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Full Description
Add Data and Analytics to Your TD Toolkit
Instructional design pro Megan Torrance addresses the importance of instructional designers accessing and applying learning and performance data—from how to design learning experiences with data collection in mind to how to use the data to improve and evaluate those experiences.
With the advance of new learning technologies and data specifications, instructional designers have access to more and richer data sources than ever before. With that comes the question of what to do with the data. While most data and analytics books focus on their application for measurement and evaluation and assume a prior baseline understanding of what learning data and analytics mean, Data and Analytics for Instructional Designers delves into the foundational concepts that will enable instructional designers and L&D professionals to use data in their roles.
Split into two parts, the book first defines key data and analytics terms, data specifications, learning metrics, and statistical concepts. It then lays out a framework for using learning data for planning how to gather data and to building scale and maturity in your data operations. Megan reassures readers that basic math skills with some computer assistance is what you'll need to get going. So set aside any math anxiety!
Through a "If I can see it, I can be it" approach to learning data and analytics, the book blends practical what-is and how-to content with real-world examples and longer case studies from practitioners. Chapters conclude with opportunities for you to put these techniques to work right away, whether you are in a data-rich environment already, or whether you are just getting started and working on hypotheticals.
Contents
Introduction
Part 1. The Foundations
1. Why Should Instructional Designers Care?
2. Getting Started With Definitions
3. Data Specifications in Workplace Learning
4. Unique Learning Metrics
5. A Little Bit of Statistics
Part 2. Designing for Data
6. A Framework for Using Learning Data
7. Make a Plan for Gathering and Using Data
8. Form Your Hypothesis and Specify Data Needs
9. Identify Data Sources
10. Build in Data Capture
11. Store the Data
12. Iterate on the Data and the Analysis
13. Communicate and Visualize the Data
14. Build Scale and Maturity
Acknowledgments
Resources and References
Index
About the Author
