Putting Data Analysis to Work

Using data analysis to answer the questions, “What do the data tell us about our students’ learning and what do we do next?”

Presented by: Ginny Huckaba

NORMS

Be timely, present and participatory Phones on silent Minimize bird walking (M. Hunter) Return from break

Goals

At the end of this session, participants will:

1. Have knowledge of the process of data analysis

2. Be able to use HIVE site to analyze student performance data

3. Have templates to use to perform item trend analyses

4. Be able to serve as a resource of information to other educators

5. Have developed a plan for taking the knowledge back to colleagues

AGENDA

1. Welcome, Introduction, Goals

a) Group dynamics

b) What do you already know?

c) What do you want to know (goals)?

2. Using Data to Enhance & Improve Student Learning

3. HIVE

4. Item & Trend Analysis

5. Data analysis-needs assessment

6. Planning for Back-home Colleagues/PD

7. Close

What is Data Analysis?

The breaking (“unwrapping” per Ainsworth) of a whole into its parts and looking for relationships and functions. In the educational setting, it provides clarity for what students must know and be able to do.

It is NOT data disaggregation (that stops at the breaking-down stage)

Analyzing data requires: looking at data closely and objectively. using it to make improvements. halting the gathering of it if you don’t use it!

Analyzing the Data

What is the function (the purpose) of data analysis?

What does “it” measure and how is that information used?

What is the best use for assessments? What are the relationships between interim assessment data, progress reports/report cards and criterion-referenced tests?

Common reasons for Data Analysis: Improve student learning and achievement

Improve teachers’ instruction

Provide students with feedback on proficiency

Get a common understanding of exemplary performance/work and how to achieve it

Measuring program effectiveness

Rescuing kids who are falling through the cracks

Learn what programs are yielding desired results

Getting to the root cause of a problem

Accountability

Guide curricular revisions/development

4 Data Lenses through which to look:

1. Demographics ( sources: test scores, APSCN)

2. Student learning (sources: state, school, class levels)

3. School process data (sources: special programs, finance, transportation, professional learning)

4. Perceptions (public/stakeholders; sources--surveys)

Step 1 of Data Analysis: Data Collection

(Treasure Hunting) Student assessment data shows what is/was—they do not necessarily tell why

“Why” may come from secondary data Teacher training

Instructional strategies

Student demographics, norms, behaviors

Interventions

Existing support systems

Step 1: Data Collection Ask: “Who are our kids and how are they performing on high-stakes assessments?”

Find out: “How do the prominent stakeholders feel about our actions/efforts?”

Examine: “What programs and processes are in place in our school that meet our students’ needs (or don’t meet them)?”

Objectively answer: “How are our students identified for extension, supplemental, and gifted-student programs?”

2: Data Analysis(Reflection)

Most critical; most complex to organize

Student achievement drives change (avoid getting PD cart before the horse)

Don’t implement what kids don’t need

Look first at the data, then decide on the PD

Focus on use of data at the classroom level

To be effective, teachers must be provided: Tools

Time

Leadership’s commitment

2: Data Analysis(Reflection)

What did we discover from the data:

Strengths & Success (celebrate)?

Challenges (to be met)?

Trends (across subject/grade levels)?

3: Set Data-based Priorities(Translation—or “triage!”)

Careful scrutiny of each component:

What has to be done

External factors

“Do now” (meet AYP)

Rank Ordering (determining most pressing)

Based on areas of greatest need

Based on existing capacity

Set Data-based Priorities(Translation)

Moves the data to the instructional level

Requires change at the systems level

Decisions grounded in: Analyzing test data

Implementing measurable changes

Studying change data

Making decisions from resulting data

Evidence: visible changes within schools

Set Data-based Priorities (Translation)

Components (partial listing):

Curriculum mapping Content adjustments Instructional adjustments Data-driven instructional

design, feedback Formative and summative

assessments Goal setting

4: Goals

Goals must be:

Explicit

Focused

Make sure all goal statements are designed to be:

Specific

Measurable

Achievable

Relevant

Timely

Goals—three essentials

Setting

Reviewing

Revising

5: Instructional Design

Is powerful & focused

Requires: teacher training and consistent practice in classroom instruction

Enhances student achievement

Is composed of Instructional Strategies

Is NOT: activities, programs, adopted textbooks

6: Feedback(Interim measures)

Formative (formal and teachermade) and summative assessments (and teachers’ use of them)

Use to determine: Proper implementation of instructional design

(strategies)

Effectiveness (intended effect on student performance taking place)

“Is it worth it?”

7: Action Plan(It’s Quality, not quantity, that

matters)

Quality Action Plan Must Haves:

Explicitly communicated to staff, parents

Administrative backing of implementation and sustainability

Leadership follow-up

Relentless efforts to maintain focus on the data

The Process, in a Nutshell:

Build the team, then get started

Identify the Problem (key indicators for student success/failure)

Hypothesize and articulate hunches

Identify the data to be examined and gather it

Analyze the data (strengths/weaknesses/trends/challenges)

Begin developing an Action Plan by:

Setting Goals for the plan and implement the plan

Evaluate using interim assessments/measures

Make decisions for improvement based on evaluation results

Put Action Plan to work

Start the process all over again—data analysis IS a cycle, NOT a checklist!

Building your Team

Collaboratively decide: Who is going to do what work and when?

Team roles (not limited to): Lead project

Input data

Produce reports

Maintain data “warehouse”

Report the results of analysis

Questions to Ponder: Is time set aside to reflect on actual student work?

Is there a process in place whereby reflections and insights are used to make modifications in instructional/school practices?

Is there a habit of taking action as a result of patterns/trends that come out of the data?

Are there redundant or ineffective practices that need to be eliminated?

Is data collection followed by their analyses and changes/improvements?

Is the feedback cycle used for continuous improvement of instruction? (evaluation, decisions made based on evaluation results , actions taken because of those decisions, evaluation of resulting actions, etc.)

Resources:

• Bernhardt, V.L. (2004), Data Analysis for Continuous School Improvement. Larchmont, NY: Eye on Education, Inc.

• Blink, R.J. (2007), Data-Driven Instructional Leadership. Larchmont, NY: Eye on Education, Inc.

• Love, N. editor (2009), Using Data to Improving Learning for All. Thousands Oaks, CA: Corwin Press

• White, S.H. (2005), Beyond the Numbers. Englewood, CO: Advanced Learning Press

• White, S.H. (2005), Show Me the Proof. Englewood, CO: Advanced Learning Press