406 Building Better VR Solutions with Learning Engineering Practices
10:45 AM - 11:45 AM PT
Thursday, October 27
Virtual reality (VR) is becoming an increasingly popular medium for delivering learning experiences, yet there has been little defined in terms of solid frameworks and best practices for immersive learning experience design. This can make designing VR learning solutions feel like a shot in the dark, hoping you won’t miss the mark fulfilling learning objectives and achieving learning efficacy. This is where learning engineering processes can help us. We don’t need to guess whether our design is sound or leads to favorable outcomes—data can tell us about our design’s effectiveness. Learning engineering helps us build better VR learning experiences.
This session will equip you with the knowledge you’ll need to implement learning engineering practices and processes to design and measure learning within your virtual reality solutions. We’ll discuss using the cycle of questioning, collecting data, deriving insights, and applying our findings to our models and solutions to further improve conditions of learning. You’ll learn how you can apply learning science research to guide decisions pertaining to learning design, interaction design, measurement methodology, and the overall immersive experience of your VR solution through the lens of several VR use cases. You’ll discover how modeling helps to inform design choices and we’ll unpack examples of various models that you can apply to your immersive learning design, including the learner model, domain model, and activity model. We’ll discuss how learning engineers employ instrumentation to collect data to test their hypotheses and assumptions, which can then be analyzed and interpreted to shed light on their research and design questions. By instrumenting and analyzing data, you’ll be able to discover what about your VR solution works, why it works, identify critical issues, develop targeted solutions, and most importantly, scale what works through iterative, evidence-based design.
In this session, you will learn:
- What learning engineering is, the processes and practices involved, and how they can be applied to the design and iterative development of VR learning solutions
- How you can apply learning science research and human-centered engineering design methodologies to the design and development of your VR learning solutions
- How you can use modeling (knowledge domain, activity, and learner) to craft a comprehensive view of the learner and their environment, which helps set the foundation for initial design and subsequent data-informed iterations of your VR learning experience
- How you can use instrumentation to gain insights about learners, learning models, instructional scaffolding, the immersive experience, and product efficacy to make data-driven decisions to improve your learning design
Head of Learning Engineering
Kristin Torrence serves as the head of learning engineering at Talespin, where she focuses on applying learning sciences, instructional design, and data science practices to design, instrument, and validate XR learning solutions. Her background is in cognitive science, game-based learning, and instructional design and she is particularly interested in the intersection of learning science, XR, and learning analytics. She co-founded XR in LXD, a meetup and community of practice for IDs/LXDs interested in designing XR, and she is an active member of XR Women and the IEEE Industry Consortium on Learning Engineering (ICICLE) Design SIG and Tools SIG.