All students enter school with a combination of "headwinds" and "tailwinds". Tailwinds are the things that make school easier for students. Tailwinds include things such as coming from a home with parents of high education levels and economic stability, being a native English speaker, not having a disability, and 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. These include standardized and standards-based assessments such as the SAT, Smarter Balanced Assessments, AP and IB tests, STAR Reading and Math, kindergarten screeners, cumulative grade point averages, rates of college eligibility, and rates of college 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.

Friday, April 6, 2018

AERA 2018, New York, April 17th, 10:35-12:05

Session Title:   Building Your Own Academic Support Index for Research, Evaluation, and Intervention Design

Abstract 
Disaggregating data by demographic categories such as gender, race, and class ignores the fact that
students exist in multiple categories simultaneously and that these categories are inherently interactive. The Academic Support Index (ASI) addresses this by accounting for the additive impact of students’characteristics. The ASI is a tool based on the statistical relationship between demographic fields and student academic performance. The ASI has strong correlation to outcomes including Smarter Balanced Assessments, grade point averages, and post-secondary degree attainment. This session will include an introduction to the background, development, and effective applications of the ASI as well as a practicum for researchers and educators to calculate the ASI of their students.


Session Summary 
Objective of the session:
The objective of this session is to provide educational researchers and practitioners with the practical
knowledge of how to calculate and use the Academic Support Index (ASI) as both a lens to better
understanding their data as well as a tool for the evaluation of programs and the development of effective student supports. Persons attending this session will leave with a strong understanding of the ASI including its background, how it is calculated, its relationship to academic outcomes, and how it has been effectively used to improve student performance and help narrow the achievement gap. Attendees will learn how to both develop their own index as well as how to score their own students or research subjects using the ASI.

Overview of the session:
This session will be a combination of presentation of the ASI and its theoretical background, demonstration of how to calculate the ASI, and practicum where attendees will have the opportunity to calculate the ASI of their own students.

Scholarly or scientific significance:
A general practice in educational research and accountability has been to make comparisons of students from similar demographic categories such as by race, class, disability, or English language status. With a growing demand to show evidence of efficacy in educational practices, it is necessary to be able to compare outcomes across similar students. However, students are not products of a single demographic bucket but rather exist in many “buckets” simultaneously, all of which interact with each other. The ASI aggregates and quantifies the interaction of these buckets and their collective impact on student academic outcomes. Previous studies have demonstrated strong correlation between students’ ASI scores and academic performance. Because students with similar ASI scores tend to perform similarly across schools and overtime, the ASI is a valid and reliable tool for analyzing the impact of interventions and instructional changes on students. Through its ability to provide “apples to apples” comparisons, the ASI can enhance the quality of educational research and evaluation as well as provide valuable context when examining data.

Structure of the session:
This presentation will be composed of four sections:
1. Introduction and background of the ASI
2. Review of effective applications of the ASI
3. Demonstration of how to calculate your own ASI and score your students.
4. Guided practice for attendees to score their own students on the ASI. Attendees are encouraged to bring their own student data to use. The following data should be included if possible (OK if some data fields are not available):
a. Student identifier or ID
b. Gender
c. Special Education status
d. Socioeconomic status
e. English Learner status
f. Race/Ethnicity
g. Primary language
h. Highest household education level
i. Cumulative grade point average
j. Other academic outcomes such as Smarter Balanced Assessment scores
Estimated Attendance Edit Estimated Attendance
25
Length of Session 
1 hour, 30 minutes

Descriptors 
Achievement Gap, School Reform, Data-driven decision making

Participants 
David Stevens, davestevens@berkeley.net; Berkeley Unified School District (Session
Organizer)
--------------------------------------------------------------------------------------------------------------------------
Title (Session Paper)
Revisiting the Academic Support Index: A Validation Study Using Data from
Three School Districts

Abstract
Previous studies have shown that the Academic Support Index (ASI) has strong
correlations to academic outcomes and can be a valuable tool in educational research and
practice. In this validation study, the ASI was evaluated against standardized test
performance and grade point average in three school districts: rural, semi-urban (original
district of study), and urban. The results validated the earlier findings that the ASI is a
strong predictor of academic performance. The study also replicated the original ASI point
assignment protocol creating local versions of the ASI and evaluated these against the
same outcomes. Correlations for the locally developed ASI were not as strong as with the
original ASI.

Learn more about using the ASI framework in your school or district here.




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