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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.

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Presentation on theme: "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."— Presentation transcript:

1 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

2 Welcome from MACC and ARCC Marty Orland, MACC Director DelawareDistrict of Columbia MarylandNew Jersey Pennsylvania Sharon Harsh, ARCC Director KentuckyTennessee VirginiaWest Virginia

3 Housekeeping Overall format of webinar Technical issues to 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 address if not registered

4 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.

5 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

6 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 6 Today’s Agenda

7 The Need to Improve Teacher Preparation and Data Literacy Emphasis from policymakers Emerging standards from CAEP and other professional organizations NCTQ 7

8 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 8

9 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. 9

10 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 10

11 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) 11

12 Systemic Nature of the Reform Issue Complex Interacting players Change cannot happen in isolation Change comes slowly 12

13 Who Are the Key Players State Education Agencies State Licensure Agencies Professional organizations Schools of education Testing organizations Local Education Agencies Others 13

14 A Metaphor from the Data Quality Campaign The Flashlight vs. The Hammer 14

15 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 15

16 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 16

17 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 17

18 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? 18

19 The Role of the SEA Janice Poda Education Workforce Council of Chief State School Officers (CCSSO)

20 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 20

21 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)

22 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)

23 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.

24 Determining Success 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.

25 The Role of IHEs and Licensure Agencies Ellen Mandinach, Edith Gummer, & Jeremy Friedman WestEd

26 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 26

27 Take Home Messages from Prior Work 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 27

28 The Dell Project: Objective 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. 28

29 The Dell Project: The Survey 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 [68.4] percent are public colleges or universities (this reflects the second sample) percent offer teaching candidates bachelor’s degrees, 76.4 percent offer master’s degrees. 29

30 The Dell Project: Syllabus Review Purpose – To drill down to see what courses address 30

31 The Dell Project: Licensure Requirements Purpose – To examine existing licensure and certification requirements for data literacy skills Collaborators – The Data Quality Campaign, NASDTEC 31

32 Survey Results 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 [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. 32

33 Survey Results – What they’re doing Stand-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 percent say the stand-alone course is a requirement for a teaching degree percent say the target audience are pre-service teacher candidates percent of the time the course’s instructor of record is tenured or tenure track percent of the courses examine authentic data; 87.4 percent examine simulated data.

34 Survey Results – What they’re doing Integrated 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 percent of the courses examine authentic data; 85.4 percent examine simulated data.

35 Survey Interpretations and Caveats National 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?

36 Results from the Syllabus Review - Focus 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 36

37 Results from the Syllabus Review - Assignments 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 37

38 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) 38

39 Results from the Licensure Review – Skills (59) Average number of states per skill = 18.61; s.d. = 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 39

40 Results from the Licensure Review - Skills 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 40

41 Results from the Licensure Review – Local Highlights 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 41

42 Data Quality Campaign Survey Results states with licensure policies, including DE, KY, MD, TN, & VA DE is considered a “leading” state KY and VA considered “growing” states 42

43 Poll 4: Reality Check From your perspective do these results reflect the reality of what’s going on in your states? Yes No Unsure 43

44 Teacher and Leader Data Literacy Elizabeth N. Farley-Ripple School of Education University of Delaware

45 External Delaware ahead of the curve in data (DQC) RTTT investment in data coaches (Amplify) DE DOE imposing regulations based on CCSSO report Context and Impetus for Change Internal Shift in approach to staffing courses Undergraduate Internal data and new performance assessment Change in program structure Graduate Ed Leadership faculty research in EBDM Emerging teacher leadership program Teacher and Leader Data Literacy

46 Data Literacy Efforts: What are we do ing ? Teacher and Leader Data Literacy 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 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 EdD program: 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) Bridging pre-service and in-service training, differentiated to roles and responsibilitie s

47 How is making this happen? What’s still challenging Teacher and Leader Data Literacy Structure Need for dialogue across content areas Culture Academic freedom (to be respected!) and other traditions Leadership Need for faculty buy-in Externa l Cooperating teachers Testing culture Lack of clear standards for data literacy Lack of consequential external demands What’s working 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

48 Takeaways 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 Teacher and Leader Data Literacy

49 What Needs to Happen? 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 49 Discussion

50 How will the certification agencies respond? Who are your partners in the effort to reform and change? What are your biggest challenges? 50

51 Discussion Do you think schools of education want to change? Do you think schools of education will change? 51

52 Continuing Efforts What is the difference between elephants mating and establishing the importance of data literacy? Photo by Ellen Mandinach and Eli Gruber 52

53 Next Steps https://www.surveymonkey.com/ s/TeacherDataLiteracyWebinar https://www.surveymonkey.com/ s/TeacherDataLiteracyWebinar Questions and ideas for followup Closing comments from Caitlin Howley, ARCC and Marty Orland, MACC


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