Content comes in many formats and is created using many different tools and standards. At the end of the day you need to get from what you have to what you want, and regardless of what that is, it takes work to get there. In a world where cars drive themselves and merchants know what you want to buy before you do, you would think that technology could be used to lessen the load of, if not fully automate, many of the tasks involved in content transformation. The good news is that it can!
In a series of projects sponsored in part by the National Science Foundation and the Advanced Distributed Learning Initiative, many of the key steps in transforming and repurposing content have been automated through text analysis and machine learning. These steps include the ADDIE steps of analysis, design, development, and implementation, as well as document conversion and question generation. This session will share what was learned, what can be done, where the limitations lie, how these methods can be practically applied, and how they relate to the formats, standards, and techniques from other sessions in this Online Forum.
In this session, you will learn:
- How text analytics and machine learning apply to managing and repurposing learning content
- The key technical challenges in converting from informational content to interactive learning
- What types of standards can help reduce time and effort in content transformation workflows
- How automation can enhance the work and value of instructional designers and content developers
Intermediate designers, developers, managers, and CLOs.
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