Glossary

Below is a list of common terms related to data for decision making, and their accompanying definitions. Have another definition for a term listed below? Or do you have a term that you would like added to our glossary? Please contact us at DDI@wested.org.

Topics

Data Culture Components


Data Coach
An educator who assumes responsibility within a school or a district for data use. The data coach serves as the leader, mentor, or facilitator for data team activities, and helps other educators within the school or district use data to inform their practice (see Love, Stiles, Mundry, & DiRanna, 2008).

Data Culture
An environment within a district or school that espouses the importance of using data to inform practice. The environment contains attitudes and values around data use, recognized behavioral norms and expectations for using data, and objectives for why data are to be used. A data culture is informed by a district-level or school-level vision for data use (from Mandinach & Jackson, 2012; see also Love, Stiles, Mundry, & DiRanna, 2008).

Data Team/Professional Learning Community
A group of educators in a school or district designated to collaborate, examine, and use data to inform practice, and help other educators use data (see Love, Stiles, Mundry, & DiRanna, 2008).

Distributed Leadership
The idea that leadership in a school does not reside solely in the principal, but that management and leadership can be disbursed across a number of educators to create a shared sense of ownership, responsibility, and leadership (from Mandinach & Jackson, 2012).

 


Turnkey Model
When an individual or small group of individuals in a school or a district receive professional development training and then provide the training to the rest of the school or district.

General Data Terms

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Data
Empirical information that educators use to make decisions. Data, in isolation, are meaningless to inform decision making. They are given meaning from contextual factors. Data can be both qualitative and quantitative.

Data-Driven Decision Making
The collection, examination, analysis, interpretation, and application of data to inform instructional, administrative, policy, and other decisions and practice (from Mandinach & Jackson, 2012).

Data Literacy
The ability to transform data into information and ultimately actionable knowledge through an iterative process of inquiry that includes collecting, examining, and analyzing data.

Data Literacy for Administrators
The ability to transform information into actionable administrative knowledge and practices by collecting, analyzing, and interpreting all types of data (e.g., administrative, personnel, finance, assessment, school climate, behavioral) to help determine what administrative steps need to be taken.

Data Literacy for Teaching
The ability to transform information into actionable instructional knowledge and practices by collecting, analyzing, and interpreting all types of data (e.g., assessment, school climate, behavioral, snapshot, longitudinal, moment-to-moment) to help determine instructional steps.Also known as pedagogical data literacy, data literacy for teaching combines an understanding of data with standards, disciplinary knowledge and practices, curricular knowledge, pedagogical content knowledge, and an understanding of how children learn (from Gummer & Mandinach, in press; Mandinach, Friedman, & Gummer, in press).


Data Literacy Skills
The skills and knowledge educators need in order to use data effectively in their practice. These include: inquiry processes, habits of mind, general data use skills, understanding of data quality, understanding of data properties, data use procedural skills, the transformation of data to useful information for decision making, and the transformation of data to implementation (i.e., instructional changes) (see Gummer & Mandinach, in press; Mandinach, Friedman, & Gummer, in press).

Data Ethics
The knowledge of how to use data in a responsible, appropriate, and legal manner.

Data Privacy
The protection of student data from inappropriate and unethical use. See FERPA below.

Data Quality
Characteristics of the data that include accuracy, timeliness, reliability, and validity.

FERPA (The Family Educational Rights and Privacy Act)
A law that protects the use of student data and educational records. The law provides certain access by parents and adult students to educational records, with the objective of protecting the privacy of the student. The U.S. Department of Education website outlines FERPA here.

 

Requisite Skills and Knowledge

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Analytics
The process of examining data to identify patterns and trends.

Collaborative Inquiry
The coming together of educators to pose questions, collect and examine data, discuss results, and use them to inform their practice.

Cycle of Inquiry
The data analytic process that is led by framing questions or forming hypotheses; collecting, examining, analyzing, and interpreting data; and then making a decision based on the process to inform practice. The cycle of inquiry is iterative and ongoing.

Data Mining
The process of examining datasets to inform practice. Typically used for large datasets.

Data Use for Learning
Data that provide indicators of what students have learned or not learned so that the information can help inform instruction.

Data Use for Teaching/Instructional Uses of Assessment
The use of diverse data to inform the instructional process, including test results, student performance, classroom activities, affective variables, attendance, and behavior.

Pedagogical Content Knowledge
A concept introduced by Lee Shulman (1985) in which teachers’ knowledge of a particular topical area or domain is merged with their knowledge of pedagogy or instruction, resulting in their ability to know how to adapt instruction on a topic for particular students.

 

Statistics and Psychometrics

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Central Tendency
The center, middle, or average values that are measured by the arithmetic mean, mode, and median.

Dispersion
The distribution of scores, measured by standard deviation and variance.

Error of Measurement
The difference between the actual value of a quantity and what is collected through the measurement of that variable.

Growth Models
Statistical procedures to measure the development of students on one or more outcomes over time.

Predictive Modeling
The process of predicting the likelihood of an outcome.

Range
The span of scores from the lowest to the highest.

Regression to the Mean
A statistical concept in which high scores and low scores tend to converge on or gravitate toward the average score.

Reliability
The consistency of measurement. For example, the same test given twice should yield similar results. Two observers looking at the same activity should yield similar ratings.

Statistical Methodology
The processes by which data are designed, collected, organized, analyzed, interpreted, and presented.

Triangulation
The bringing together of disparate sources of data or information.

Validity
The extent to which a test or work product measures what it purports to assess. Validity is not only a property of the test but also of the interpretation of the results (see Cronbach, 1971, 1988, 1989; Messick, 1988, 1989).

Value Added
One model of teacher evaluation in which the test scores of a teacher’s current students are compared with the test scores those same students achieved in previous school years to provide one measure of the contribution of the teacher to students’ learning. Value-added measures can also be applied to principal and school evaluations by aggregating the data across all of the classes in the school.This form of measuring student growth is generally considered more informative than simple reporting of static scores of students, classes, or schools each year.

 

Technology

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Assessment System
A technology application that enables educators to create tests and then score, analyze, and report the results (see Wayman, 2005, 2007).

Data Dashboard
A technology tool that organizes important data elements in one place, typically on a desktop, presenting these data through immediate, easily accessible, and understandable graphical representations (from Mandinach & Jackson, 2012).

Data Warehouse
A technology-based repository of data that collects and manages the data from a variety of sources within a school district or state department of education. Some data warehouses also include the capacity to analyze and report the data.

Instructional Management System
A technology-based tool that helps educators design and structure their instruction, using the data to inform that instruction (see Wayman, 2005, 2007).

Interoperability
The capacity of different data systems to communicate with one another.

Other Data Systems
Other technologies to support data use include: handheld devices, diagnostics and monitoring tools, interactive whiteboards, classroom response systems, virtual learning environments and assessments, web-based tutoring tools, computer adaptive testing, universal design for learning and assessment, personalized learning systems, vodcasting (video podcasting), and visual data analysis tools. New technologies are constantly emerging.

Statewide Longitudinal Data System (SLDS)
A repository of data, located in each state education agency, with data collected from schools and districts, as well as state data. The SLDSs manage, analyze, and use education data, including data at the student, school, and district levels. The system communicates accountability data to the U.S. Department of Education (see http://nces.ed.gov/programs/slds/). See Longitudinal Data for more information.

Student Information System (SIS)
A technology tool that helps districts and schools collect and manage student data (see Wayman, 2005, 2007).

 

Types of Assessment

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Benchmark/Interim Assessment
Tests administered periodically throughout the school year to measure progress based on the curricula. Results can be examined at the class, school, or district levels to determine progress over time.

Common Assessments
Tests that are given uniformly within a state, district, or school.

Formative Assessment
Not a test per se, but rather a process of measurement. The process yields feedback to educators that is periodic and moment-to-moment with the objective of informing the teaching and learning process. The formative assessment process provides educators with short-cycle data that facilitate modifications to instruction.

Summative Assessment
A test that measures the culmination of learning from a course, a topic unit, a marking period, or a school year. Summative assessments measure student learning, knowledge, and skills for a particular end point in time (from Mandinach & Jackson, 2012).

 

Types of Data

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Data Elements/Indicators
Variables that are collected for the decision-making process.

Longitudinal Data
Data that are comprised of information for the same unit (e.g., students, teachers) over multiple points in time.

Moment-to-Moment Data
Data that have a short feedback cycle between instruction and data collection. These data occur quickly in interactions between student and teacher or between students, and are often part of the formative assessment process.

Qualitative Data
Data that approximately or characterizes but does not measure the attributes of a thing or occurrence. Qualitative data describe.

Quantitative Data
Data that can be quantified and are amendable to statistical manipulation. Quantitative data define.

Snapshot Data
Data that focus on performance at a particular point in time.

 

Other

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Bubble Kids
Students that are on the cusp of either side of the cut score for passing or failing. They are on the “bubble” of either being considered proficient or not proficient.

Data Use in Action
Data use applied to address a particular, pressing education question, issue, or topic, such as college readiness, teacher evaluation, or closing the achievement gap.

Foundational Research
Research that focuses on the components of data-driven decision making, such as data systems, data teams, data coaches, creating a data culture, vision, and the need for leadership.

Learning Analytics
The process of collecting and analyzing data in multiple categories from learners for multiple purposes.One purpose is to predict future student performances and identify students at risk of failure so that interventions can be planned, implemented, and evaluated. A second purpose is to develop better understanding of a student’s learning and to optimize subsequent learning experiences and environments.


Learning Progressions
The pathway a student takes toward achieving competence or mastery in a specific topical area, content domain, or skill set. The pathways are defined by specific skills identified through intensive analyses of the domain.

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