Decision trees for contextual learning
When in a real life situation questions do not come in isolation and events unfurl in relation to your actions. Knowledge by rote only has a certain level of usefulness in this sense. To engage learners to apply knowledge we can use case based learning so they can further understand the context of decision making.
Example of and Action Maze embedded in Wimba
For this we can use a programme such as Quandary. Quandary is a tool for creating Web-based Action Mazes. This Action Maze is a concept to deliver simulated environments via text through a case study. The answers to one question directly effects the next question asked. This branching tree is known as an Action Maze. These can be used to deal with problem-solving, diagnosis, procedural training etc . If you wish to use Quandary it is free to use but if you are using a University of Bristol console you will need administrative access to download it.
The software for Quandary can be found here - http://www.halfbakedsoftware.com/quandary.php
In this section we will be:
- Introducing the Quandary decision tree system
- Showing how to embed this content into your Wimba Output
