Exploring Data Analytics in Mixed Reality Simulations to Measure Teacher Responsiveness

Abstract

A growing body of research begins to illustrate how mixed reality simulation (MRS) based on digital puppeteering (e.g., Mursion) may be used to provide practice-based opportunities in teacher education. Ironically, current research of this new technology often uses historic measures and conventional data analytics to measure teacher learning, such as holistic rubrics of qualities that describe an average or overall teacher performance or frequency counts of teaching behaviors. What is missing from the literature are novel approaches to measures, data collection and analyses that leverage the digital data available through MRS to explore new dynamic and responsive measures of teaching. For example, measurement of teacher growth could shift from focusing on teacher performance and behaviors to measuring teacher responsiveness to student variances. Rather than just measuring the extent to which a teacher can implement a specific teaching practice, researchers could examine the extent that the teacher adapted the teaching practice or selected appropriate teaching strategies based on qualities perceived in student responses. Now more than ever, we need innovation in teaching, especially developing teacher capacity to perceive and respond to student diversity in real time as learning unfolds. This study explored possible MRS measures and data analytics that examine teaching as a dynamic process responsive to student diversity. We found that specific elements of simulation design and implementation can generate data that measures indicators of teacher responsiveness to student variance.