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DDI and Assessments in Mathematics: Analyzing and Tracking DataPowerPoint Presentation

DDI and Assessments in Mathematics: Analyzing and Tracking Data

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DDI and Assessments in Mathematics: Analyzing and Tracking Data

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DDI and Assessments in Mathematics: Analyzing and Tracking Data

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DDI and Assessments in Mathematics: Analyzing and Tracking Data

At the K-5 Level

May 15, 2014

- Be able to describe what to look for when analyzing student work for a Common Core-aligned assessment
- Be able to create a data tracker for assessments
- Develop questions that drive data-analysis meetings around Common Core-aligned assessment data

- Introduction
- Warm Up: Review of Assessment Design
- Looking at Student Work
- Examples
- Time with work you brought

- Tracking Student Work
- Leading a Data Meeting with Questions
- Q & A

- Part I: Assessment (February)
- Part II: Analysis* (May)
- Part III: Action (July)

- We don’t want to “granularize” content…
…butwe have to do something to look

“under the hood”

- We want our students engaged in rich tasks…
…butwe want to dig into the work

associated with the tasks to learn specifics about

what our students know and can do

- We don’t want to put rigor in silos or to create a “checklist” for rigor…
…but we want usable information about

how are kids are doing with respect to the

demands of the Common Core

1. What makes this assessment Common Core-aligned?

2. Critique this. How could it be improved?

1. Balance of rigor

2. Variety of levels

- I knew some of my students were functioning below grade level, so I used the NF domain heading to locate similar understandings at the 3rdgrade level. This drove instruction for my unit and allowed for more differentiation.
- I tried to vary performance level using PLDs by mixing identifying equivalent fractions and generating equivalent fractions.
- I tried to include a variety of prompts/question types that would offer a balance of rigor. This drove instruction for my unit and ensured a balance of rigor throughout the unit.

First Focus Question: “Imagine looking at some student work associated with this assessment.

What kinds of errors do you think you’d see?

What would these errors reveal about students?”

Second Focus Question: “Look at the work from Caitlin and Truman.

What kinds of errors do you see?

What do these errors reveal about students?”

Analyze student work based on:

1. The grade level standard(s) being measured

2. The type of error, viewed through a rigor* lens

- Procedural
- Conceptual
- Application

Notice:

1. The grade level standard(s) being measured

2. The type of error, viewed through a rigor* lens

- Spend some time with student work that you brought.
- What standards are being measured?
- What types of errors are being made?

- Break down data to show strategies employed (e.g., table, equation, picture)
- Break down P, C, A further (e.g., “P – Division of fractions”)
- Include other “lenses” (e.g., vocabulary, writing)
- Also tag items at performance levels, using PLDs
- Tag items to more than one standard

“Bambrick Model”—Based on Paul Bambrick-Santoyo’sDriven By Data

We’ll look at:

- “Pre-Cursors” (what happens before a data meeting)
- “Conversation Starters and Re-Directors” (what happens during a data meeting)
$64,000 Question:

How might these look different using

a Common Core-aligned assessment?

- How would we prepare differently for a Common Core assessment meeting?
- What different activities would we ask teachers to do?
- What different questions would we pose?

- What would be different during a Common Core assessment meeting?
- What different activities would we ask teachers to do?
- What different questions would we pose?

- Analyze student work based on
1. The grade level standard(s) being measured

2. The type of error, viewed through a rigor lens

- Be able to describe what to look for when analyzing student work for a Common Core-aligned assessment
- Be able to create a data tracker for assessments
- Develop questions that drive data-analysis meetings around Common Core-aligned assessment data

- Q & A