Data-Driven Decisions

  • Data Driven

    Data-Driven Decisions

    Frequent data collection informs instructional decisions and groupings. 

    Classroom Goals: (ordered from emerging to sustaining)

    • Variety of material reflects differing student needs and current ability levels
    • Groups are based on quantitative or qualitative data (digital content, conferencing notes, exit tickets, benchmarks, interest, SEL goals, need etc.)
    • Visual tracking of student progress (data-walls, individual student trackers) that is frequently updated
    • Formative assessments are used throughout the lesson
    • Teacher uses a do now and/or exit slip that measures student skill/knowledge mastery 
    • Evidence of individualized feedback on student assessment, interactions, or work products
    • Evidence that teacher and students review data together to identify needs


    Teachers regularly conference with students individually to discuss academic data and set goals. Teachers review data points and unit tests, guiding students in understanding their strengths and growth areas. From there, teachers and students determine specific short- and long-term goals to strive for and strategies for achieving these goals. These goals are often SMART goals (Specific, Measurable, Assignable, Realistic, and Time-related).

    During data conferences, teachers help guide students to ensure they understand their learning data. Some of the areas a teacher might focus on when discussing data with the student are:

    • Ensuring they understand what the number means (percentage, score, etc.).
    • Showing them how their scores compare to national or class averages.
    • Helping them identify areas where they performed well.
    • Helping them identify areas where they could improve.
    • Identifying areas where they are ready for new content (by showing mastery of prerequisite content).

    These data conferences help motivate students by improving their understanding of data beyond letter grades. This helps students make the connection between their day-to-day work and their learning data (“I want to improve X score so I need to spend more time doing Y and Z”).

    Student Does

    • Discusses data with teacher, and sets short- and long-term goals.

    Teacher Does

    • Guides students in understanding data and setting goals.

    Technology Does

    • Stores assessment data for each student.