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Meholick, Sarah; Honey, Rose; LaTurner, Jason – National Center for Education Statistics, 2023
Statewide longitudinal data systems (SLDSs) can enable researchers, policymakers, and practitioners to identify and understand important relationships and trends across the education-to-workforce continuum. A well-developed SLDS can increase state and territory governments' ability to establish more informed and equitable policies, enable agency…
Descriptors: Longitudinal Studies, State Programs, State Policy, Data Collection
Data Quality Campaign, 2014
This publication provides an overview of the current federal opportunities to help states advance their data-related activities. Although not exhaustive, this list provides a starting point for federal policymakers to support states' work in this area.
Descriptors: Federal Aid, Data Collection, Data Processing, Information Management
Odom, John E. – Journal of Educational Data Processing, 1971
Descriptors: Computer Oriented Programs, Cooperative Planning, Data Processing, Federal Aid
Molnar, Andrew R.; Sherman, Beverly – Educ Technol, 1969
Descriptors: Computer Assisted Instruction, Computer Oriented Programs, Computers, Data Processing
Department of Education, Washington, DC. Student Financial Assistance. – 2000
This workbook is intended to assist schools that participate in the William D. Ford Direct Loan Program gain the necessary skills to reconcile their loan records with U.S. Department of Education data each month. After an introduction that offers general information, establishes course goals and objectives, and sets out the course agenda, the…
Descriptors: Data Processing, Federal Aid, Federal Programs, Higher Education
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School Management, 1972
Schools in New Jersey have found that by putting their heads together, they can slash the whopping costs of computers. (Author)
Descriptors: Computer Assisted Instruction, Computer Science Education, Computers, Cooperative Programs
St. John, Edward P. – CAUSE/EFFECT, 1985
To utilize automated technology for student aid processing, it is necessary to work with multi-institutional offices (student aid, admissions, registration, and business) and to develop automated interfaces with external processing systems at state and federal agencies and perhaps at need-analysis organizations and lenders. (MLW)
Descriptors: Data Processing, Federal Aid, Higher Education, Information Systems
Office of Education (DHEW), Washington, DC. – 1966
THE OPERATIONS AND PROGRAMS CONDUCTED UNDER TITLE I OF THE ELEMENTARY AND SECONDARY EDUCATION ACT FOR THE EDUCATION OF DISADVANTAGED CHILDREN ARE DESCRIBED IN A SUMMARY OF THE INDIVIDUAL REPORTS SUBMITTED BY 50 STATES, THREE TERRITORIES, AND THE DISTRICT OF COLUMBIA. THE REPORTS, PREPARED IN RESPONSE TO A U.S. OFFICE OF EDUCATION (USOE) REQUEST…
Descriptors: Compensatory Education, Data Processing, Educational Programs, Educationally Disadvantaged
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Smiley, James; Beasley, Gary – Delta Pi Epsilon Journal, 1973
Federal funds are available to encourage the development of programs involving cooperative efforts by education and business and industry to design and implement inservice occupational experience programs for business teachers. (Authors)
Descriptors: Business Education Teachers, Data Processing, Federal Aid, Inservice Education
Stark, Joan S. – 1975
Consumerism has become an important force in postsecondary education. This document points out the need of trustees to delineate an ethical relationship between the consumer of education and the institution. The document begins by suggesting proposals for state and federal regulation for consumer protection. Some suggestions are: (1) publication…
Descriptors: Accreditation (Institutions), Consumer Education, Data Processing, Educational Improvement
Congress of the U.S., Washington, DC. House Committee on Education and Labor. – 1988
The U.S. House Subcommittee on Postsecondary Education of the Committee on Education and Labor met in Missoula, Montana for an oversight hearing on the student aid delivery system. An opening statement from Pat Williams is followed by a report of the Advisory Committee on Student Financial Assistance on the issue of multiple data entry (MDE)…
Descriptors: Contracts, Data Processing, Educational Finance, Educational Legislation
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Garcia, Robert; And Others – Government Information Quarterly, 1985
Seven articles by members of the Bureau of the Census discuss various aspects of the census process and uses of census figures. Areas which are discussed include the historical basis of the census; automation of the census process; access to census information and confidentiality; and census taking in developing nations. (CLB)
Descriptors: Access to Information, Automation, Census Figures, Confidentiality
Advanced Technology, Inc., Reston, VA. – 1983
The way that the U.S. Department of Education's Office of Student Financial Assistance (OSFA) should conduct its quality assurance function of monitoring a processing contractor is described. For the following quality assurance procedures, information is provided on the goal of the procedure, activities to be measured, measures, and the role of…
Descriptors: Accountability, Computer Software, Contracts, Data Processing
Advanced Technology, Inc., Reston, VA. – 1982
A framework for monitoring quality control (QC) of processor contracts administered by the Department of Education's Office of Student Financial Assistance (OSFA) is presented and applied to the Pell Grant program. Guidelines for establishing QC measures and standards are included, and the uses of a sampling procedure in the QC system are…
Descriptors: Accountability, Computer Software, Contracts, Data Processing
Advanced Technology, Inc., Reston, VA. – 1983
The issues, options, and procedures for annually measuring overall payment error in the Pell Grant program are specified in detail. Guidelines for establishing a definition of Pell Grant payment error are provided, and the design issues related to error measurement are examined. A comparison is made of options for selecting a study sample and for…
Descriptors: Accountability, Compliance (Legal), Costs, Data Collection
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