Data-Based Decision Making

Overview

Data are used to analyze, evaluate and plan strategies that support sustainable system improvement and learner outcomes. This includes conducting needs assessments, efficient data collection practices for multiple data sets (e.g. demographic, student achievement, satisfaction, process data), and a formal process to guide decisions. Data-based decision making (DBDM) requires that data are timely, valid, reliable, accurate and reviewed in ongoing cycles. The roles and responsibilities for data-based decision making within the system are clearly defined and executed.

Why It’s Essential

Using a DBDM process shifts the work of school leadership teams from a reactive or crisis driven process to a pro-active, outcomes driven process, and sets the stage for continuous improvement. Data analysis allows for evidence to guide decision making for both the effectiveness of the system and for the needs of all learners. Examination of data using a standard problem-solving process to review all of the aggregated and disaggregated data for a district, school, groups of students or individual students provides information to narrow the problem to identify potential interventions and strategies.

Implementation

Core practices that characterize effective and continuous DBDM include:

• The Leadership Team has access to and uses multiple sources that are used for DBDM and determine impact:

  • Aggregated and disaggregated academic and behavior outcome data

  • Input data (e.g., demographic)

  • Process data (e.g., quality of instruction, programmatic)

  • Outcome data (e.g., learner test scores, dropout rates, office discipline referrals)

  • Perception data (e.g., surveys)

  • Fidelity data

• At a system level, data are analyzed at least three times each year to

  • Set and monitor progress on action plan and school improvement goals

  • Map, align, and reallocate resources (fiscal, personnel, time, facilities)

  • Determine needs and progress of all learners across all tiers of support

  • Evaluate the effectiveness of practices across all tiers all tiers of

• At a student level, data are used to

  • Assess, adapt and improve academic and behavior support practices

  • Determine which students need additional support

  • Identify decision rules (e.g. benchmark cut points, ODRs) to determine which students receive additional Tier 2 or Tier 3 support or intervention

  • Progress monitor student rate of growth in Tier 2 and Tier 3

  • Increase intensity of intervention based on inadequate rate of growth

• Faculty and staff receive professional learning opportunities and ongoing coaching in the effective use of data

For more detailed information on Continuous Data-Based Decision Making, review the Montana MTSS Essential Components.