Add Data and Analytics to Your TD ToolkitInstructional 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.

IntroductionPart 1. The Foundations1. Why Should Instructional Designers Care?2. Getting Started With Definitions3. Data Specifications in Workplace Learning4. Unique Learning Metrics5. A Little Bit of StatisticsPart 2. Designing for Data6. A Framework for Using Learning Data7. Make a Plan for Gathering and Using Data8. Form Your Hypothesis and Specify Data Needs9. Identify Data Sources10. Build in Data Capture11. Store the Data12. Iterate on the Data and the Analysis13. Communicate and Visualize the Data14. Build Scale and MaturityAcknowledgmentsResources and ReferencesIndexAbout the Author