All students enter school with a combination of "headwinds" and "tailwinds". Tailwinds are the things that make school easier for students. Tailwinds may include factors such as coming from a home with parents of high education levels and economic stability, being a native English speaker, not having a disability, or being a member of the cultural majority. Each of those characteristics plays a role in helping a student experience success in school.

Headwinds on the other hand make school more difficult. Headwinds can include having economic instability at home, parents with lower levels of education, having a disability, or still learning English. The more headwinds a student has, the more difficulty they will have in maximizing their academic potential and the more “tailwinds” they will need. Tailwinds come in the form of high-quality instruction, support, and intervention.

The Academic Support Index, or ASI, quantifies these headwinds. A student’s ASI is the sum of their headwinds. Their ASI can also be considered a measure of the amount of support that they will need in order to mitigate the impact of those educational headwinds. Students with a low ASI will likely need very little additional support outside of Tier 1 instruction. Higher ASI students will likely need proportionally higher amounts of Tier 2 and sometimes Tier 3 supports.

There is a strong relationship between the ASI and academic outcomes including assessments such as the SAT, Smarter Balanced Assessments, AP and IB tests, kindergarten screeners, grade point averages, rates of college eligibility, matriculation, and degree attainment. We have studied these effects over seven years of data as well as across urban, suburban, and rural schools. To date over 400,000 students have been scored on the ASI. (See the featured post below for a list of papers and presentations on the ASI).

Because the ASI is able to reliably predict student outcomes you have to opportunity to interrupt that predictability by using the ASI to make sure that you are identifying the right students for early intervention and support. With effective intervention, predictive analytics can become preventive analytics.

Sunday, February 22, 2015

The Impact of disproportionality when disaggregating student performance by race

How might the narrative change if we could talk about student performance while simultaneously addressing the disproportionality of disability, English Learner, and low socioeconomic status?  Having a disability, being poor, and/or being an English Learner are not equally distributed across all racial/ethic categories as seen in the slide below:

In the table below the top three rows are how we traditionally look at student academic performance data. In the bottom three rows students with the additional headwinds of disability, being an English Learner, and being poor have been eliminated to create the potential for a more "apples to apples" comparison.  We see that the data regarding both Hispanic/Latino students and African American students is significantly better and approaches the performance of white students.
Note the significant changes in the "n" for the Hispanic/Latino and African American population. 

 The Academic Support Index takes into account these disproportionate factors (headwinds) and helps us understand student data in a way that is both more accurate (and therefore more useful to school systems) but also doesn't support a narrative that reinforces harmful stereotypes.  Additionally, the ASI takes into account the additive nature of these headwinds.  For example, having a disability, being poor, and being an English Learner is a significantly greater headwind than each of those three on their own.