A Glimpse at Experiential Learning

June 20th, 2013 : By Chris Legarski :

There are two guys in a bar… Do I have your attention? GOOD! because this is no joke, but rather a thought provoking look at the learning that occurs through a simple conversation. In this post, you are encouraged to watch a video about “two guys in a bar” who argue whether or not one can learn French by simply making sounds with his mouth and somehow learning it. While watching this video, I couldn’t help but notice the flow of dialogue and how closely it resembles a process described by David Kolb as experiential learning. The video, although funny through the eyes of a person simply looking to be entertained, can be a representation of a learning process of someone using active experimentation to acquire new knowledge, improving upon that knowledge, and storing it as something new.

In this example, we see what some people refer to as the “standard” or “traditional” way of learning (chalk and talk) can be transcended by one’s ability to initiate a hand’s-on, learner driven approach to acquire new knowledge in a domain he has no prior knowledge about. So, with that said, I encourage you to watch the following video and reflect upon what you have just read. Please pay attention to the dialogue and see if you can pick out the specific interactions between the two characters that support the tasks in Kolb’s experiential learning diagram below.



The following relies on the assumption that you have watched the video:

We can see that the learning process was initiated by active experimentation when Character A made “French-like” sounds, expecting a response from Character B (Active Experimentation). The response from Character B touches on the next two phases of Kolb’s model. There is both rebuttal and a sense of confusion from Character B (Concrete Experience). To explain, Character B informs Character A that what he just said was not French and basically tries to make Character A feel like a fool for even thinking that it was French. Character A watches Character B’s body language which clearly suggests annoyance (Reflective Observation). It is at this point that Character A is able to think about and understand the non verbal and verbal information he received from Character B (Abstract Conceptualization). He acts on his understanding of the situation and the cycle repeats itself again with similar results. At the risk of ending Character B’s involvement of the conversation, Character A again speaks French-like gibberish a third time asking Character B to listen more carefully. This created a different reaction from Character B which moved in favor of Character A learning something. When Character B said that the last thing sounded similar to a French word, the two entered into a dialogue where the learner asked a series of questions and received the French word for mushroom. When he asked for clarification, Character B said, “Oui. That means yes.” Note the reaction of Character A to this knowledge. He just proved to Character B that his method worked. Not only did he learn a new word through his own experimentation, but he also caused the other party to volunteer new knowledge. Simply by having a conversation, Character A was able to learn some French.

So, how does this relate to Discovery Machine Inc.? Well, we are currently in the process of building systems that facilitate experiential learning by creating an interactive experience between human and computer similar to the interaction you just witnessed between the two people in this video. Using DMI’s methodology, we can capture knowledge and deploy it in the form of an intelligent agent. We can model this agent so that it interacts with the user in a way that promotes co-constructive, student directed learning with an interactive system allowing the learner to play more of an active role in the learning process.

For more information about intelligent performance support tools, please see my last post about Learner Centered Training Using Artificial Intelligence Driven Performance Support Systems.

You can also contact Discovery Machine at www.discoverymachine.com or call us at (570) 329-5661.

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