Abstract Details

Personalized Learning In Games Through Data-Driven Design

Personalized learning systems can offer individualized instruction through just-in-time feedback and adaptive recommendations tailored to each student. Paired with the immersion of digital learning games (e.g. Gee, 2005; Abdul Jabbar & Felicia, 2015), these systems can impact learning at scale both inside and outside the classroom. One recent example is the My Math Academy (MMA) app, a system of adaptive learning games designed to help pre-K to second grade children build foundational number sense. Since ongoing understanding of student progress is key to adapting to student needs, MMA personalized learning is driven by principles of Evidence Centered Design (ECD; Behrens et al., 2010) with formative assessment supporting individualized pathways to learning. This talk presents a view into MMA as a data-driven system, which integrates evidence-based design foundations, clear event-stream student data, and learning analytics (e.g. Owen & Baker, 2019). This alignment of strong learning design, assessment seamlessly embedded into the games, and a clear data stream of student performance can fuel a powerful learning experience. It also allows learning analytics to run under the hood to provide customized lesson pathways, and investigations (e.g. prediction and behavior detection) that give insight into diverse learning trajectories-informing iterative, data-driven design and personalization within an engaging learning system.



References


  • Abdul Jabbar, A. I., & Felicia, P. (2015). Gameplay engagement and learning in game-based learning: A systematic review. Review of Educational Research, 85(4), 740-779.
  • Behrens, J. T., Mislevy, R. J., DiCerbo, K. E., & Levy, R. (2010). An evidence centered design for learning and assessment in the digital world. CRESST (UCLA).
  • Gee, J. P. (2005). Learning by design: Good video games as learning machines. E-Learning and Digital Media, 2(1), 5-16.
  • Owen, V. E., & Baker, R. S. (2019). Learning Analytics for Games. In J. L. Plass, R. Meyer, & B. D. Homer (Eds.), Handbook of Game-Based Learning (pp. 513-535). MIT Press.

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