Counterpoint: Disaggregating student data will help, not hurt, the cause of addressing gaps

The April 7 article "New law breaking down student ethnic data triggers battle among parents" provided readers with some, but not all, of the pieces of the puzzle. As a professor who prepares future educators, I know that many Minnesotans, not just those of Asian background, care — or should care — about better disaggregating student achievement data.

Our state has the unfortunate distinction of leading the nation with respect to gaps in academic achievement. State scores on the National Assessment of Education Progress (NAEP), known as the "nation's report card," show that while Minnesota fourth- and eighth-graders perform among the best in the nation on assessments in reading and math, the gaps between students of color and their white peers is substantial, and significantly larger than national averages.

While this problem is long-standing and driven by myriad forces, lack of basic data is surely one of them. A few examples to illustrate the absurdity of our current system of tracking student academic achievement:

A 17-year-old student arrives in Minneapolis from a refugee camp in Kenya with close to no English knowledge and limited formal schooling. His English skills are assessed and forms completed to enroll in high school; he is then entered into the district database as "African-American," and grouped with African-American graduation and achievement data going forward.

A 12-year-old from Burma with interrupted formal schooling experience arrives to Mankato. When she enrolls, she is identified in the district database as "Asian," and her graduation and achievement data are aggregated with Japanese-background and Chinese-background students who speak only English and have lived their entire lives in suburban neighborhoods.

A recently arrived 15-year-old from Guatemala who speaks Mam, an indigenous language, as her first language, is lumped together with other Latinx students of Spanish-speaking backgrounds.

All of these student groups have vastly different academic, social and linguistic needs and skill sets. Aggregating student data renders the different challenges and abilities of particular student groups invisible. It also masks who is (and isn't) participating in gifted, talented and AP programming.

My responsibility at the University of Minnesota is to prepare teachers to work with Minnesota's K-12 students. The lack of data about which policies, programs and schools are serving which student groups well, and underserving others, limits our knowledge and effectively ties my hands, and those of other teacher-educators, administrators and teachers.

For this reason, dozens of leading state organizations, including the Coalition of Asian American Leaders, African American Leadership Forum, the Wilder Foundation, EdAllies, Generation Next and many other groups, worked for years to advocate for and support our current data disaggregation law, known as the All Kids Count Act.

As these groups and other leaders have argued: Not only does disaggregated data hold the potential to better serve recent arrivals and to close our state's yawning achievement gap, it can provide crucial information to efficiently tailor programming. For instance, where might there be greatest interest in Chinese history electives? Which schools would benefit from dual language (bilingual) programming?

Opponents of data disaggregation are unfortunately correct that the U.S. has an awful history of anti-Chinese and anti-Japanese discrimination, and there is good evidence that biases are still at work today in college admissions (and other arenas). We collectively need to identify and oppose this.

However, the disaggregation of K-12 student achievement data, which allows us to measure the success of teachers, schools and districts to serve all of our state's students, is not the enemy. Rather, it is a key component in our shared goal of making sure that all kids count — and succeed.

Kendall A. King is a professor of Second Language Education at the University of Minnesota. She recently completed an analysis of data disaggregation with Shuang Fu that appeared in the journal Discourse.