VR Will Blow The Lid Off Game-Based Learning

We speak to Elizabeth Owen, PhD and Director of Learning and Data Science at Age of Learning


Elizabeth Owen holds a PhD in Digital Media (School of Education, UW-Madison) focused in game-based learning analytics. Currently Director of Learning and Data Science at Age of Learning, she’s committed to leveraging data science to optimize adaptive, engaging learning systems. Previously a data scientist with GlassLab Games (EA), LRNG, and Metacog, her doctoral roots lie with Games+Learning+Society research and game lab. Collaborators include EA, Zynga, and Popcap games, and Dr. Ryan Baker at Columbia in ongoing Educational Data Mining. Before graduate school, Dr. Owen was a K-12 educator for a decade, and founding teacher at an LA charter school (LAAAE.org).

Elizabeth will be presenting at the Gaming Analytics Summit, taking place in San Francisco on April 28-29.

How did you get started in analytics?

In the last decade as an educator and learning scientist—as well a die-hard gamer—I quickly came to understand the power of serious games for learning. It became clear to me that in order to understand player experience and learning trajectories, and to unlock the adaptive potential of this medium, data science is absolutely critical. As a result, in doctoral work at UW-Madison and Games+Learning+Society, educational data mining in learning games became a core focus.

Are there any recent innovations in the gaming industry that you see as a game changer for analytics professionals?

Recent advancements in VR, as well as breakthroughs in sentient AI, provide field-changing opportunities for immersion in 3-D environments and highly adaptive agent-based instruction (like Stanford and Vanderbilt's science-based design foray[1] into "Betty's Brain", but on steroids). These affordances offer enormous potential, and exciting challenge, for data mining and user modeling within multi-dimensional interactive spaces and deep learning paradigms. Particularly with complex agent-based instruction, newly nuanced AI models hold great power for learner-adaptive personalization in play. Situate this in something as immersive as VR-based learning realms, and these innovations have the potential to blow the lid off of game-based learning as we know it.

What are the unique challenges facing you in your current role that you are looking to solve with analytics?

As Director of Learning and Data Science at Age of Learning, I enjoy being part of a fast-paced environment in which games of high production values are created, with deep design considerations of curricular rigor and optimal player adaptivity. Rigorous, scalable tracking of event-stream interactions—and modeling with cutting-edge methods in data science and educational data mining—are a vital part of optimizing this engaging, adaptive learner experience.

What will you be discussing in your presentation?

I'll be discussing the application of data science—specifically, data mining for educational games—that is not only designed to pick up on important play interactions (around user experience, engagement, and level design) but also can model hard-to-measure constructs of learning in real time. This application of data science represents educational data mining, an emergent discipline with intersections in cognitive science, artificial intelligence, educational psychology, and computer science. Here, the application is specifically tailored to educational game design, and methods are mapped to various core, alpha and beta stages of game development. The result is an arsenal of tools that enable intelligent optimization of engagement and learning in a user-adaptive play experience.

Your can hear more from Elizabeth, alongside other industry experts, at the Gaming Analytics Summit. Register for your pass here today.


[1]Biswas, G., Leelawong, K., Schwartz, D., Vye, N., & The Teachable Agents Group at Vanderbilt. (2005). Learning by teaching: A new agent paradigm for educational software.Applied Artificial Intelligence,19(3-4), 363-392.

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