The end of testing.
But the solution may be self-generating. While both stealth assessments and GPS systems must start from an initial map, they also share another critical capability: the potential to become more accurate over time. As GPS software records millions of data points on destinations and routes, it begins to detect otherwise unknown traffic patterns, leading to better and better routing. The same potential holds for stealth assessments. Researchers can use student performance data from across a variety of tasks to update conceptual models and better understand how students learn. David Kuntz, vice president of research at Knewton, one of the companies developing new “adaptive” learning platforms, notes that, just as data collection improves the recommendations of a GPS system, collecting large data sets in the classroom can help to confirm or disconfirm hypotheses about how students learn. And, by comparing how similar students perform when given different types of content or instructional activities, researchers can also begin to understand which learning interventions work for which students, under which conditions.
Education, of course, can’t be reduced to a series of online games. More than just a set of concepts to be learned, it’s also a complex set of relationships: between students, teachers, and the environment in which they learn. Florida State University professor Valerie Shute, who coined the term “stealth assessment,” agrees. Her assessments aren’t meant to replace human teachers, but to help teachers understand student misconceptions and provide recommendations for action. She sees automated scoring and machine-based reasoning techniques as tools for teachers to “infer things that would be too hard for humans.” Just as a pilot uses a navigational computer to crunch vast amounts of data for use in flight, teachers should use these tools to play an ever more active role, reviewing students’ progress and providing better-informed guidance and assistance as they solve problems.
While computers have long been used for drilling facts or equations, Shute is designing her assessments to keep tabs on a deeper kind of learning—the kind that takes greater care and effort to measure but is essential for making sound progress. In math, for example, heavy drilling may help students pass a quiz on, say, fractions. But in order for them to put their knowledge of fractions to good use later on in, say, algebra class, students need a real conceptual grasp of what fractions are and how they work.
Shute’s new project involves building and embedding stealth assessments in the game Crayon Physics Deluxe (CPD). This is meant to cultivate and measure just that kind of mastery. In CPD’s virtual world, students must discover and/or apply their knowledge of the principles of physics, such as gravity, kinetic energy, and inertia, to propel a red ball through various puzzles toward its destination, marked by a yellow star. But in this world, just as in the real world, students aren’t just given problems with one predefined answer with which to solve them. Instead, students experiment with different approaches in a world largely of their own creation. Using a virtual crayon, they draw their solutions. In one instance, they might draw a ramp to roll the ball across an obstacle. In another, they draw a rock that falls on a lever to thrust the ball upward. The game encourages students to continually refine their approach, rewarding not just what it calls “old school” solutions but also more “elegant” ways to move the ball toward its destination.
As they play, CPD is assessing their performance constantly, collecting information on both simple indicators, such as the time spent on a particular problem, and complex information, such as the agents of force and motion—a springboard, say—that students use to accomplish a task. As students play, the assessment draws on more and more of the data points, which are constantly mapped against a model to update an estimate of the student’s competencies. In this case, a teacher could use CPD alongside more traditional instruction, ensuring that students understand the mathematical equations in physics but also the concepts underlying it.
Another stealth assessment in CPD strives to measure students on their care, organization, and persistence in trying to solve problems—what researchers call “conscientiousness.” Research consistently shows that these skills can predict academic achievement but are independent from intelligence or cognitive ability. They are also essential to success in school and life. In CPD, data on persistence, for example, comes primarily from problems that students have trouble solving. CPD tracks the number of times a student tries to solve each problem and the overall time spent on each try. And the assessment is designed so that even the cleverest students are given problems that challenge them; that way, all students are measured on their level of persistence. Of course, there are various pencil-and-paper tests that can measure these skills. But those tests typically involve self-reported items and are taken in isolation from the learning process, as if persistence were a static quality, unrelated to the actual task or content at hand. Shute’s goal is to build stealth assessments that can be inserted into almost any game or interactive lesson, allowing both students and teachers to see how qualities like persistence and creativity relate to their overall performance throughout the course of learning, and may even be improved over time as a function of game play.
So far, all these stealth assessment prototypes fall into the category of what educators call “formative assessments.” That means they are functionally analogous to the kinds of short-term tests, like chapter quizzes, that teachers use for diagnostic purposes—to gauge whether students grasp the lesson you just taught, so you can adjust your instruction in real time. This sets such tests apart from “summative assessments”—the weightier, more stress-inducing tests taken at semester’s or year’s end to judge the performance of students, teachers, and schools. It’s reasonably clear that stealth technology can someday be used for formative assessments. The big question is whether this technology can also eliminate the need for the annual summative testing.
The answer, in theory anyway, is yes. If done correctly, stealth assessments could help educators amass much greater evidence, over time, and at a deeper level, of what a student knows and is able to do. But doing so will require major changes in instruction—changes that would probably be beneficial for a whole host of reasons.
Today, a calendar defines what students learn and how they progress. An eighth-grade U.S. history course fits into two eighteen-week semesters, with a test at the end of each. And no matter what knowledge students walk in with or what they manage to absorb in the first eighteen weeks, the teacher must move on to the second eighteen weeks’ worth of content when the schedule dictates. This is what some educators call a “time-based” approach to education; a “competency-based” approach flips this paradigm. In this model, rather than wait for an end-of-year test, students can demonstrate their competency in a subject over time, allowing them to move on as they are deemed ready. Learning, instead of seat time, defines progress.
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