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Baker, Ryan S.; Esbenshade, Lief; Vitale, Jonathan; Karumbaiah, Shamya – Journal of Educational Data Mining, 2023
Predictive analytics methods in education are seeing widespread use and are producing increasingly accurate predictions of students' outcomes. With the increased use of predictive analytics comes increasing concern about fairness for specific subgroups of the population. One approach that has been proposed to increase fairness is using demographic…
Descriptors: Demography, Data Use, Prediction, Research Methodology
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Giorgio Di Pietro – European Education, 2023
We use Eurobarometer data to examine barriers to international student mobility. Multivariate analysis is employed to study how individual characteristics are related to the obstacles preventing higher education students from participating in activities in another EU country. The results suggest that several demographic factors including area of…
Descriptors: Student Characteristics, Barriers, Student Mobility, Foreign Countries
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Hoek, Lianne; Munniksma, Anke; Dijkstra, Anne Bert – Journal of Social Science Education, 2022
Purpose: Scholars are increasingly paying attention to the characteristics of effective citizenship education. The systematic use of data to maximise student learning, also called an output-driven approach, is often presented as a powerful predictor of student outcomes. However, its effectiveness has not been studied in citizenship education.…
Descriptors: Teaching Methods, Citizenship Education, Instructional Effectiveness, Measurement Techniques
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Costa, Stella F.; Diniz, Michael M. – Education and Information Technologies, 2022
The large rates of students' failure is a very frequent problem in undergraduate courses, being even more evident in exact sciences. Pointing out the reasons of such problem is a paramount research topic, though not an easy task. An alternative is to use Educational Data Mining techniques (EDM), which enables one to convert data from educational…
Descriptors: Prediction, Undergraduate Students, Mathematics Education, Models
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Ahmadi, Mohammad; Dileepan, Parthasarati; Wheatley, Kathleen K. – Journal of Education for Business, 2016
This is the decade of data analytics and big data, but not everyone agrees with the definition of big data. Some researchers see it as the future of data analysis, while others consider it as hype and foresee its demise in the near future. No matter how it is defined, big data for the time being is having its glory moment. The most important…
Descriptors: Strategic Planning, Data, Data Analysis, Statistical Analysis
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Vidotto, Davide; Vermunt, Jeroen K.; van Deun, Katrijn – Journal of Educational and Behavioral Statistics, 2018
With this article, we propose using a Bayesian multilevel latent class (BMLC; or mixture) model for the multiple imputation of nested categorical data. Unlike recently developed methods that can only pick up associations between pairs of variables, the multilevel mixture model we propose is flexible enough to automatically deal with complex…
Descriptors: Bayesian Statistics, Multivariate Analysis, Data, Hierarchical Linear Modeling
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Kedron, Peter; Quick, Matthew; Hilgendorf, Zach; Sachdeva, Mehak – Journal of Geography in Higher Education, 2022
Educational materials focused on spatial data analysis often feature mathematical descriptions of methods and step-by-step instructions of software tools, but infrequently discuss the set of decisions involved in specifying a statistical model. Failing to consider model specification may lead to specification searching, or the process of repeating…
Descriptors: Geography Instruction, Data Analysis, Meta Analysis, Decision Making
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Fynn, Angelo – International Review of Research in Open and Distributed Learning, 2016
The prediction and classification of student performance has always been a central concern within higher education institutions. It is therefore natural for higher education institutions to harvest and analyse student data to inform decisions on education provision in resource constrained South African environments. One of the drivers for the use…
Descriptors: Academic Achievement, Higher Education, Foreign Countries, Data Collection
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Turner, David A. – Compare: A Journal of Comparative and International Education, 2017
In his proposal for comparative education, Marc Antoinne Jullien de Paris argues that the comparative method offers a viable alternative to the experimental method. In an experiment, the scientist can manipulate the variables in such a way that he or she can see any possible combination of variables at will. In comparative education, or in…
Descriptors: Comparative Education, Comparative Analysis, Research Methodology, Predictor Variables
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Durand, Roger; Decker, Phillip J.; Kirkman, Dorothy M. – American Journal of Evaluation, 2014
Despite our best efforts as evaluators, program implementation failures abound. A wide variety of valuable methodologies have been adopted to explain and evaluate the "why" of these failures. Yet, typically these methodologies have been employed concurrently (e.g., project monitoring) or to the post-hoc assessment of program activities.…
Descriptors: Evaluation Methods, Program Implementation, Failure, Program Effectiveness
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Avella, John T.; Kebritchi, Mansureh; Nunn, Sandra G.; Kanai, Therese – Online Learning, 2016
Higher education for the 21st century continues to promote discoveries in the field through learning analytics (LA). The problem is that the rapid embrace of of LA diverts educators' attention from clearly identifying requirements and implications of using LA in higher education. LA is a promising emerging field, yet higher education stakeholders…
Descriptors: Higher Education, Literature Reviews, Data Collection, Data Analysis
Chang, Hedy N.; Gee, Kevin; Hennessy, Briana; Alexandro, David; Gopalakrishnan, Ajit – Attendance Works, 2021
This report describes how Connecticut took steps to collect consistent attendance data by learning mode -- remote, in-person and hybrid -- and publicly released data in a timely manner during the pandemic. For example, the Connecticut State Department of Education (CSDE) agreed upon a standard definition of attendance -- showing up to school for…
Descriptors: Attendance, COVID-19, Pandemics, Data Collection
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Howell, Roy D. – Measurement: Interdisciplinary Research and Perspectives, 2014
Building on the work of Bollen (2007) and Bollen & Bauldry (2011), Bainter and Bollen (this issue) clarifies several points of confusion in the literature regarding causal indicator models. This author would certainly agree that the effect indicator (reflective) measurement model is inappropriate for some indicators (such as the social…
Descriptors: Statistical Analysis, Measurement, Causal Models, Data Interpretation
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Hao, Jinmei; Li, Suke – Journal of Education and Practice, 2017
With the adjustment of industrial structure of China in recent years, the market urgently needs different levels of professionals. Specialty education is an important part of higher education in China, has its unique advantages. Through the analysis of the history data of specialty education in our country, the result shows that the specialty…
Descriptors: Foreign Countries, Specialization, Specialists, Enrollment Trends
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Shaw, Stacy; Radwin, David – National Center for Education Statistics, 2014
The web tables in this report provide original and revised estimates of statistics previously published in 2007-08 National Postsecondary Student Aid Study (NPSAS:08): Student Financial Aid Estimates for 2007-08 (NCES 2009-166). The revised estimates were generated using revised weights that were updated in August 2013. NPSAS:08 data were…
Descriptors: Student Financial Aid, Tables (Data), Comparative Analysis, Statistical Data
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