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Foundations of Data Literacy Dr. Janet Johnson September 27, 2011.

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Presentation on theme: "Foundations of Data Literacy Dr. Janet Johnson September 27, 2011."— Presentation transcript:

1 Foundations of Data Literacy Dr. Janet Johnson September 27, 2011

2 10 Years Experience Teaching Educators to Use Data Themselves

3 It’s required a paradigm shift

4  2008 EDSTAR, Raleigh-Durham, N.C. All rights reserved. We help educators see the faces in the data

5 Foundations Must Precede Data Literacy

6 She is low- income, minority, and lives with a single parent. At-Risk Model

7 She is scoring at the highest level. Predictive software shows probability of success is 99%. Pro-Equity Model

8 We created vocabulary and developed conceptual aids to help = students = below grade Understanding data about groups of students Understanding different types of data Aptitude Work Ethic Understanding reasons for success

9 Once a student is on a path, often academic status is assumed and used for alignment of other instructional opportunities.

10  2008 EDSTAR, Raleigh-Durham, N.C. All rights reserved. This high achieving gifted student is tracked low in math because of demographic characteristics. Then he gets recommended for other remedial interventions because of assumptions about why he was tracked low.

11 Math Tracking Traditionally Sixth grade placement is the strongest predictor of 8th grade math placement. and The main predictor of sixth grade placement of equally high scoring students is social factors—race being one of the most significant factors. O’Connor, C, Lewis, A, & Mueller (2008)

12 Importance of Alignment Alignment is an even stronger predictor of student achievement on standardized tests than are socioeconomic status, gender, race, and teacher effect. (Elmore & Rothman, 1999: Mitchell, 1998; Wishnick,1989)

13 A NC School System Used Teacher Recommendations Exclusively for Math Placement When Tracking Began. Correlation Between Top Level Scores and Top Track Enrollment (Differs by Race)

14 Achievement Gap A School Asked EDSTAR Analytics with help on math achievement gap

15  2008 EDSTAR, Raleigh-Durham, N.C. All rights reserved. © 2009 EDSTAR Analytics, Inc. Percentage of Students At or Above Grade on Math Standardized Test TrackingTracking Not much of a gap at all!

16 85% of Top Level Hispanic/Latino & Black students were tracked into the low math track in 6th grade! We created the Achievement Gap!

17 103 students were recommended for low track but moved to high track based on academic data and EVAAS prediction.

18 98% of the students were successful

19 Suspensions declined by two-thirds when students were properly challenged.

20 A student in the bottom math track, who was participating in the dropout prevention program, with the highest number of suspensions in the school, had the highest academic math score in the school. He had not had the prerequisite to Algebra (Pre-algebra). He graduated top in this class, and instead of being in the dropout prevention program in high school, is taking Honors and AP STEM courses.

21 Next Tuesday: The Board of Education from that school district is voting on a Board Policy that will require enrolling students in advanced math courses based on whether or not they meet academic criteria. Comparing when we began working with this district to the implementation of this policy, minority enrollment in advanced math classes has increased by 400%.

22 CHALLENGE YOUR ASSUMPTIONS… Begin building your Data Literacy Foundations today!

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