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Data-Driven Instruction

  • Data-Driven Instruction

The introduction of the Response to Intervention (RtI) model in school districts means the role of data is more vital than ever. Educators are responsible for administering assessments, collecting data, analyzing data, and determining how to use the results. In schools today, assessment and instruction are closely linked and data-driven instruction is leading the way.

Data analysis provides educators the knowledge of what students know, what they should know, and what can be done to meet academic needs. Through interpretation, the educator makes informed decisions that affect student progress. In our M.Ed. and graduate teacher education programs at Merrimack’s School of Education & Social Policy, we ensure that the educators we teach understand how to use multiple data sources over a period of time to facilitate well-informed instructional decisions. Educators gain experience through hands-on courses using a variety of assessments and data, including:

State and District-Wide Assessments:

  • Results from assessments indicate which students performed at the advanced, proficient, basic, or below basic levels. These results help educators choose student groups, create seating charts, and differentiate instruction.
  • Analysis of test results per period taught guides decisions. If a high number of students scored advanced in a third-period class and a high number of students score basic in period two, an educator asks what is happening differently in period two than three. Adjusting teaching and support as needed is a key outcome of analyzing results like these.
  • Examine how “A” students performed on the test. If the strongest students did not perform well, then ask why. Was the performance possibly due to nerves, distractions in the classroom, etc.? Based on the analysis, an educator takes steps prior to the next test. These include providing a quick review of test strategies for lowering anxiety, taking practice tests, maintaining focus during testing, and more.

Formative Assessments:

  • Quizzes, exit slips, and thumbs up/down quickly assess what students know and what skills still need to be addressed.

Summative Assessments:

  • Essays, end-of-unit exams, and projects measure the growth of individual and whole-group learning. If a large number of students score poorly on summative assessments, educators reflect back on the teaching and make necessary adjustments to the content and/or delivery. This is one of the core functions of data-driven instruction.

Observations:

  • Observations indicate how well students are making sense of the content, struggling with a learning activity, and interacting with others.
  • Observation data allows educators to adjust pacing for the whole class or to provide scaffolding for those students who are still struggling.

Educators collect tremendous amounts of data on students’ attendance, behavior, and performance, but when it comes to improving instruction and learning, it’s not the quantity of the data that counts, but how the information is used (Hamilton et al., 2009). It could be argued as well that it’s the quality of the questions being asked that leads to the strongest uses of data.

In Merrimack’s teacher preparation courses, we provide real-world student assessment data and multi-step projects. M.Ed. students grapple with complexity under the guidance of our faculty who give iterative feedback across assignments. Students immediately apply what they are learning about data-driven instruction in their own classrooms as their understanding of data analysis and data-driven decision-making deepens.

2018-08-16T14:20:59+00:00