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.

Monday, July 20, 2015

Identifying Students for Support During the Transition from Middle School to High School

The transition from middle to high school is a critical period of time for students. Students who do not make this adjustment smoothly experience long-lasting impacts on both graduation progress as well as post-secondary options. Identifying students who will require additional support, both academic as well as socio-emotional, is critical.  Increasingly schools have “early warning systems” to catch students as they begin to show signs of difficulty with this transition. The reactive nature of these systems often means that students are already in a difficult situation by the time they show up on schools’ radars.
Predictive analytics attempt to use past data to predict potential future outcomes. The Academic Support Index is a novel method for quantifying the likelihood that students may struggle in school.  Additionally, the institutional knowledge about students acquired during the middle school years is often lost as students articulate to high school.  Through the use of the “Middle to High School Transition Rubric” (MHST Rubric) critical knowledge was efficiently passed from the middle schools to the accepting high school.  The rubric consisted of six areas addressing academic and social-emotional concerns with scores ranging from one to five.  High rubric scores equated to high levels of concern.

All students were then screened for an Academic Support Index of four or higher, any individual MHST Rubric scores of four or five, and a total MHST Rubric score of eighteen.  The results of the screens can be seen below. The totals indicate the number of students who were identified through that particular screen.

Total Students Identified
Percent of ALL 9th Graders
Average End of Year Grade Point Average
Percent with ANY Ds or Fs
Screen: ASI of 4 or higher
Screen: Any 4s or 5s on the rubric
Screen: A total Rubric score of 18 or higher
Identified through any of the 3 screens
Not identified on any screens (includes students receiving Special Education services)
All Screened Students (Students From BUSD Middle schools. Includes students with IEPs.)
Students Not Screened (Not from BUSD Middle School)
All BHS 9th Graders

There was also a very strong relationship (r squared =0.9235) between the number of screens through which a student was identified and their end of the year GPA:

Using these three screens we chose to target group of eighty students (any student who showed up on at least two of the three screens) that was predicted to struggle with the transition from middle school to high school.  The combination of screens identified ninety percent of the non-special education students in the ninth grade class eventually demonstrating severe academic difficulty as measured by having four or more Ds or Fs at the end of the first semester. This difference was significant at p<0 .05.="" nbsp="" span="">

The validation of this method for identifying students for transitional support has significant practical implications for schools as they can more efficiently utilize resources to target and support at-risk students. 

Consider this:  We knew the names of 90% of our incoming 9th graders who would ultimately struggle during their freshman year while they were still in 8th grade. The validation of this method for identifying students for transitional support has significant practical implications for schools as they can more efficiently utilize resources to target and support at-risk students. 

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Screening Tool for At-Risk Students by David Stevens is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.