What Does it Mean for Teachers to be Data Literate: Skills, Knowledge, and Dispositions

Integrating Data Literacy into Teacher Preparation

Data use in education has gained increasing prominence across various levels, from school-wide improvement strategies to granular classroom and instructional decision-making. Despite this emphasis, the field of education has historically struggled to provide a concrete and universally accepted definition of what it means to be a data literate educator. Traditionally, focus has been placed on "assessment literacy" due to the perceived salience of assessment results as the primary form of data. However, modern approaches seek to encompass all education data to provide a comprehensive depiction of students.

Definitions of Data Literacy

Historically, the field lacked a common definition for data literacy, with various stakeholders proposing different interpretations:

• The Data Quality Campaign (DQC): Defines data-literate educators as those who "continuously, effectively, and ethically access, interpret, act on, and communicate multiple types of data from state, local, classroom, and other sources to improve outcomes for students in a manner appropriate to educators' professional roles and responsibilities."

• North Carolina Department of Public Instruction: Defines data literacy as "one’s level of understanding of how to find, evaluate, and use data to inform instruction."

• Data Literacy for Teachers (DLFT): The authors define DLFT as "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."

The Three Core Propositions of Data Literacy

Research has led to three fundamental propositions regarding data literacy in education:

1. Arming Educators with Data: Policymakers internationally are pushing for evidence-based education, requiring data to inform practice and decisions.

2. The Problematic Conflation of Assessment and Data Literacies: There is a persistent misconception that "data" refers only to assessments. Data literacy is a more comprehensive construct that subsumes assessment literacy.

3. Inadequacy of Professional Development: Relying solely on professional development (PD) for practicing teachers is insufficient. PD providers often lack the subject-specific knowledge required to triangulate data with content and pedagogy. Data use must be introduced early in teacher preparation programs.

Policy and Research Context

Policy and Policymakers

Data-driven decision making (DDDM) has become a global priority. Research by Schildkamp, Lai, and Earl ($2013$) explores enablers and barriers to data enculturation across several European countries (the United Kingdom, Poland, Germany, Lithuania, and the Netherlands). While some reported basic data skills, none showed evidence of deep data literacy or comprehensive preparation training.

In the United States, significant initiatives have prioritized data use:

• The American Recovery and Reinvestment Act (ARRA, $2009$) and Race to the Top identify data use as a central pillar.

• Arne Duncan: Former Secretary of Education who publically challenged schools of education to accelerate efforts in teaching educators how to use data.

• Professional Organizations: The Council for Accreditation of Educator Preparation (CAEP), the Council of Chief State School Officers (CCSSO), and the National Board of Professional Teaching Standards (NBPTS) all advocate for data literacy.

The InTASC Standards

CCSSO ($2013, 2015$) released the Interstate New Teacher Assessment and Support Consortium (InTASC) standards, which are deeply infused with data literacy. A crosswalk of these standards identified $66$ data literacy elements. For example:

• Standard 9h: Teachers must know how to use learner data to analyze practice and differentiate instruction.

• Standard 10b: Teachers must work with other professionals to jointly facilitate learning on how to meet diverse learner needs.

Conceptual Framework for Data Literacy for Teachers (DLFT)

Evolution of the Framework

The framework was developed based on a convening of $55$ experts and an analysis of state licensure documents. The experts identified over $100$ sets of knowledge and skills, which were categorized into themes such as inquiry cycles, data quality, and the transformation of data into information. Licensure reviews showed a range of data-related requirements across states—from $0$ to $59$ specific skills.

Integration with Shulman’s Knowledge Domains

Data literacy cannot exist in a vacuum; it must be integrated with Shulman’s ($1986, 1987$) seven foundational knowledge areas for effective teaching:

1. Content Knowledge: Mastery of the subject matter.

2. General Pedagogical Knowledge: Teaching strategies and classroom management.

3. Curriculum Knowledge: Understanding materials and program structures.

4. Pedagogical Content Knowledge (PCK): Knowing how to represent and formulate specific subject matter for student understanding.

5. Knowledge of Learners and Their Characteristics: Understanding how children learn and their specific backgrounds (demographics, behavior, attendance).

6. Knowledge of Educational Contexts: Understanding the school environment, district regulations, and local community.

7. Knowledge of Educational Ends, Purposes, and Values: Understanding the goals of education and standards (e.g., Common Core State Standards, Next Generation Science Standards).

The Five Components of Data Use for Teaching

The DLFT framework organizes specific skills into five iterative components of an inquiry cycle.

1. Identify Problems and Frame Questions

Teachers must initiate inquiry by identifying specific problems of practice. Skills include:

• Articulating problems regarding students, groups, or curricula.

• Understanding context at both the student and school levels.

• Involving stakeholders (educators, parents, and students).

• Student Privacy: Understanding regulations regarding protection and confidentiality. This is critical as data breaches increase.

2. Use Data

This is the largest component, containing $27$ specific skills related to the mechanical and conceptual aspects of data handling:

• Identifying and Evaluating Sources: Aligning data with the problem of practice.

• Assessment Foundations: Understanding formative vs. summative assessments, test design, and implementation.

• Foundational Measurement: Understanding statistics (central tendency, dispersion) and psychometrics (reliability, validity, error of measurement).

• Data Characteristics: Using multiple measures, triangulating qualitative and quantitative data, and understanding data levels (item-level vs. composite).

• Technical Skills: Navigating data systems (dashboards, spreadsheets, warehouses) and performing operations like aggregating, disaggregating, cleaning, and drilling down into data.

3. Transform Data into Information

This stage moves raw data toward meaningful interpretation through $9$ identified skills:

• Hypothesis Generation: Projecting instructional impacts and testing assumptions.

• Interpretation and Representation: Using charts, tables, and graphs to identify patterns and trends.

• Causality and Inference: Probing for "the why" behind performance and synthesizing diverse data into coherent conclusions.

4. Transform Information into a Decision

Information must lead to instructional action. Skills include:

• Determining the next logical instructional steps.

• Diagnosing student strengths and weaknesses from performance over time.

• Monitoring ongoing performance to evaluate the need for adjustments.

• Contextualizing decisions within the existing curriculum and scope/sequence.

5. Evaluate Outcomes

This final stage reinforces the iterative nature of the cycle:

• Re-examining the original question to see if it was addressed.

• Comparing pre- and post-decision performance (e.g., comparing scores before and after a specific intervention).

• Monitoring changes in classroom practices and student behavior.

• Commencing new iterative cycles as decisions lead to more data collection.

Dispositions and Habits of Mind

While not included as primary skills in the framework, six dispositions are identified as essential for effective data use:

1. Belief that all students can learn.

2. Critical thinking and belief in data as a tool for efficacy.

3. Commitment to continuous inquiry.

4. Ethical behavior: Ensuring privacy and confidentiality/security.

5. Collaboration: Working vertically and horizontally across the school hierarchy.

6. Communication Skills: Adapting the discussion of empirical results for diverse audiences (parents, students, stakeholders).

Systemic Implications for Teacher Preparation

Timing and Method of Introduction

The authors advocate for introducing data literacy early in teacher education programs. Waiting until teachers are in the classroom is often too late as practices become less malleable.

• Integrated Approach: Data literacy should be woven through existing courses (Philosophy, Psychology, Methods) rather than solely relying on stand-alone courses. For example, an Educational Psychology course can provide datasets for interpretation instead of just presenting study findings.

• Triangulation: Schools of education are uniquely positioned to teach the triangulation of data skills with content and pedagogical knowledge.

Key Stakeholders in the System

• Schools of Education: The central players who must be responsive to emerging policy changes.

• State Education Agencies: Responsible for designing licensure requirements that include explicit data competencies.

• School Districts: Can exert pressure on universities to produce graduates with specific data skill sets.

• Testing Organizations: Major exams like Praxis, NOTE, and edTPA have introduced components requiring test-takers to demonstrate data use abilities.

• Federal Government: The U.S. Department of Education provided technological infrastructure but has done less to support individual educator capacity.

Future Research and Next Steps

1. Evolving Framework: The model must remain dynamic to include emerging topics like data security.

2. Developmental Continuum: Defining what data literacy looks like for a novice versus an expert, and determining the minimum acceptable level for all educators.

3. Role-Based Literacy: Examining how data needs differ for teachers, administrators, and specialists.

4. Material Development: Creating resources (like MOOCs or integrated course modules) to help university faculty retool their curricula with minimal resistance.

As the old joke goes: "How many psychologists does it take to change a light bulb? One, but the light bulb must want to change." For schools of education, the change toward data literacy must be driven by a genuine desire for improvement rather than just external accountability pressures.

Summary of Framework

The Data Literacy for Teachers (DLFT) framework was developed through collaboration among 55 experts and a thorough analysis of state licensure requirements, identifying over 100 sets of knowledge and skills. This framework categorizes essential data literacy components into five iterative components of an inquiry cycle:

1. Identify Problems and Frame Questions: Teachers initiate inquiry by defining specific problems of practice and understanding context, while involving stakeholders and ensuring student privacy.

2. Use Data: This component encompasses 27 specific skills related to data handling, including identifying sources, understanding assessments, data characteristics, and technical navigation of data systems.

3. Transform Data into Information: In this phase, raw data is interpreted through hypothesis generation, representation of data (charts, graphs), and understanding causality and inference.

4. Transform Information into a Decision: Teachers use the information gathered to determine instructional steps, diagnose student strengths and weaknesses, and adjust strategies based on performance.

5. Evaluate Outcomes: The final stage involves re-evaluating the original question, comparing performance changes pre-and post-decision, and initiating new inquiry cycles as a response to data findings.

The framework emphasizes that data literacy must be integrated with Shulman’s seven foundational knowledge areas for effective teaching, ensuring that data is not viewed in isolation.

Impact on teaching

The Data Literacy for Teachers (DLFT) framework impacts teaching positively in several ways:

1. Enhanced Instructional Decisions: By using data to inform decisions, teachers can tailor their instructional strategies based on actual student performance and learning needs, thus improving student outcomes.

2. Inquiry-Based Learning: The framework promotes an inquiry cycle, encouraging teachers to identify specific problems and questions related to student learning. This fosters a culture of continuous improvement in teaching practices.

3. Integration of Data Skills: Teachers are equipped with essential data handling skills, enabling them to navigate various data sources and assessments efficiently. This empowers them to utilize data in meaningful ways that enhance their teaching effectiveness.

4. Collaboration and Communication: The emphasis on involvement of stakeholders encourages collaboration among educators, parents, and students, fostering a supportive learning environment.

5. Continuous Evaluation and Adjustment: The framework's iterative nature promotes ongoing evaluation of instructional practices and outcomes, allowing teachers to make necessary adjustments based on data findings, ultimately leading to improved educational experiences.

The Data Literacy for Teachers (DLFT) framework significantly enhances teaching practices by promoting enhanced instructional decisions and fostering inquiry-based learning. By utilizing data to inform their instructional strategies, educators can tailor their approaches to meet the actual learning needs of students, leading to improved outcomes. Additionally, the framework encourages teachers to engage in an inquiry cycle, where they identify specific problems and questions related to student learning, thereby creating a culture of continuous improvement. This is further supported by the integration of essential data handling skills that empower teachers to efficiently navigate various data sources and assessments, facilitating meaningful data use. Moreover, the emphasis on collaboration and communication among stakeholders, including educators, parents, and students, fosters a supportive learning environment. The iterative nature of the framework also promotes ongoing evaluation and adjustment of instructional practices based on data findings, ultimately enhancing the educational experience.

Data Definition

Data literacy for educators is defined as the ability to transform information into actionable instructional knowledge and practices by collecting, analyzing, and interpreting all types of data to help determine instructional steps.

Mandinach and Gummer (2013) define data literacy for teachers as the capacity to recognise the importance of data in identifying student needs, adjusting instructional approaches, and ultimately improving student outcomes through informed decision-making. It is proposed that the Data Literacy for Teachers (DLFT) framework serves as a comprehensive guide for educators to systematically enhance their data literacy skills, ensuring they are equipped to effectively manipulate data in their teaching practices (Mandinach & Gummer, 2016). The framework begins with problems and framing questions as the first step in the data analysis process, guiding educators to identify specific challenges within their instructional context.