801 “Did it Work?”—Data Strategy for Instructional Design
1:00 PM - 2:00 PM Thursday, February 18
Instructional designers expend great effort in order to design effective courses but after the course goes live, the next logical question is: Did it work? In order to improve course design, it is essential to understand specifically what, how, and why the course did or did not work; which is where data comes into play. Data is available from a number of sources, providing insights far beyond what is available from your LMS, but it will only offer benefits if you acquire meaningful data and understand how to improve instructional design based on that data.
In this session you will explore data strategy for instructional design, which includes understanding the data supply chain, evaluating the available data streams for their relative value, and the basics of data stewardship. You will learn how to design to gather meaningful data within the context of course goals and overall performance objectives, as well as how to use quantitative and qualitative data to improve course design. Additionally, you will learn the potential pitfalls of data interpretation.
Sean Putman
Vice President of Learning Development
Altair Engineering
Sean Putman, a partner in Learning Ninjas, has been an instructor, instructional designer, and developer for over 15 years. He has spent his career designing and developing training programs, both instructor-led and online, for many different industries, but he has had a strong focus on creating material for software companies. Sean has spent the last few years focusing on the use and deployment of the Experience API (xAPI) and its effect on learning interventions. He has spoken at industry conferences on the subject and is co-author of Investigating Performance, a book on using the Experience API and analytics to improve performance.
Janet Laane Effron
Managing Principal
Four Rivers Group
Janet Laane Effron is a data scientist who focuses on the creation of effective learning experiences through iterative processes, data-driven feedback loops, and the application of best practices in instructional design. She has worked on xAPI design projects related to designing for performance outcomes and designing both for and in response to data and analytics. Janet’s areas of interest include text analytics, machine learning, and process improvement. She is also the co-author of Investigating Performance: Design and Outcomes with xAPI.