A range of research has documented that many common gifted and talented identification practices miss many students, particularly students of color and those from low-income families. Often these students are missed because 1.) roughly one in three schools don’t identify gifted students at all, and 2.) those that do too often rely on referrals when deciding whom to consider.
School district administrators typically do not want to do any more testing than is absolutely necessary. It takes away valuable instructional time and resources that could be used for something else. What’s more, as schools work to help students rebound from COVID-related learning loss, new ideas are needed for how to best target instruction to student learning needs—and this includes advanced learning needs.
It doesn’t have to be this way
School districts can use data they already have on hand to universally screen students for advanced learning opportunities. I led a research initiative to develop and release guidelines on how schools can use assessment data they already have on hand for the secondary purpose of making their identification process better and more equitable.
Most gifted and talented identification systems follow a two-phase process. In the graphic at right, Phase Two is where the actual identification or eligibility determination takes place. That’s where a school might collect a range of data to make eligibility decisions for gifted and talented programs or even 8th-grade Algebra 1 placement. But Phase One is where many students are missed. Too often there is no universal screener. Instead, schools rely on referrals, nominations or applications.
The result: Any student who isn’t referred or nominated or who doesn’t apply is never considered. And because research has shown that some students are more likely to be referred than others, the result is an ineffective and inequitable process.
Existing data can improve the identification process
Although it won’t solve all the ills facing gifted and talented identification, one simple way to help improve such a process is to use an existing, universally administered achievement or accountability test to screen all students for eligibility. Students who meet a certain threshold on the universal screener then move on to phase two where actual eligibility is determined.
This saves time and money because 1.) not every student goes through phase two and 2.) an existing assessment is used at phase one. It’s also more equitable because all students have access to the process via a universal screener.
Superintendent search: Why are applicant pools drying up in these big districts?
There are three requirements for an effective universal screener. First, it needs to be reliable in a psychometric or assessment sense. This means scores aren’t influenced by outside, irrelevant factors. This is a place where standardized achievement tests, for all their imperfections, outshine nominations or referrals. They are just much more consistent, particularly because their scores do not depend on who is doing the nominating or referring.
Second, an effective screener is strongly correlated with the eligibility criteria. In an ideal world, every student who will do well at phase two will do well at phase one (and vice versa). Since most gifted and talented services are academic in nature, phase two criteria should also be focused on specific domains. As a result, an academic achievement test in the same domain will be strongly correlated with phase two and make for an effective screener.
Finally, we want an effective screener to be fast, cheap and easy. We don’t want to spend more time testing than is absolutely necessary. And it doesn’t get faster, cheaper or easier than an assessment you’ve already purchased and already administered. Achievement data that a district already has on hand checks all three of the boxes.
So what should a district administrator do? I helped develop guidelines for how districts can use their existing achievement data as a universal screener for gifted and talented and other advanced learning opportunities. I’ve also written (along with my co-authors) additional guidelines on how to put these systems together. But that’s for the advanced user. For now, districts can make a lot of progress using data they already have to proactively seek out students who might benefit from advanced learning opportunities.
