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Duprey, Michael A.; Pratt, Daniel J.; Wilson, David H.; Jewell, Donna M.; Brown, Derick S.; Caves, Lesa R.; Kinney, Satkartar K.; Mattox, Tiffany L.; Ritchie, Nichole Smith; Rogers, James E.; Spagnardi, Colleen M.; Wescott, Jamie D. – National Center for Education Statistics, 2020
This data file documentation accompanies new data files for the High School Longitudinal Study of 2009 (HSLS:09) Postsecondary Education Transcript Study and Student Financial Aid Records Collection (PETS-SR). HSLS:09 follows a nationally representative sample of students who were ninth-graders in fall 2009 from high school into postsecondary…
Descriptors: Longitudinal Studies, High School Students, Sampling, Data Collection
Rihák, Jirí – International Educational Data Mining Society, 2015
In this work we introduce the system for adaptive practice of foundations of mathematics. Adaptivity of the system is primarily provided by selection of suitable tasks, which uses information from a domain model and a student model. The domain model does not use prerequisites but works with splitting skills to more concrete sub-skills. The student…
Descriptors: Mathematics Achievement, Mathematics Skills, Models, Reaction Time
Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol – Grantee Submission, 2015
Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…
Descriptors: Educational Environment, Predictive Measurement, Predictor Variables, Cooperative Learning
Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol – International Educational Data Mining Society, 2015
Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…
Descriptors: Educational Environment, Predictive Measurement, Predictor Variables, Cooperative Learning
Nord, C.; Hicks, L.; Hoover, K.; Jones, M.; Lin, A.; Lyons, M.; Perkins, R.; Roey, S.; Rust, K.; Sickles, D. – National Center for Education Statistics, 2011
This user's guide documents the procedures used to collect, process, and summarize data from the 2009 High School Transcript Study (HSTS 2009). Chapters detail the sampling of schools and graduates (chapters 2 and 3), data collection procedures (chapter 4), data processing procedures (chapter 5), and weighting procedures (chapter 6). Chapter 7…
Descriptors: High School Graduates, Academic Records, National Competency Tests, Questionnaires
Ewell, Peter; L'Orange, Hans – State Higher Education Executive Officers, 2009
A rapidly changing global economy, shifting demographics, and concerns about our ability to maintain a competitive workforce have focused national attention on the educational systems of America's states, highlighting their critical role in ensuring a productive and creative future for our country. As a result, Americas colleges and universities…
Descriptors: Higher Education, Elementary Secondary Education, Information Management, Design Requirements
Shettle, Carolyn; Cubell, Michele; Hoover, Katylee; Kastberg, David; Legum, Stan; Lyons, Marsha; Perkins, Robert; Rizzo, Lou; Roey, Stephen; Sickles, Diane – US Department of Education, 2008
This technical report documents the procedures used to collect and summarize data from the 2005 High School Transcript Study (HSTS 2005). The transcript studies serve as a barometer for changes in high school graduates' course-taking patterns; these patterns provide information about the rigor of high school curricula followed across the nation.…
Descriptors: Check Lists, High Schools, School Activities, Course Selection (Students)
Ingels, Steven J.; Pratt, Daniel J.; Wilson, David; Burns, Laura J.; Currivan, Douglas; Rogers, James E.; Hubbard-Bednasz, Sherry – National Center for Education Statistics, 2007
This manual has been produced to familiarize data users with the procedures followed for data collection and processing for the base year through second follow-up of the Education Longitudinal Study of 2002 (ELS:2002). It also provides the necessary documentation for use of the data files, as they appear on the ELS:2002 base-year to second…
Descriptors: Longitudinal Studies, High School Students, Research Design, Data Collection
Patrick, Ertha – 2000
This report examines college enrollment of students working and/or attending public postsecondary institutions in Texas for the Austin Independent School District (AISD) for the 1997-1998 senior cohort. College enrollment percentages of former students are presented as one indicator of AISD's success in preparing students for the future. The…
Descriptors: Academic Records, Data Processing, Enrollment, Followup Studies
Roey, Stephen; Caldwell, Nancy; Rust, Keith; Hicks, Lloyd; Lee, Janice; Perkins, Robert; Blumstein, Eyal; Brown, Janis – National Center for Education Statistics, 2005
This technical report documents the procedures used to collect and summarize data from the 2000 High School Transcript Study (HSTS 2000). Chapters in the report detail the sampling of schools and students (chapters 2 and 3), data collection procedures (chapter 4), data processing procedures (chapter 5), and weighting procedures (chapter 6).…
Descriptors: Research Reports, Data Processing, Sampling, High Schools
Jones, Calvin; And Others – 1983
This data file user's manual documents the procedures used to collect and process high school transcripts for a large sample of the younger cohort (1980 sophomores) in the High School and Beyond survey. The manual provides the user with the technical assistance needed to use the computer file and also discusses the following: (1) sample design for…
Descriptors: Academic Records, Data Analysis, Data Collection, Data Processing

Pfeiffer, Jay J. – Journal of Vocational Education Research, 1994
Florida's Education and Training Placement Information Program links records of former students to databases of state and federal agencies to obtain follow-up data on employment, military service, and postsecondary education. Results are provided to counselors, teachers, and policymakers for planning, evaluation, and guidance. (SK)
Descriptors: Academic Records, Data Processing, Databases, Followup Studies
American Association of Collegiate Registrars and Admissions Officers, Washington, DC. – 1993
It is common for information about students to be transferred from one learning institution to another or to some other location. The ease with which student records can be transferred and understood often depends on their design, the efficiency of the delivery system, and how the information is handled by the receiver. The Standardization of…
Descriptors: Academic Records, Data Processing, Elementary Secondary Education, Higher Education

Dixon, Jon M.; And Others – Journal of Continuing Education in Nursing, 1975
Descriptors: Academic Records, Attendance Records, Computers, Data Collection
White, Stephen R. – 1975
This document describes the computerized procedures developed and utilized by Montgomery College (Maryland) to maintain records related to student academic progress. The new system was fully operable by fall 1972. Systems development included: (1) the conversion of past student records to a permanent grade history file; (2) the development of a…
Descriptors: Academic Records, Computers, Data Collection, Data Processing