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Leone, Elizabeth L. – ProQuest LLC, 2023
Data collection and analyzation practices for English language development services are scarcely found in research, but needed in the subgroup of minority students commonly known as English language learners (Wiseman & Bell, 2021). Wiseman and Bell (2021) identified ELLs as one of the most under-documented student subgroups in the American…
Descriptors: Data Collection, Data Analysis, Second Language Learning, English Language Learners
Anderson, Lorin W. – Education Policy Analysis Archives, 2018
In this summary article, six recommendations for the design, implementation, and interpretation of educational evaluations are presented and discussed. These recommendations are based on common "threads" that run through most, if not all, of the papers included in this special issue. The recommendations concern (1) the need for awareness…
Descriptors: Educational Assessment, Evaluation Methods, Program Design, Program Implementation
Cizek, Gregory J. – Assessment in Education: Principles, Policy & Practice, 2016
Advances in validity theory and alacrity in validation practice have suffered because the term "validity" has been used to refer to two incompatible concerns: (1) the degree of support for specified interpretations of test scores (i.e. intended score meaning) and (2) the degree of support for specified applications (i.e. intended test…
Descriptors: Scores, Definitions, Evaluation Utilization, Data Interpretation
Dahm, Kevin – Journal of STEM Education: Innovations and Research, 2014
ABET requires that engineering programs demonstrate continuous assessment and continuous improvement in order to be accredited. Central to the process is establishing and assessing measurable "student outcomes" that reflect whether the goals and objectives of the program are being met. This paper examines effective strategies for…
Descriptors: STEM Education, Scoring Rubrics, Grading, Assignments
Oren Pizmony-Levy; James Harvey; William H. Schmidt; Richard Noonan; Laura Engel; Michael J. Feuer; Henry Braun; Carla Santorno; Iris C. Rotberg; Paul Ash; Madhabi Chatterji; Judith Torney-Purta – Quality Assurance in Education: An International Perspective, 2014
Purpose: This paper presents a moderated discussion on popular misconceptions, benefits and limitations of International Large-Scale Assessment (ILSA) programs, clarifying how ILSA results could be more appropriately interpreted and used in public policy contexts in the USA and elsewhere in the world. Design/methodology/approach: To bring key…
Descriptors: Misconceptions, International Assessment, Evaluation Methods, Measurement
Ball, Samuel – ETS Research Report Series, 2011
Since its founding in 1947, ETS has conducted a significant and wide-ranging research program that has focused on, among other things, psychometric and statistical methodology; educational evaluation; performance assessment and scoring; large-scale assessment and evaluation; cognitive, developmental, personality, and social psychology; and…
Descriptors: Program Evaluation, Evaluation Research, Outcome Measures, Program Effectiveness
Knoeppel, Robert C.; Della Sala, Matthew R. – Educational Considerations, 2013
The purpose of this article is to introduce a new statistic to capture the ratio of equitable student outcomes given equitable inputs. Given the fact that finance structures should be aligned to outcome standards according to judicial interpretation, a ratio of outputs to inputs, or "equity ratio," is introduced to discern if conclusions can be…
Descriptors: Educational Opportunities, Outcomes of Education, Educational Objectives, Equal Education
Soule, Marcus – Science Scope, 2009
Examining data provides a unique opportunity to have students work actively with various technologies, such as computers or graphing calculators. Students can import data into spreadsheet software, execute mathematical calculations, create data graphs, and use this material in reports to present the results of their inquiry. Reinforcing the use of…
Descriptors: Earth Science, Data Interpretation, Technology Uses in Education, Science Instruction

Armstrong, Robert L.; Dusseau, Deborah Jeffries – Journal of School Improvement, 2001
Analyzes two possible assessment outcomes: (1) differing results on two or more assessments; and (2) loss of achievement after improvement cycle. Proposes short- and long-term solutions to problematic results, including analyzing differences in tests and pre-planning to avoid flaws in assessment design. (NB)
Descriptors: Data Interpretation, Differences, Educational Environment, Educational Objectives
Pettit, Michele L.; Fetro, Joyce V. – Health Educator, 2006
This article seeks to describe attributes of effective health educators by presenting the interrelationships between Stephen Covey's "Seven Habits of Highly Effective People" and the responsibilities and competencies proposed by the National Commission for Health Education Credentialing, Inc. A brief historical account of key figures and events…
Descriptors: Health Education, Change Agents, Goal Orientation, Leadership Effectiveness

Schmoker, Mike – Educational Leadership, 2003
Calls for simplicity when presenting data on student achievement. Data should help teachers improve teaching and learning, and focus on specific goals such as determining how many students are succeeding in a subject and, within that subject, what are the areas of strength or weakness. (Contains 22 references.) (WFA)
Descriptors: Academic Achievement, Data Analysis, Data Collection, Data Interpretation
Levesque, Karen; Bradby, Denise; Rossi, Kristi; Teitelbaum, Peter – 1998
This book discusses how to use everyday data to create strategies for educational improvement in schools. It provides users with a process for building a performance indicator system, identifying the most important aspects of a school's innovational efforts and traps to avoid that may lead to misinterpretation of data. The introduction identifies…
Descriptors: Data Analysis, Data Interpretation, Educational Assessment, Educational Improvement

Bernhardt, Victoria L. – Educational Leadership, 2003
A primer for schools attempting to analyze the data they collect. Describes ways schools can get a better picture of how to improve learning by gathering, intersecting, and organizing four categories of data more efficiently: (1) demographic data; (2) student-learning data; (3) perceptions data; and (4) school-processes data. (WFA)
Descriptors: Data Analysis, Data Collection, Data Interpretation, Data Processing
Elgin, Catherine Z. – Theory and Research in Education, 2004
I discuss the contributions of Harvey Siegel, Francis Schrag and Randall Curren to this volume. Their articles cast in bold relief the relation of High Stakes Testing to the goals of education, the nature of mind and the demands of justice. I argue that the connections are deep, but that the considerations these authors raise do not show that High…
Descriptors: Educational Objectives, High Stakes Tests, Data Interpretation, Reader Response
Corrallo, Sal – 1996
This report summarizes proceedings and conclusions of a two-day national planning workshop to further the assessment of national postsecondary outcomes, as suggested by Goal 6.5 of the National Education Goals, and to determine how the National Center for Education Statistics (NCES) and the states might work more effectively to develop…
Descriptors: College Outcomes Assessment, Data Collection, Data Interpretation, Educational Assessment
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