Data and Analytics for Instructional Designers

Megan Torrance is CEO and founder of TorranceLearning, which helps organizations connect learning strategy to design, development, data, and ultimately performance. Megan has more than 25 years of experience in learning design, deployment, and consulting. Megan and the TorranceLearning team are passionate about sharing what works in learning, so they devote considerable time to teaching and sharing about Agile project management for learning experience design and the xAPI. TorranceLearning hosts the xAPI Learning Cohort, a free, virtual 12-week learning-by-doing opportunity where teams form on the fly and create proof-of-concept xAPI projects.Megan is the author of ATD books (Agile for Instructional Designers, The Quick Guide to LLAMA), and two ATD TD at Work publications (Agile and LLAMA for ISD Project Management and Making Sense of xAPI). She is a frequent speaker at conferences nationwide. TorranceLearning projects have won several Brandon Hall Group awards, the 2014 xAPI Hyperdrive contest at DevLearn, and back-to-back Learning Guild DemoFest Best-in-Show awards in 2016/2017 with xAPI projects. TorranceLearning is a 2018 Michigan 50 Companies to Watch.A graduate of Cornell University with a degree in communication and an MBA, and an eCornell Facilitator in the Women’s leadership curriculum, Megan lives and works near Ann Arbor, Michigan.

“Through real world stories and easy to understand examples,Data and Analytics for Instructional Designers answers why L&Dprofessionals should care about data instrumentation and analytics, and how to leveragedata in their work. It covers foundational concepts and provides a practicalframework for using workplace learning data. This books is a timely resourcethat removes the mystique around data-informed decision-making in learning anddevelopment. Every L&D professional should read this book.”—Jim Goodell, Editor, Learning Engineering Toolkit; Chair,the IEEE Learning Technology Standards Committee“Data and Analytics for Instructional Designers is a unique and important titlefor talent development professionals to understand the tidal shift under way inthe industry. Megan's depth of experience and expertise in the space isunparalleled, and her readers will benefit from this experience and learn fromher work and detailed examples shared here. Megan has created an important bookfor the learning and development community with Data and Analytics forInstructional Designers. The dawn of big data in learning is here, and thisbook will prepare you and your team for the new tools, processes, and businessstrategies that accompany it.”—Chad Udell, Chief Strategy Officer, Float and SparkLearn “Approachable and insightful! Data and Analytics forInstructional Designers reads like a conversation with your smartest friend,and when you finish, you’ll have the confidence and know-how to begin usingthese powerful tools in practice.”—Sae Schatz, PhD, Co-Founder of Bedrock Learning; FormerDirector, Defense Department’s Advanced Distributed Learning (ADL) Initiative “Today's informational revolution demands that instructionaldesigners are more versatile than ever before. As a very early practitioner ofemerging learning analytics technologies, I am often asked ‘Where do I start?’Right here, with this book, is now my go-to answer."—Brian Floyd, Learning Architect "A practical guide for L&D professionals that arelooking for approaches to leverage analytics from learning and training data,supported with real-world stories to help guide success. This book pullstogether years of blogs, case studies, and project outcomes to help providestructure around how to use xAPI."—Chris Tompkins, Director of Sales, Rustici Software

• 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