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A Joint Event from the Mid-Atlantic Comprehensive Center and the Appalachia Regional Comprehensive Center February 5, 2014. Enhancing Teacher Data Literacy: What SEA s and their partners can do. Welcome from MACC and ARCC. Marty Orland, MACC Director Delaware District of Columbia

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Enhancing teacher data literacy what sea s and their partners can do

A Joint Event from the

Mid-Atlantic Comprehensive Center and

the Appalachia Regional Comprehensive Center

February 5, 2014

Enhancing Teacher Data Literacy: What SEAs and their partners can do

Welcome from macc and arcc
Welcome from MACC and ARCC

  • Marty Orland, MACC Director

    Delaware District of Columbia

    Maryland New Jersey


  • Sharon Harsh, ARCC Director

    Kentucky Tennessee

    Virginia West Virginia


  • Key features for attendees

    • Participants

      • All participants are muted

    • Chat

      • Use chat box to share questions or thoughts

      • Specify public vs. private

    • Polling

      • Questions/results will appear in polling box

    • Evaluation

      • Link to evaluation will be provided at end

      • Please provide email address if not registered

  • Polls are located on the right side of the screen.To better see the poll questions, minimize the participant and chat windows by clicking on the light blue arrow.

    Poll 1 tell us about yourself
    Poll 1: Tell Us About Yourself

    • What is your job responsibility/role?

      • State education agency staff

      • Credentialing/licensing agency staff

      • Dean or administrator from a school of education

      • Faculty member

      • School or school district staff

      • Comprehensive Center staff

      • Other

    • What state(s) do you represent?

      • Delaware

      • District of Columbia

      • Kentucky

      • Maryland

      • New Jersey

      • Pennsylvania

      • Tennessee

      • Virginia

      • West Virginia

      • Multiple states – Mid-Atlantic

      • Multiple states – Appalachia

      • Other


    Today’s Agenda

    • Setting the stage – Ellen Mandinach, WestEd

    • The role of the SEA – Janice Poda, CCSSO

    • The role of IHEs and licensure agencies – Ellen Mandinach, Edith Gummer, & Jeremy Friedman, WestEd

    • Description from Delaware – Elizabeth Farley-Ripple, University of Delaware

    • Discussion – all

    • Next steps – Ellen Mandinach


    The need to improve teacher preparation and data literacy
    The Need to Improve Teacher Preparation and Data Literacy

    • Emphasis from policymakers

    • Emerging standards from CAEP and other professional organizations

    • NCTQ


    Why is data driven decision making important for education
    Why is Data-Driven Decision-Making Important for Education?

    • Proliferation of diverse sources of data

    • Need for evidence-based practice

    • Changes in policymakers’ emphasis from data for accountability and compliance to data for continuous improvement


    What is data literacy for teachers
    What is Data Literacy for Teachers?

    • The ability to transform information into actionable instructional knowledge and practices by collecting, analyzing, and interpreting all types of data (assessment, school climate, behavioral, snapshot, longitudinal, moment-to-moment, etc.) to help determine instructional steps. It combines an understanding of data with standards, disciplinary knowledge and practices, curricular knowledge, pedagogical content knowledge, and an understanding of how children learn.


    Data literacy for teaching categories of skills
    Data Literacy for Teaching: Categories of Skills

    • Inquiry Processes

    • Habits of Mind

    • General Data Use

    • Data Quality

    • Data Properties

    • Data Use Procedural Skills

    • Transform Data to Information

    • Transform Data to Implementation


    An important caveat
    An Important Caveat

    • Data literacy is NOT the same as assessment literacy

    • There are differences

    • The differences are important

    • Typically called data literacy but most often really assessment literacy

    • Counter example – CCSSO (2012)


    Systemic nature of the reform issue
    Systemic Nature of the Reform Issue

    • Complex

    • Interacting players

    • Change cannot happen in isolation

    • Change comes slowly


    Who are the key players
    Who Are the Key Players

    • State Education Agencies

    • State Licensure Agencies

    • Professional organizations

    • Schools of education

    • Testing organizations

    • Local Education Agencies

    • Others


    A metaphor from the data quality campaign
    A Metaphor from the Data Quality Campaign

    • The Flashlight

    • vs.

    • The Hammer


    What can seas do
    What Can SEAs Do?

    • Integrate data literacy into licensure requirements

    • Be informed by research and policy trends

    • Recognize the difference between data literacy and assessment literacy

    • Work with schools of education to align curricula

    • Work with districts to understand needs


    Poll 2 status of reform in your state
    Poll 2: Status of Reform in Your State

    • Is data literacy a topic of action/reform in your state?

    • • Yes, definitely

    • • Yes, but not a strong emphasis

    • • No


    Poll 3 data literacy in courses and licensure requirements
    Poll 3: Data Literacy in Courses and Licensure Requirements

    • Is data literacy something that you have included in courses and licensure requirements in your state agency or institution?

    • • Yes, definitely

    • • Yes, but not a strong emphasis

    • • No


    Some questions
    Some Questions

    • Are any states in the process of updating data literacy requirements for teacher preparation programs based on updated InTASC standards, the Danielson framework, or other guidance?

    • If you are a state licensure person, are you trying to strengthen or make more explicit the requirements around data literacy skills?

    • What are your challenges, successes? What kinds of resources, supports, or assistance might you need?


    The Role of the SEA

    Janice Poda

    Education Workforce

    Council of Chief State School

    Officers (CCSSO)

    The role of seas
    The Role of SEAs

    • SEAs have the power of persuasion through the bully pulpit, role modeling, and the enforcement of law through policy to ensure that teachers and leaders are data literate.

    • Examples of policy levers to ensure that teachers and leaders are data literate are:

      • Program approval

      • Licensure

      • Renewal of a license

      • Professional Learning


    Ccsso s emphasis on data literacy
    CCSSO’s Emphasis on Data Literacy

    • InTASC Standards

    • Task Force report titled “Our Responsibility, Our Promise”

      • 7 pilot states participating in the Network for Transforming Educator Preparation

    • Revision of Leadership Standards

      • ISLLC (practicing school leaders)

      • ELCC (candidates for school leadership)

      • Principal supervisor (district office employees who support, develop and evaluate principals)

    Where states are currently
    Where States Are Currently

    • Data from 49 states and the District of Columbia have some sort of documentation that addresses what knowledge and skills teacher candidates need in order to be licensed. The documentation ranges from outdated to up-to-date.

      • 21 states address data literacy

      • 37 states address assessment literacy

    • Six states (AR, AZ, DC, NV, ND, and SC) make strong use of the InTASC standards (CCSSO, 2011) and one state (SD) uses the Danielson framework (2013).

    • Source: Mandinach, Friedman, Gummer (2014)

    Excerpt from rhode island recently adopted program approval standards
    Excerpt from Rhode Island Recently Adopted Program Approval Standards

    • 1.4 Data-Driven Instruction: Approved programs ensure that candidates develop and demonstrate the ability to collect, analyze, and use data from multiple sources- including research, student work and other school-based and classroom- based sources- to inform instructional and professional practice.

    Determining success
    Determining Success Standards

    • Data literacy is not just knowledge but the application of knowledge into effective practice.

    • States need effective measures to determine if candidates are data literate.

    • The results of these measures and the support and development teachers and leaders receive should help determine if a candidate is recommended for licensure.

    The Role of IHEs and Licensure Agencies Standards

    Ellen Mandinach, Edith Gummer, & Jeremy Friedman


    The role of ihes and licensure agencies mandinach gummer friedman wested
    The Role of IHEs and Licensure Agencies – Mandinach, Gummer, & Friedman, WestEd

    • Three Projects: The Foundation for our Thinking

      • Spencer Foundation convening

      • Gates Foundation data literacy conference

      • Dell Foundation schools of education project


    Take home messages from prior work
    Take Home Messages from Prior Work Gummer, & Friedman, WestEd

    • Lack of clarity in the terminology – data literacy means different things to different people

    • Developmental continuum for educators’ acquisition of data literacy skills and knowledge is unknown

    • Process to elevate the importance to schools of education to have them help build human capacity is complex

      • How best to integrate data literacy into higher education – stand-alone or cross program?

      • Courses or integrated suites of courses?

    • Professional development is not enough

    • Recognition of the systemic nature of the issue


    The dell project objective
    The Dell Project: Objective Gummer, & Friedman, WestEd

    • To understand how many and what kinds of courses and experiences are being offered in schools of education that help prepare educators to use data.


    The dell project the survey
    The Dell Project: The Survey Gummer, & Friedman, WestEd

    • Objective – Examine what schools of education are doing to enhance teachers’ data literacy

      • Response rate: 24.9 percent (208 out of 836). [26.8 percent / 38 out of 142]

      • Respondents were from 47 states, DC, and the Virgin Islands.

      • Enroll between 51,840-96,543 pre-service teacher candidates.

      • 67.3 [68.4] percent are public colleges or universities (this reflects the second sample).

      • 83.7 percent offer teaching candidates bachelor’s degrees, 76.4 percent offer master’s degrees.


    The dell project syllabus review
    The Dell Project: Syllabus Review Gummer, & Friedman, WestEd

    • Purpose – To drill down to see what courses address


    The dell project licensure requirements
    The Dell Project: Licensure Requirements Gummer, & Friedman, WestEd

    • Purpose – To examine existing licensure and certification requirements for data literacy skills

    • Collaborators – The Data Quality Campaign, NASDTEC


    Survey results
    Survey Results Gummer, & Friedman, WestEd

    • 91.1 [90.6] percent claim that a focus on use of data is a sustained component of their teacher prep program in all or multiple courses.

    • 45.7 [32.0] percent plan on developing and implementing at least one new course focused on use of data.

    • Note: “Don’t know” responses were not calculated into percentages for any survey results slides.


    Survey results what they re doing stand alone course
    Survey Results – What they’re doing Gummer, & Friedman, WestEdStand-Alone Course

    • 24.1 [26.3] percent claim to have one stand-alone use of data course, 38.2 [42.1] claim to have multiple stand-alone courses.

      • 44.2 percent say the stand-alone course is a requirement for a teaching degree.

      • 47.6 percent say the target audience are pre-service teacher candidates.

      • 63.5 percent of the time the course’s instructor of record is tenured or tenure track.

      • 77.1 percent of the courses examine authentic data; 87.4 percent examine simulated data.

    Survey results what they re doing integrated course s
    Survey Results – What they’re doing Gummer, & Friedman, WestEdIntegrated Course(s)

    • 95.6 [97.0%] percent claim to have use of data integrated within existing courses.

      • Integrated most prominently into pedagogy and teaching methods courses.

      • Many respondents also stated data use was prominently addressed in assessment courses. Confusing data literacy for assessment literacy?

      • The course(s) instructors of record are most frequently tenured or tenure track professors.

      • 76.9 percent of the courses examine authentic data; 85.4 percent examine simulated data.

    Survey interpretations and caveats national
    Survey Interpretations and Caveats National Gummer, & Friedman, WestEd

    • Many schools did not respond.

      • Possible that some schools which did not participate did so because they do not have courses on data use.

    • Clear that most schools believe they are teaching data use, particularly integrated into other courses. Is this really the case?

    • Clear that data use is a focus among the responding schools. Or is it?

    Results from the syllabus review focus
    Results from the Syllabus Review - Focus Gummer, & Friedman, WestEd

    • 76% focused on design, implementation, and analysis of assessments that would be used at the individual student or classroom level

    • Secondary focus – formative assessments, state assessments, or assessment policy issues


    Results from the syllabus review assignments
    Results from the Syllabus Review - Assignments Gummer, & Friedman, WestEd

    • Lesson or unit plan with assignments

    • Analysis or writing of assessment items

    • ------------------------------------------------------

    • Summative assessment

    • Analysis of data

    • Rubric design

    • Formative assessment

      • classroom and individual students (benchmark or interim)

    • Statistical analysis

    • Case studies

    • Portfolio assessment


    Results from the licensure review general characteristics
    Results from the Licensure Review – General Characteristics

    • Amount of data-related skills (range across states)

    • Does it address data (12 states – no)

    • Does it address assessment (2 states without)

    • Does it list specific skills (7 states without)

    • How specific are the statements (range across states)

    • InTASC (6 states)

    • Developmental continuum (7 states)

    • Specific data standard (8 states)

    • Danielson (1 state)

    • Data literacy (22 states) vs. assessment literacy (37 states)


    Results from the licensure review skills 59
    Results from the Licensure Review – Skills (59) Characteristics

    • Average number of states per skill = 18.61; s.d. = 11.06

    • Average number of skills per state = 21.3; s.d. = 13.8 [NJ – 19; PA – 15; DE – 49 (InTASC); MD – 12; DC – 10; VA – 20; WV – 21; KY – 24; TN – 20]

    • Most frequent skills: assess, collaborate, plan, evaluate, monitor, communicate, use multiple sources, involve stakeholders, make decisions, document/review, provide feedback, self-assess, adjust, analyze, use data, collect/ gather, interpret


    Results from the licensure review skills
    Results from the Licensure Review - Skills Characteristics

    • Moderately frequent skills: identify, adapt, use technology, inquiry, reflect, question, differentiate, access, implement, design, ethics, use research, disaggregate

    • Least frequent skills: individualize, use statistics, act, summarize, predict/ hypothesize, synthesize, solve problems, develop assessments, integrate, review, process, infer


    Results from the licensure review local highlights
    Results from the Licensure Review – Local Highlights Characteristics

    • DE – strong data and data literacy emphasis

    • DC – data standard but really about assessment

    • MD – more about assessment literacy

    • NJ – little specifics, more on assessment

    • PA – more about assessment literacy

    • KY – more about assessment literacy

    • TN – has a data standard

    • VA – strong data emphasis

    • WV – quite specific, more on assessment


    Data quality campaign survey results 2013
    Data Quality Campaign Survey Results - 2013 Characteristics

    • 19 states with licensure policies, including DE, KY, MD, TN, & VA

    • DE is considered a “leading” state

    • KY and VA considered “growing” states


    Poll 4 reality check
    Poll 4: Reality Check Characteristics

    • From your perspective do these results reflect the reality of what’s going on in your states?

      • Yes

      • No

      • Unsure


    Teacher and Leader Data Literacy Characteristics

    Elizabeth N. Farley-Ripple

    School of Education

    University of Delaware

    Context and impetus for change
    Context and Impetus for Change Characteristics

    • External

    • Delaware ahead of the curve in data (DQC)

    • RTTT investment in data coaches (Amplify)

    • DE DOE imposing regulations based on CCSSO report

    Teacher and Leader Data Literacy

    • Internal

    • Shift in approach to staffing courses

    • Graduate

    • Ed Leadership faculty research in EBDM

    • Emerging teacher leadership program

    • Undergraduate

    • Internal data and new performance assessment

    • Change in program structure

    Data literacy efforts what are we do ing
    Data Literacy Efforts: What are we do Characteristicsing?

    • Undergraduate

    • Shift in assessment course from strictly assessment/measurement toward how you are using that information to make instructional decisions

    • Baby steps toward bringing in more than assessment data

    Teacher and Leader Data Literacy

    Bridging pre-service and in-service training, differentiated to roles and responsibilities

    • Graduate

    • MEd in Teacher Leadership forthcoming with two courses to help teacher leaders to understand, manage, and use data for student assessment, instructional planning, and school improvement

    • EdDprogram: program revision with 12 credits dedicated to data and evidence based decision-making (focus on secondary data, research use, and collecting data to identify, diagnose, and solve problems)

    How is making this happen
    How is making this happen? Characteristics

    What’s working

    What’s still challenging


    Need for dialogue across content areas


    Academic freedom (to be respected!) and other traditions


    Need for faculty buy-in


    Cooperating teachers

    Testing culture

    Lack of clear standards for data literacy

    Lack of consequential external demands

    Teacher and Leader Data Literacy

    • Structure

      • School of Education has no silos so faculty time can flow between programs

    • Culture

      • Culture of being proactive and responsive to external demands

    • Leadership

      • Program coordinators use levers - such as external demands and faculty representation in national dialogue - to achieve goals

    Takeaways Characteristics

    Teacher and Leader Data Literacy

    • Higher education may be hard to move but it is possible!

    • Internally, structures, culture and leadership can support change

    • Externally, national dialogue, consumer demand, and regulation are important levers

    Thank you!

    University of Delaware School of Education

    Elementary Teacher Education

    M.Ed. In Teacher Leadership

    Ed.D. in Education Leadership

    What needs to happen
    What Needs to Happen? Characteristics


    • Schools of education need to discuss how to introduce data literacy

    • Licensure agencies need to be more explicit

    • Discussions about what if Praxis includes data literacy

    • Discussions among stakeholders about how to make the integration happen


    Discussion Characteristics

    • How will the certification agencies respond?

    • Who are your partners in the effort to reform and change?

    • What are your biggest challenges?


    Discussion Characteristics

    • Do you think schools of education want to change?

    • Do you think schools of education will change?


    Continuing efforts
    Continuing Efforts Characteristics

    • What is the difference between elephants mating and establishing the importance of data literacy?

    • Photo by Ellen Mandinach and Eli Gruber


    Next steps
    Next Steps Characteristics

    • https://www.surveymonkey.com/s/TeacherDataLiteracyWebinar

    • Questions and ideas for followup

    • Closing comments from Caitlin Howley, ARCC and Marty Orland, MACC