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DATA LITERACY IN TEACHER PREPARATION PROGRAMS

Ellen B. Mandinach, WestEdOctober 10, 2013

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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.

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Components of Data Literacy for Teaching

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Data Literacy for Teaching: Categories of Skills

Inquiry ProcessesHabits of MindGeneral Data UseData QualityData PropertiesData Use Procedural SkillsTransform Data to InformationTransform Data to Implementation

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Data Literacy for Teaching: Inquiry Processes

Identify problems of practiceFrame questionsUnderstand the contextGenerate hypothesesProbe for causalityEvaluate outcomesDetermine next steps

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Data Literacy for Teaching: Habits of Mind and General Data Use

Think criticallyBelief in dataCollaboration

Use dataEthics of data use

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Data Literacy for Teaching: Data Quality and Data Properties

Understanding problematic dataKnowledge of psychometrics/assessmentsUnderstanding data quality – accuracy

Identify and evaluate the right dataQualitative dataQuantitative dataUnderstanding what data are not applicableUse multiple measuresUse formative and summative assessments

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Data Literacy for Teaching: Data Use Procedural Skills

Use technologies to support data useFind, locate, access, retrieve dataGenerate dataCollect, gather, store dataOrganize dataPrioritize dataIntegrate dataDevelop sound assessments

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Data Literacy for Teaching: Transform Data to Information

Transform data to information to actionable knowledge

Analyze data/use statisticsInterpret – make meaningAssess patterns and trendsUnderstand data displays and representationsSynthesize diverse dataSummarize data/explainConsider the impact/consequences

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Data Literacy for Teaching: Transform Data to Implementation

Pedagogical data literacy/instructional adjustments

Diagnose/monitorPlan/guide/designAdapt/modify/individualize/differentiateAdjust/applyTransform data into implemented

decision

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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?

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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.

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The Dell Project: Components

• Survey• Syllabus Review• Case Studies (on hold)

• Licensure and Certification Requirements Review

• Outreach

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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

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The Dell Project: Licensure Requirements

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

• Collaborators – The Data Quality Campaign, NASDTEC

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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

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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)

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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.

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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.

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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.

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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?

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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

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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

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Results from the Syllabus Review – Course Week Analysis

• Assessment and measurement---------------------------------------------• Analytic processes• Statistics• Data topics• Data-driven decision making

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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

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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)

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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

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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

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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

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Poll

• Do these skills make sense to you?

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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?

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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

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The Data Quality Campaign Metaphor: The Hammer Versus the Flashlight

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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?

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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

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