A Crystal Ball To Predict Student Struggles

What if you could predict which students would struggle before they do? What if your predictions were so accurate that they let you to focus your attention on those who truly needed your help? What if this enabled you to put support programs in place that made learning more equitable? This doesn’t have to be a “what if?” because this tool currently exists.
 

I first heard David Stevens speak about his Academic Support Index (ASI) at a conference in San Diego. My colleagues and I were so impressed by the potential that he was hired to help implement the program in my district, Downey Unified School District. For years I’ve wanted others to know about his research, so that’s why I’m writing about it on my blog.

The ASI is a tool to help identify students who need academic support as well as what level of support they likely need. David uses the metaphor of a ship at sea that has “tailwinds” which push it forward and “headwinds” that impede its progress. The ship can make more progress either by increasing the tailwinds or decreasing the headwinds. He goes on to say:

All students enter school with a combination of “headwinds” and “tailwinds”. Tailwinds are the things that make school easier for students: Parents with high education levels, cultural capital, stable homes, good attendance, and past academic success are examples. Headwinds are things that might make success in school more difficult: Having a learning difference, being an English Learner, a history of academic struggles, or low socio-economic status. Some students come to us with a lot of headwinds and some students arrive with a lot of tailwinds.

 
With the goal of minimizing headwinds and increasing tailwinds, he set out to statistically determine which factors strongly affect students’ success. His results, which he incorporated into a mathematical model called the Academic Support Index, give each student a score that helps educators determine “the likelihood that [a student] will require additional academic support to fully realize his or her learning potential.”

 

The ASI has helped identify ~400,000 students at a variety of school districts. What he learned was that the ASI had an effect size that was “almost twice that of other factors such as parent education level and socioeconomic status making it a strong predictor of student performance.” The results were so exceptional that they were accepted by the national research society American Educational Research Association (AERA) an astounding three times in 2015, 2017, and 2018.

This is a game changer people. I have not heard of anything like this that can reliably give us this kind of information. The ASI can help you answer the following questions:

• How can we tell if our programs or interventions are actually making a difference in student outcomes?
• How do we predict in advance which students might struggle academically so we can provide them with appropriate support?
• How can we more effectively and efficiently use our limited resources on the students most in need?
• How can we address the achievement gap without contributing to stereotype threat?

 

If the potential to support the students who need us the most excites you as much as much as it excites me, I highly encourage you to go to David’s website (academicsupportindex.com) and contact him ([email protected]) to learn more about how you can set this up at your school or district.