To Be Data-Driven
What does it mean to be “data-driven?"
Bambrick (2010), in Driven by Data, outlines four key principles that drive the term “data-driven” in assessment analysis, action research, and in establishing a data-environment or "data culture":
- Assessment must be rigorous, and interim assessment must provide meaningful data.
- Analysis should involve the reexamination of results to identify root causes of students' strengths and learning gaps.
- Data action invokes teaching strategy focused on precisely on what students need most to learn.
- A data culture is an environment in which “data-driven instruction can survive and thrive” (p. xxvi).
Looking at data from an instructiona lens has a learning curve; there are several mistakes commonly made when using data to drive instruction. Among them four that we will detail and address in this course. Often due to limitations in time, scheduling and resources, here are some common mistakes educators often make when looking at data for the first time:
- Infrequent and/or inferior use of assessments, such as using only standardized assessments, or neglecting the value of benchmark and interim assessments.
- A misalignment of assessment to curriculum
- Delayed assessment results that cause for delayed action, such as reports in October for assessments taken in April or May.
- Ineffective follow-up to assessment data.
Bambrick, 2010
Interim Assessments and a Data-Culture
Interim assessments are highly relevant to creating and maintaining an effective data-driven culture. They must be carefully and strategy thought out, especially if they are written tests, otherwise using them to effectively analyze strengths and weaknesses is impossible. Here are some important considerations when creating interim assessments:
- Set the bar high
- Align them to student, grade-level goals and objectives
- Align them in rigor to the state assessments (create them side-by-side to one another).
- Include written and open-ended responses; never leave these responses out for sake of easier to grade multiple-choice.
- Reveal and review the interim assessments ahead of time – they’re maps that let you know where to go with your instruction.
Much of this course will focus on leveraging multiple assessment types with data-driven instructional strategy; assessment is at the heart of what we need to know about our students. In order for us to leverage this pulse with what they should know and be able to do, we must evaluate their progress along the way. Assessment must be common and frequent to accurately track, evaluate, and predict, student progress (Bambrick, 2010).
How frequently should we assess our students? More than every few months; assessment should be monthly, without occupying the majority of curriculum time. Our best assessment should also be formative, thereby allowing us to draw information about our students while they’re working (Bambrick, 2010).
Curriculum Scope and Sequence
It is vital that assessment be aligned to what will measure students for ultimately – strengths and weaknesses; goals and objectives; curriculum scope and sequence. The scope and sequence needs to match the interim assessments in order to know what to teach, what to assess, and what to do with the results. These results need to be immediate so that we can be proactive, with results analyzed through data teams or PLCs. Teachers should analyze their own students’ results, making their own plans for, and with, those results (Bambrick, 2010).
Framework for Data Culture Implementation
- Use of common interim assessments with grade level teams analyzing results, establishing common goals and developing lessons, units, and/or curriculum.
- Development of action plans, or data next-steps to aid in the strategic decision-making needed to strengthen core instructional approaches.
- Develop assessments around the standards – know how they will be evaluated. Know what the end is, but begin with them (Bambrick, 2010).
- Use in-the-moment assessments that require constant checks for student understanding with opportunities to make adjustments to instruction. Often these checks for understanding are more powerful than interim assessments with the immediate data received about the extent to which students are learning, and why they are not.
Here are four cardinal rules about assessment – Assessments must be…:
- the starting point
- transparent
- common
- interim
The Starting Point:
“for data-driven instruction to be effective, this process must…be created before teaching ever begins. In data-driven instruction, the rigor of the actual assessment items drive the rigor of the material taught in class” (p. 12).
- Assessments must be written before teaching begins so that adjustment can be made to curriculum and lessons in order to address all skills necessary.
Common and Transparent Assessments
Transparency means available to all educational stakeholders – teachers and school leadership alike. In addition to using standards to drive rigor, assessment data must drive it too. The public visibility of this data assures that all educational stakeholders know what the markers are, where students have gone, where they need to go, and what needs to be done to get them there. Common assessments assure that all content areas have the same expectations. Common assessments have repeatedly proven to further the sharing of ideas among teachers, collaboration and facilitate effective curriculum design (Bambrick, 2010; Reeves, 2008).
This course will delve carefully and enthusiastically into a focus on developing and using data to employ effective on-going instructional decisions that work into high student achievement by looking at what it means to be data-driven, what a data-driven culture looks like, how to use formative and interim assessment data, student artifacts, protocols to use in teacher teams, what studies have shown and what research says about data culture. Most important, we will learn what tools and resources can, and have been, used to effectively analyze and use data responsibly. Decision-making that results from the collection, analysis, and interpretation of data will work into responsible planning with strategy for effective instruction - we will practice with all of this throughout the 10 modules of this course. In addition to best practices for analyzing data in teacher teams, we’ll look at methods for involving student in their own decisions about using data to self-inform and build school-wide collective capacity. Here’s to a wonderful online course experience!