Data-Based Decision Making
College and Career Readiness and
Success Center
Jenny Scala
Senior Researcher, American Institutes for Research
January 2017
Agenda
 Introductions
 Data-based decision making overview
 Selecting appropriate interventions
2
Overview of Data-Based Decision
Making
3
Institute of Education Sciences (IES)
Practice Guide: Using Student Achievement Data
The recommended practices for effective data use are as
follows:
1.
2.
3.
4.
5.
Make data part of an ongoing cycle of instructional improvement.
Teach students to examine their own data and set learning goals.
Establish a clear vision for schoolwide data use.
Provide supports that foster a data-driven culture within the school.
Develop and maintain a districtwide data system.
Source: Hamilton et al., 2009.
4
Examples of Recommendations
 Analyze data at all levels (i.e., state, school, Tier I, Tier II,
Tier III).
 Establish routines and procedures for making decisions.
 Set explicit decision rules for assessing student progress
(e.g., division benchmarks).
 Use data to compare and contrast the adequacy of the
core curriculum and the effectiveness of different
instructional and behavioral strategies.
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Tiered Approach
Students with disabilities
Receive services at all
levels, depending on need
~5%
Tier III
Specialized, individualized systems for
students with intensive needs
~15%
Tier I
Schoolwide
instruction for
all students, including
differentiated instruction
Tier II
Supplemental group systems for
students with at-risk response to
primary level
~80%
Academic
Focus
Behavior
Focus
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Types of Decisions
 Instruction
• How effective is the instruction?
• What instructional changes need to be made?
 Evaluate effectiveness
• Is the core curriculum effective for most students?
• Is one intervention more effective than another?
 Movement between supports and interventions
• How do we know when a student no longer needs additional supports?
7
Middle School Examples From the
Field
 Prescreening questionnaire is given to all incoming sixth
graders.
 District-provided cut scores are used to determine which
students are in need of interventions.
 School counselors organize all the data.
 Leadership team meets every four weeks and discusses all
students receiving intervention as well as those students
who have been referred to the team by content-area
teachers.
Middle School Examples From the
Field
 Intervention teachers meet every two weeks with primarylevel teachers to discuss students’ progress in both the
core curriculum and in the intervention.
 Data are also used as a “report card” for instruction.
High School Examples From the
Field
 The student information system contains screening and
progress monitoring data.
 The early warning system tool identifies which students are
at risk for not graduating high school.
 Data are reviewed during department, small learning
community, or monthly data meetings
• Data inform which students are placed in interventions.
• Student progress in interventions is reviewed during meetings.
 The school establishes exit and entrance criteria for
interventions.
High School—Examples From the
Field
 Data are shared with entire faculty during “data days” (half
days of professional development are held three times a
year).
 Students receiving Tier II or Tier III instruction are given the
opportunity every other week to view their progress
monitoring data and goals.
 Parents are notified of students participation in secondary
and/or tertiary levels of support with two weeks of
placement.
Process for Analyzing Data
Big Picture
Define Target
Other Data
Confirm Cause
Action Planning
• Review big picture data and predictions.
• What patterns emerge?
• What students or groups of students most concerned?
• What initial theories may explain why the student is at risk?
• What additional information could you collect to better understand
underlying causes of risk?
• Are there gaps in data you have available?
• What have you learned from this new data or evidence?
• What do you now believe is the likely cause(s) of risk?
• What do student(s) need (define the problem to be solved)?
• What steps or tasks need to be implemented to address the underlying
cause of concern?
• How will these changes be monitored to determine student progress?
• How will fidelity be monitored?
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