Your teachers and building staff probably collect and use multiple sources and types of data right now. However, before starting an English Language Arts (ELA) Standards implementation effort, your school or district should evaluate its existing data collection system to determine whether the system can provide timely and reliable data.
These data will inform all of your implementation decisions and help lead to a successful effort. This is an important, but often undervalued step. If your data system is not sufficient for your implementation, you may need to identify and secure funding to purchase a more robust system.
You will need to analyze and report data over time and for multiple levels, including data about individual students, classrooms, grades, schools, or an entire district. Leadership and Implementation Teams use these data to determine progress, identify needs, and generate improvement plans. School principals or leadership teams analyze data across grades or curriculum areas to address systemic issues. Staff analyze outcome data to find the appropriate level of intervention, such as school-wide or at the level of the grade, group, or individual student. Individual teachers use data to monitor student progress, make educational decisions, and check for improvement in their own practices through fidelity assessment.
Types of Data
Leadership and Implementation Teams need to gather, analyze, and act upon multiple sources of data. Outcome (or student) data show the impact of adult behavior on student performance. Performance assessment (or adult effort and fidelity) data show the extent to which teachers and building staff are effectively applying the critical aspects of the practices.
Outcome data measures the extent to which the actives, initiatives, and improvement efforts are leading to a desired end. Academic performance data are typically obtained through standardized assessments, these data may be used for establishing baseline, monitoring progress, and evaluating the areas of improvement and continued weakness. Non-academic outcome data includes data such as attendance, office discipline referrals, student engagement, and school climate.
Outcome data are the end to which all other data becomes a means of gauging our ultimate success. Outcome data are critical in helping teachers and administrators in prioritizing areas of success and continued need. Outcome data can be used for school-wide decisions on down to decisions about individual performance; however, administrators and teachers must be aware of the intended use of the assessments and use data according to its intended use.
Performance Assessment: Teams must collect and use performance assessment data to know how to allocate resources to support adults. Performance assessment is in service to reaching student outcomes. Without measuring efforts and fidelity of implementation, the ability to make accurate decisions about training, coaching, allocation of resources, etc. is not possible. A deeper analysis of effort and fidelity follows:
- Effort data often include such things as time spent on a specific endeavor or the number of training sessions attended. At the beginning of implementation, teams may use effort data, such as time allocated on an agenda or number of training sessions, to document their inputs and resources. Effort data is a quick way to determine if staff is translating training into practice. While effort is important in guiding the need for more leadership supports, need for training, and coaching, it is not sufficient to make attributions about the depth of implementation and success or failure of a practice.
- Fidelity data measure the extent to which teachers and building staff have applied the critical features of a practice as they were designed. Fidelity data should focus on competency and quality. Independent checks for fidelity are more valid and reliable than self-report. As implementation efforts continue, checking effort and fidelity of practices against outcome data will guide next steps in strengthening the implementation effort. It is important to note that effort and fidelity data are only meaningful when connected to results.
These types of data not only measure success in attaining intended outcomes, but how well implementation efforts are in leading to more effective and efficient systems. Leadership and Implementation Teams need to gather, analyze, and act upon two different kinds of data. Outcome (or student) data show the impact of adult behavior on student performance. Performance assessment (or adult effort and fidelity) data show the extent to which teachers and building staff are effectively learning and applying the critical aspects of the implementation.
Effective Leadership and Implementation Teams must regularly use both outcome and performance data to make decisions about how to make systemic, group, and individual level decisions.
PK-3 Assessment Guidance
. (http://education.state.mn.us/mdeprod/idcplg?IdcService=GET_FILE&dDocName=041563&RevisionSelectionMethod=latestReleased&Rendition=primary). This six-page guide supports the requirement to build a comprehensive assessment system. This resource includes information on oral language assessments, as well as screening, diagnostic, and progress monitoring tools that will help educators make decisions about assessing and identifying reading proficiency in young readers. This guide includes all features required by law, but does not provide guidance on performance assessment of adult implementation effort and competency.
Introduction to Data-Based Individualization (DBI): Considerations for Implementation in Academics and Behavior
(http://www.intensiveintervention.org/resource/introduction-data-based-individualization) which provides a rationale for intensive intervention and an overview of DBI. DBI is a research-based process for individualizing validated interventions through the systematic use of assessment data to determine when and how to intensify intervention. Two case studies, one academic and one behavioral, are used to illustrate the process and highlight considerations for implementation. The training module includes a PowerPoint presentation with speaker notes, handouts, and a coaching guide.
Using Student Achievement Data to Support Instructional Decision Making
(http://ies.ed.gov/ncee/wwc/pdf/practice_guides/dddm_pg_092909.pdf). This 76-page guide from the Institute of Education Sciences (IES) provides a framework for teachers and administrators who use student achievement data to support instructional decision-making. It provides recommendations for creating the organizational and technological conditions that foster effective data use. The short two-page summary of the IES guide
(http://educationnorthwest.org/webfm_send/1123) by the Northwest Regional Education Laboratory is useful for creating a planning checklist.The Active Implementation Hub
(http://implementation.fpg.unc.edu/). These short online learning modules cover the field of implementation science and focus on specific implementation tools and practices.
The Decision Support Data System
(http://implementation.fpg.unc.edu/module-2/organization-drivers) and the Continuous Improvement Cycles
(http://implementation.fpg.unc.edu/modules-and-lessons) modules are helpful for leadership teams and those engaged in using data to support implementation.