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Rodney McCrowre; Burcu Adivar – Industry and Higher Education, 2024
This study aims to analyse the impact of demographic and educational factors on in digital upskilling. We address the relationship between digital skills, critical thinking skills and the student learning experience in courses with embedded upskilling programs. Statistical analysis and exploratory research are used to analyse the data collected by…
Descriptors: Digital Literacy, Skill Development, Competence, Critical Thinking
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Siegel, Peter; Ramirez, Nestor; Johnson, Ruby – National Center for Education Statistics, 2021
This publication describes the methods and procedures used for the 2017-18 National Postsecondary Student Aid Study, Administrative Collection (NPSAS:18-AC). It also provides information that will be helpful to analysts in accessing and understanding the restricted-use files containing the NPSAS:18-AC data. NPSAS:18-AC includes cross-sectional,…
Descriptors: Student Financial Aid, College Students, Postsecondary Education, Institutional Characteristics
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Agley, Jon; Tidd, David; Jun, Mikyoung; Eldridge, Lori; Xiao, Yunyu; Sussman, Steve; Jayawardene, Wasantha; Agley, Daniel; Gassman, Ruth; Dickinson, Stephanie L. – Educational and Psychological Measurement, 2021
Prospective longitudinal data collection is an important way for researchers and evaluators to assess change. In school-based settings, for low-risk and/or likely-beneficial interventions or surveys, data quality and ethical standards are both arguably stronger when using a waiver of parental consent--but doing so often requires the use of…
Descriptors: Data Analysis, Longitudinal Studies, Data Collection, Intervention
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Edwards, John; Hart, Kaden; Shrestha, Raj – Journal of Educational Data Mining, 2023
Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as…
Descriptors: Data Analysis, Computer Science Education, Learning Analytics, Research Methodology
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Hunter, Gerald P.; Williamson, Stephanie; Wilks, Asa; Hanley, Janet M.; Stecher, Brian M. – RAND Corporation, 2020
The Intensive Partnerships for Effective Teaching initiative, which was funded by the Bill & Melinda Gates Foundation, was a multiyear effort to improve student outcomes--particularly high school graduation and college attendance among low-income minority students--by increasing student access to effective teaching. The RAND Corporation worked…
Descriptors: Data Collection, Data Use, Instructional Effectiveness, Teacher Effectiveness
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Kartal, Ozgul; Dunya, Beyza Aksu; Diefes-Dux, Heidi A.; Zawojewski, Judith S. – International Journal of Research in Education and Science, 2016
Critical to many science, technology, engineering, and mathematics (STEM) career paths is mathematical modeling--specifically, the creation and adaptation of mathematical models to solve problems in complex settings. Conventional standardized measures of mathematics achievement are not structured to directly assess this type of mathematical…
Descriptors: Mathematical Models, STEM Education, Standardized Tests, Mathematics Achievement
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Nichols, Timothy; Ailts, Jacob; Chang, Kuo-Liang – Honors in Practice, 2016
This study gathered, analyzed, and compared perspectives of students who were honors-eligible but never began the program, students who began in honors and discontinued their enrollment, and those who were persisting in honors. Broadly speaking (and not surprisingly), the responses of students persisting in honors reflected the most positive…
Descriptors: Higher Education, College Students, School Holding Power, Honors Curriculum
Hyslop, Anne – Education Sector, 2011
Today, there is a growing agreement that students should leave high school "college- and career-ready." But what does that mean? And how can high schools tell if they are meeting the goal? This analysis identifies four characteristics of the most successful college readiness reports. (Contains 3 charts, 1 figure and 25 notes.)
Descriptors: Feedback (Response), High Schools, Charts, College Readiness
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Cole, James S.; Gonyea, Robert M. – Research in Higher Education, 2010
Because it is often impractical or impossible to obtain school transcripts or records on subjects, many researchers rely on college students to accurately self-report their academic record as part of their data collection procedures. The purpose of this study is to investigate the validity and reliability of student self-reported academic…
Descriptors: Academic Records, College Students, Validity, Scores
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Varela, Otmar E.; Cater, John James, III; Michel, Norbert – Human Resource Development Quarterly, 2011
This study tests a process model of learning in which trainer and trainee traits are simultaneously considered as endogenous variables of learning outcomes. The article builds on a social view of training and similarity-attraction paradigms. In this context, the authors hypothesize that trainer-trainee similarity in personality (agreeableness)…
Descriptors: Evidence, Undergraduate Students, Personality Traits, Interpersonal Attraction
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von Davier, Alina A., Ed.; Liu, Mei, Ed. – ETS Research Report Series, 2006
This report builds on and extends existent research on population invariance to new tests and issues. The authors lay the foundation for a deeper understanding of the use of population invariance measures in a wide variety of practical contexts. The invariance of linear, equipercentile and IRT equating methods are examined using data from five…
Descriptors: Equated Scores, Statistical Analysis, Data Collection, Test Format