the new gig as ID over at The George Washington University’s College of Professional Services.
In the following, I’m going to outline some thoughts as they pertain to the new position and the program that I’ve been informed that I’ll be on (dipping into the ‘ole forensic archaeology background).
Branching Learning Pathways & Start States
Branching learning scenarios are an effective way of bringing real-world relevance into the learning environment. Creating a single module to encompass all of the different branches of a complex module can, however, be daunting. (That’s an understatement.) So many variables to keep a track of, assumptions about the length of interaction, and so forth.
This is where the expanded desire for analytics on the web, and in online learning, can be rather useful. One of the reasons that learning materials that offer highly-individualised learning pathways are so expensive is that they tend to be biiiig projects—not just in terms of their scope, but the fact that they have so many moving parts it ultimately comes down to integrating them into a single package.
So, other than that, what advantages does this give. Well, here are some thoughts.
With user variables accessible remotely and brought into the module for each user, the pathway taken for learning is less relevant. This means that you can get away from illusionist or “on rails” scenarios where the learner progresses from A to B to C to D etc. At the very best you’re replicating the ‘ole Fighting Fantasy books of yesteryear with metaphorical thumbs stuck in all the pages.
What non-linear learning means is that you can choose any particular avenue and, depending on the design, you can progress through modules without necessarily having all the data to do so effectively.
“Wait!” I hear you cry, “If the learner can progress through a module without having the full information at their hands, then it cannot be an effective learning experience!”
I hear you. There are, however, many solutions to this depending on the modality of the course: synchronous, asynchronous, and blended. Yet we must remember that while the idea that moving the learner linearly in an “on rails” experience might give them information, it doesn’t inherently give them understanding. And this is where non-linear experiences can shine. Failure is not a bad thing if you give appropriate feedback and, in scenario-based learning, give them their Groundhog Day moment.
While µ-learning is mentioned below, non-linearity also has some interesting implications when it comes to µ-learning in general. Those drip-fed courses? Well, it doesn’t matter what order they’re taken in and, indeed, non-standard orders can be fed into level-based notions.
Virtual Worlds & Crime Scene Investigation
Complex environmental analysis within traditional e-learning environments tends to be limited by the sheer complexity of even simple crime scenes. Similarly, virtual worlds tend to be independent creations unless you throw a lot of money and time at them. Indeed, while I’ve been having oodles of fun working on the Enchanted Castle recreation and virtual world, I’ve also been thinking under what situations could they—and the related virtual reality—be used effectively in e-learning. Sure, they’re sexy and all, but if they cannot be employed in a substantive learning environment, that’s all they are.
Image © Michael Harrison
Yet if we are to make learning relevant, such as with scenario-based learning, there are certain opportunities. Consider, for example, the use of SketchUp in crime scene analysis. Simple and freely-available 3d software allows LEO’s to model the complex data-sphere of the crime scene, and also to present a version of it to juries in criminal prosecutions.
Rather than just an independent resource that has “wow factor,” you now have something that can be used in interesting ways. Are certain bits of evidence identified in the crime scene? If so, change a variable’s value in the database and draw from that for the purposes of scoring or for using gamification and “levels” to assess what kind of questions to ask the learner.
There are some technical and security questions to ask, but it does at least show that VR-based learning can be integrated into more traditional e-learning and blended environments.
Micro-Learning & Crime Scene Investigation
The ability to break up learning into smaller sections that are easily digestible can be a great thing. But just as with breaking up complex branching learning scenarios can be really useful to bring down production complexity and cost, it can also work with scenarios where, say, you’re dealing with different types of evidence—stratigraphic/chronological, blood spatter, DNA, and so forth. Even something like grid-based survey of the crime scene and its subsequent interpretation can benefit from smaller learning objects and data that can be stored in a database.
As an example, the other day I was getting my (not-so) inner nerd on in watching the latest Bladerunner film. What this did was remind me of not only the original film, but also the subsequent video game. Both utilised the analysis of an image in really creative ways. Here’s the section from the film on Youtube.com.
You can imagine a number of scenarios here:
- Each micro-learning “image” is sent out and contains multiple bits of evidence that needs to be correctly identified. This then alters the start characteristics of another module that is predicated upon the “fog of war” premise (here referencing the video game version of von Clausewitz’s original creation). For example, the image of a suspect might become more in focus with the more bits of evidence that are correctly identified, or the chances of successful prosecution increased because of the maintenance of the chain of evidence. (Really, there are oodles of possibilities here.)
- Different disciplines that contribute to forensic examination of the crime scene might need to be identified and subsequently imported into a final scene of crime module that allows for the integration of that data.
- Multiple crime scenes that are used to identify a single perpetrator (e.g., serial crimes).
The above works for different types investigation: from what one might consider more “traditional” crime scenes, to cybercrimes, forensic accounting etc. It’s really about setting the scene and breaking down the narrative.