Chapter 20: Stealth Assessment in Computer-Based Games to Support Learning

This chapter is about stealth assessment: what it is, why it’s needed, and how to accomplish it effectively. To make the ideas come alive, I provide two examples of stealth assessment in existing computer-based games. I end with my thoughts about challenges and next steps relating to this research stream.

The first time I formally used the term “stealth assessment” was in 2005, during an AERA symposium on diagnostic assessment. However, I had designed and employed stealth assessment about two decades prior to that, as part of a guided-discovery world called Smithtown (e.g., Shute & Glaser, 1990; Shute & Glaser, 1991; Shute, Glaser, & Raghavan, 1989). In Smithtown, students learned about principles of microeconomics (i.e., the laws of supply and demand) as they explored the simulated world, manipulated variables (e.g., the per capita income, population, price of coffee), tabulated and graphed data, and generated hypotheses about ensuing change(s) to other variables based on their manipulations. The system used artificial intelligence methods to monitor and analyze student performance data relative to their scientific inquiry skills and provided feedback to students that supported these skills. The idea was that improving students’ inquiry skills would subsequently improve their learning of the microeconomics content. A series of experiments supported the efficacy of this approach.