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Showing 1 to 15 of 26 results Save | Export
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Brown, Charles L. – Assessment Update, 2023
With increasing canonicity, particularly within higher education assessment, the demonstrable achievement of goals, or the delivery of a program, or as one scholar wryly deemed it, the "manipulation of the independent variable" (Moncher and Prinz 1991, p. 247), are commonly referred to as implementation fidelity or fidelity of…
Descriptors: Fidelity, Meta Analysis, Student Evaluation, Higher Education
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Cherrstrom, Catherine A.; Boden, Carrie J.; Sherron, Todd; Wilson, Lindsey – Journal of Continuing Higher Education, 2022
Many Americans dream of a college degree, but earning one requires time and money. As one solution, prior learning assessment (PLA) documents college-level learning gained outside the classroom for academic credit and can make the difference between earning or not earning a college degree. However, the literature lacks an integrative and…
Descriptors: Prior Learning, Student Evaluation, Literature Reviews, Meta Analysis
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Young-Suk Grace Kim; Dandan Yang; Jinkyung Hwang – Educational Psychology Review, 2024
Writing and mathematics are essential in academic achievement. In the present study, we investigated whether writing skills and mathematics skills are related and if so, whether their relation is moderated by participants' grade level (a proxy for developmental phase), subskills of mathematics and writing skills, and assessment characteristics…
Descriptors: Mathematics Skills, Writing Skills, Evaluation Methods, Instructional Program Divisions
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Fahd, Kiran; Venkatraman, Sitalakshmi; Miah, Shah J.; Ahmed, Khandakar – Education and Information Technologies, 2022
Recently, machine learning (ML) has evolved and finds its application in higher education (HE) for various data analysis. Studies have shown that such an emerging field in educational technology provides meaningful insights into several dimensions of educational quality. An in-depth analysis of the application of ML could have a positive impact on…
Descriptors: Artificial Intelligence, Electronic Learning, Higher Education, Academic Achievement
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Wiley, Jennifer L.; Wiley, Kristofor R.; Intolubbe-Chmil, Loren; Bhuyan, Devi; Acheson, Kris – Journal of Transformative Education, 2021
Transformative learning (TL) goals are becoming commonplace in higher education, continuing education, and other adult learning contexts; however, valid and reliable assessments of TL are not so common. This imbalance begs the development of assessment methods that allow for a deeper understanding of how, when, and why deep reshaping of self takes…
Descriptors: Evaluation Methods, Transformative Learning, Measures (Individuals), Values
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Harwell, Michael; Maeda, Yukiko; Bishop, Kyoungwon; Xie, Aolin – Journal of Experimental Education, 2017
Measures of socioeconomic status (SES) are routinely used in analyses of achievement data to increase statistical power, statistically control for the effects of SES, and enhance causality arguments under the premise that the SES-achievement relationship is moderate to strong. Empirical evidence characterizing the strength of the SES-achievement…
Descriptors: Correlation, Socioeconomic Status, Statistical Analysis, Academic Achievement
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Saltan, Fatih; Arslan, Ömer – EURASIA Journal of Mathematics, Science & Technology Education, 2017
Augmented Reality (AR) is recognized as one of the most important developments in educational technology for both higher and K-12 education as emphasized in Horizon report (Johnson et al., 2016, 2015). Furthermore, AR is expected to achieve widespread adoption that will take two to three years in higher education and four to five years in K-12…
Descriptors: Computer Simulation, Simulated Environment, Educational Technology, Literature Reviews
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Pereira, Diana; Flores, Maria Assunção; Niklasson, Laila – Assessment & Evaluation in Higher Education, 2016
A review of articles published in "Assessment and Evaluation in Higher Education," over the last eight years (2006-2013) on assessment in higher education, since the introduction of the Bologna process, is the subject of the paper. The first part discusses the key issue of assessment in higher education and the method used for selecting…
Descriptors: Higher Education, Meta Analysis, Literature Reviews, Evaluation
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Snell, Joel C.; Marsh, Mitchell – Journal of Instructional Psychology, 2011
The authors have over the years tried to revise meta-analysis because it's basic premise is to add apples and oranges together and analyze. In other words, various data on the same subject are chosen using different samples, research strategies, and number properties. The findings are then homogenized and a statistical analysis is used (Snell, J.…
Descriptors: Research Methodology, Statistical Analysis, Teacher Attitudes, Meta Analysis
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Schmid, Richard F.; Bernard, Robert M.; Borokhovski, Eugene; Tamim, Rana; Abrami, Philip C.; Wade, C. Anne; Surkes, Michael A.; Lowerison, Gretchen – Journal of Computing in Higher Education, 2009
This paper reports the findings of a Stage I meta-analysis exploring the achievement effects of computer-based technology use in higher education classrooms (non-distance education). An extensive literature search revealed more than 6,000 potentially relevant primary empirical studies. Analysis of a representative sample of 231 studies (k = 310)…
Descriptors: Higher Education, Research Design, Effect Size, Educational Technology
Costes, Nathalie; Crozier, Fiona; Cullen, Peter; Grifoll, Josep; Harris, Nick; Helle, Emmi; Hopbach, Achim; Kekalainen, Helka; Knezevic, Bozana; Sits, Tanel; Sohm, Kurt – ENQA (European Association for Quality Assurance in Higher Education), 2008
Quality assurance for higher education in Europe has developed significantly since 2002, and has increasingly influenced, and been influenced by, the Bologna Process. A major step in the Bologna Process was taken at the ministerial meeting in Bergen in May 2005, with the adoption of the Standards and Guidelines for Quality Assurance in the…
Descriptors: Foreign Countries, Quality Assurance, Higher Education, Educational Quality
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Hough, Susan L.; Hall, Bruce W. – Journal of Educational Research, 1994
Compares results of Hunter-Schmidt meta-analytic technique with results of Glass meta-analytic technique on three meta-analytic data sets chosen from the literature, hypothesizing that the Hunter-Schmidt mean effect size would be significantly larger than the Glass mean effect size because of correlation for measurement error. Results confirmed…
Descriptors: Comparative Analysis, Educational Research, Effect Size, Error of Measurement
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Falchikov, Nancy; Goldfinch, Judy – Review of Educational Research, 2000
Subjected 48 quantitative peer assessment studies that compared peer and teacher marks to meta-analysis. Peer assessments were found to resemble teacher assessments more closely when global judgments based on well understood criteria were used rather than when marking involved assessing several individual dimensions. (Author/SLD)
Descriptors: College Faculty, College Students, Comparative Analysis, Criteria
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Levine, Timothy R.; Bresnahan, Mary Jiang; Park, Hee Sun; Lapinski, Maria Knight; Wittenbaum, Gwen M.; Shearman, Sachiyo Morinaga; Lee, Sun Young; Chung, Donghun; Ohashi, Rie – Human Communication Research, 2003
Reports a meta-analysis of published cross-cultural self-construal research. Notes that the results across studies suggests that the evidence for the predicted cultural differences is weak, inconsistent, or nonexistent. Concludes that catastrophic validity problems exist in research involving the use of self-construal scales in cross-cultural…
Descriptors: Communication Research, Cross Cultural Studies, Cultural Differences, Evaluation Methods
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Gudykunst, William B.; Lee, Carmen M. – Human Communication Research, 2003
Disagrees with Levine et al.'s conclusion (published in this issue, see CS 764 297) that the three self construal scales currently in use have "severe" or "fatal" flaws. Argues that the results of Levine et al.'s meta-analysis and priming studies do not raise problems with the validity of self construal scales. Concludes that the two-dimensional…
Descriptors: Communication Research, Cross Cultural Studies, Cultural Differences, Evaluation Methods
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