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NJPEP Home > Professional Development > Achievement Gap

What does data look like? What does it show us?

    Data must be valid. "Total" data (all the data lumped together) may be analyzed in order to develop policies involving all stakeholders. In addition, trends of data over time may be analyzed to see if intervention strategies have helped in reducing the gap.
    Disaggregating the data can help educators try to focus on a single problem or success, and then create targeted solutions, interventions, or building of capacity.  Disaggregated data may show subgroup or content cluster information.

Data may be reported in several formats
Data in table format. This format is good if there is not too much information, or if the information is being summarized. Data in graph format. This format is good for visual learners, and for showing trend or making comparisons.
Total Persons (1989-1990)
 88,675 
Percent Urban
100.00
Percent White 
  37.81
Percent Black
  48.15
Percent Amer Ind, Eskimo, Aleut 
    0.29 
Percent Asian/Pacific Islander
    0.42 
Percent Hispanic
  13.20 
Percent Other 
    0.14
Percent in Poverty
  17.31

Data of just one town in NJ.

from EdSource in California
Data can aid in supporting good teacher practice

NJPEP's Classroom Assessment web site gives examples of data and data use before, during, and completing instruction. Sections of the tutorial are:

  1. Before the First Day of Class
  2. Making a Plan to Teach
  3. Assessment for Instruction
  4. Statewide Assessment
Data shows student growth
The Northwest Evaluation Association (NWEA) has developed data tools that support growth in the classroom during the academic year, in the form of formative assessment. A district central office may have tools to disaggregate data from "district level down to the individual student." There are several major companies that have online, wireless and/or Palm-based software with similar features.
Data showing student growth is sometimes related to teacher accountability

In an article, Individual Student Growth Is Focus of California Analysis Model, Krista Kafer (from the Heritage Institute; 2004) reviews a system that "uses each student's Rate of Expected Academic Change (REACH) to reveal the impact of teaching on individual students--regardless of whether those students started class behind their peers or ahead of them." "...the model enables schools to reward teachers for individual student progress, no matter where those students initially rank compared to their peers." See also ETS's Using Student Progress To Evaluate Teachers: A Primer on Value-Added Models [PDF].

Data belongs to the entire educational community
Identifying the Factors, Conditions and Policies that Support Schools' Use of Data for Decisionmaking and School Improvement: Summary of Findings (2001), from the Education Commission of the States (ECS), identifies the factors, conditions and policies (state and local) that support the use of data for decisionmaking and school improvement. Preliminary recommendations for state policymakers are suggested.