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data literacy in teacher preparation programs

Ellen B. Mandinach, WestEd October 10, 2013. data literacy in teacher preparation programs. What is Data-Literacy for Teachers?.

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data literacy in teacher preparation programs

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  1. Ellen B. Mandinach, WestEd October 10, 2013 data literacy in teacher preparation programs

  2. 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, 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. 2

  3. Components of Data Literacy for Teaching

  4. 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 4

  5. Data Literacy for Teaching: Inquiry Processes • Identify problems of practice • Frame questions • Understand the context • Generate hypotheses • Probe for causality • Evaluate outcomes • Determine next steps 5

  6. Data Literacy for Teaching: Habits of Mind and General Data Use • Think critically • Belief in data • Collaboration • Use data • Ethics of data use 6

  7. Data Literacy for Teaching: Data Quality and Data Properties • Understanding problematic data • Knowledge of psychometrics/assessments • Understanding data quality – accuracy • Identify and evaluate the right data • Qualitative data • Quantitative data • Understanding what data are not applicable • Use multiple measures • Use formative and summative assessments 7

  8. Data Literacy for Teaching: Data Use Procedural Skills • Use technologies to support data use • Find, locate, access, retrieve data • Generate data • Collect, gather, store data • Organize data • Prioritize data • Integrate data • Develop sound assessments 8

  9. Data Literacy for Teaching: Transform Data to Information • Transform data to information to actionable knowledge • Analyze data/use statistics • Interpret – make meaning • Assess patterns and trends • Understand data displays and representations • Synthesize diverse data • Summarize data/explain • Consider the impact/consequences 9

  10. Data Literacy for Teaching: Transform Data to Implementation • Pedagogical data literacy/instructional adjustments • Diagnose/monitor • Plan/guide/design • Adapt/modify/individualize/differentiate • Adjust/apply • Transform data into implemented decision 10

  11. A Challenge to Consider • To the Schools of Education: • How many of these skills are taught in your courses? • To the K-12 Partners: • How many of these skills do your educators use? • For how many of these skills do your educators need training? 11

  12. 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. 12

  13. The Dell Project: Components • Survey • Syllabus Review • Case Studies (on hold) • Licensure and Certification Requirements Review • Outreach 13

  14. The Dell Project: The Survey • Original Objective – Examine what schools of education are doing to enhance educators’ data literacy • Revised Objective – Examine what schools of education are doing to enhance teachers’ data literacy 14

  15. The Dell Project: Licensure Requirements • Purpose – To examine existing licensure and certification requirements for data literacy skills • Collaborators – The Data Quality Campaign, NASDTEC 15

  16. The Dell Project: Licensure Requirements Methods • Location of documents – Not easy • Help from NASDTEC • Search techniques – data, assessment • Coding scheme • State x skill; state by outcome; state x general characteristics 16

  17. The Dell Project: The Survey – Implications for the Change in Focus • Use of data for instructional purposes moves to the front of the focus – Formative assessment • Leaves us with a continuing problem of how to distinguish assessment literacy from data literacy • Makes seeing the difference between assessment courses and courses that prepare teachers to use other data problematic • Decrease attention to other sources of data • Missing an essential group that may be more the focus of higher education than teachers (i.e., preparation of administrators) 17

  18. Survey Results – Who they are • Response rate: 24.9 percent (208 out of 836). [21.3%; 3/61] • Respondents were from 47 states, DC, and the Virgin Islands. • Enroll between 51,840-96,543 pre-service teacher candidates. • 67.3 percent are public colleges or universities (this reflects the second sample). [84.6% with 100% local/state placement] • 83.7 percent offer teaching candidates bachelor’s degrees, 76.4 percent offer master’s degrees. • 91.1 [100%] 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 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. 18

  19. Survey Results – What they’re doingStand-Alone Course • 24.1 [30.8%] percent claim to have one stand-alone use of data course, 38.2 [38.5%] 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 tenure or tenure track professor. • 77.1 percent of the courses examine authentic data; 87.4 percent examine simulated data.

  20. Survey Results – What they’re doingIntegrated Course(s) • 95.6 [90.9%] 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.

  21. Survey Interpretations and Caveats National • Many schools did not respond. • Too many queries • Not enough interest • Residual from NCTQ • Perceived “witch-hunt” and “gotcha” • Possible that some schools who 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?

  22. 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 22

  23. 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 23

  24. Results from the Syllabus Review – Course Week Analysis • Assessment and measurement • --------------------------------------------- • Analytic processes • Statistics • Data topics • Data-driven decision making topics 24

  25. Results from the Syllabus Review – Potential Cases • Special Education courses • One course that integrated university course work with time out in the school • Only course that indicated the use of Student Information Systems or dashboards and data warehouses that are in use in a school

  26. Results from the Licensure Review – General Characteristics • Amount of data-related skills (range) • Does it address data (12 – no) • Does it address assessment (2 without) • Does it list specific skills (7 without) • How specific are the statements (range) • InTASC (6) • Developmental continuum (7) • Specific data standard (8) • Danielson (1) • Data literacy (22) vs. assessment literacy (37) 26

  27. Results from the Licensure Review – Outcomes (20) • Average number of states per outcome = 8.33; s.d. = 3.31 • Most frequent outcomes – assessment, instruction, student progress, student learning • Least frequent outcomes – program effectiveness, learning gains, accountability, guidance, readiness 27

  28. Results from the Licensure Review – Skills (58) • Average number of states per skill = 18.61; s.d. = 11.06 • Average number of skills per state = 21.3; s.d. = 13.8 • 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 28

  29. Results from the Licensure Review - Skills • Moderate 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 29

  30. Poll • Do these skills make sense to you? 30

  31. Questions • If you are a from a school of education, what steps do you need to take to begin to integrate data literacy skills into existing or stand-alone courses? • What makes more sense, the integrated approach or stand-alone courses? 31

  32. 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 32

  33. The Data Quality Campaign Metaphor: The Hammer Versus the Flashlight 33

  34. One Final Poll • Now that you know what data literacy means, do you really have a course or integrated suite of courses that pertain to data literacy? • Do you have an intent to develop such courses? 34

  35. The Dell Project: What Happens Next • What is the difference between elephants mating and establishing the importance of data literacy? • Photo by Ellen Mandinach and Eli Gruber 35

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