DataBased Decision Making College and Career Readiness and

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 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. 5

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 6

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? 12