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Jade Mai Cock; Hugues Saltini; Haoyu Sheng; Riya Ranjan; Richard Davis; Tanja Käser – International Educational Data Mining Society, 2024
Predictive models play a pivotal role in education by aiding learning, teaching, and assessment processes. However, they have the potential to perpetuate educational inequalities through algorithmic biases. This paper investigates how behavioral differences across demographic groups of different sizes propagate through the student success modeling…
Descriptors: Demography, Statistical Bias, Algorithms, Behavior
Barros, Thiago M.; Souza Neto, Plácido A.; Silva, Ivanovitch; Guedes, Luiz Affonso – Education Sciences, 2019
Predicting school dropout rates is an important issue for the smooth execution of an educational system. This problem is solved by classifying students into two classes using educational activities related statistical datasets. One of the classes must identify the students who have the tendency to persist. The other class must identify the…
Descriptors: Predictor Variables, Models, Dropout Rate, Classification
Arnold, Kimberly E. – ProQuest LLC, 2017
In the 21st century, attainment of a college degree is more important than ever to achieve economic self-sufficiency, employment, and an adequate standard of living. Projections suggest that by 2020, 65% of jobs available in the U.S. will require postsecondary education. This reality creates an unprecedented demand for higher education, and…
Descriptors: Educational Technology, Profiles, Biographies, Demography
Tienken, Christopher H.; Colella, Anthony; Angelillo, Christian; Fox, Meredith; McCahill, Kevin R.; Wolfe, Adam – RMLE Online: Research in Middle Level Education, 2017
The use of standardized test results to drive school administrator evaluations pervades education policymaking in more than 40 states. However, the results of state standardized tests are strongly influenced by non-school factors. The models of best fit (n = 18) from this correlational, explanatory, longitudinal study predicted accurately the…
Descriptors: Predictor Variables, Standardized Tests, Test Results, Models
Feng, Mingyu, Ed.; Käser, Tanja, Ed.; Talukdar, Partha, Ed. – International Educational Data Mining Society, 2023
The Indian Institute of Science is proud to host the fully in-person sixteenth iteration of the International Conference on Educational Data Mining (EDM) during July 11-14, 2023. EDM is the annual flagship conference of the International Educational Data Mining Society. The theme of this year's conference is "Educational data mining for…
Descriptors: Information Retrieval, Data Analysis, Computer Assisted Testing, Cheating
Boston, Wallace E.; Ice, Phil – Online Journal of Distance Learning Administration, 2011
As the growth of online programs continues to rapidly accelerate, concern over the retention of the online learner is increasing. Educational administrators at institutions offering online courses, those fully online or brick and mortars, are eager to promote student achievement. Retention is critically important, not just for student success, but…
Descriptors: Electronic Learning, Academic Achievement, Online Courses, Academic Persistence
Hammer, Patricia Cahape; Hixson, Nate – West Virginia Department of Education, 2014
This is the first of three evaluation reports on the effectiveness of a regional train-the-trainer strategy to support classroom implementation of the Next Generation Content Standards and Objectives (NxGen CSOs). This report focuses on six regional Educator Enhancement Academies (EEAs) hosted by the eight regional education service agencies…
Descriptors: Trainers, Program Effectiveness, Program Evaluation, Teacher Educator Education
van Ommeren, Alice – Journal of Applied Research in the Community College, 2011
The results of this study determined that community college students who transfer to for-profit institutions are indeed different from students who follow traditional routes defined as public and non-profit institutions. This study compares the demographic characteristics, academic experiences, and socioeconomic factors of California community…
Descriptors: College Transfer Students, Community Colleges, Transfer Students, Demography
Rafferty, Anna N., Ed.; Whitehill, Jacob, Ed.; Romero, Cristobal, Ed.; Cavalli-Sforza, Violetta, Ed. – International Educational Data Mining Society, 2020
The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of…
Descriptors: Educational Improvement, Teaching Methods, Information Retrieval, Data Processing
Zou, Xiehua – International Education Studies, 2009
The Chinese government's stated objective of student financial aid (SFA) policy is to help students from low-income families to access higher levels of education. This article first explores the possible associations between the distribution of SFA and students' various demographic and academic backgrounds. Then it presents the findings regarding…
Descriptors: Foreign Countries, Student Financial Aid, Social Justice, Low Income Groups
Peer reviewedZammuto, Raymond F. – Journal of Higher Education, 1984
Changes in the composition of colleges and universities during the 1970s and the changes in the subpopulation of institutions with a high commitment to liberal arts education were studied. The effect these changes had on the diversity of the population and subpopulation is described. (MLW)
Descriptors: Data Analysis, Declining Enrollment, Demography, General Education
Goenner, Cullen F.; Pauls, Kenton – Research in Higher Education, 2006
The purpose of this paper is to build a predictive model of enrollment that provides data driven analysis to improve undergraduate recruitment efforts. We utilize an inquiry model, which examines the enrollment decisions of students that have made contact with our institution, a medium sized, public, Doctoral I university. A student, who makes an…
Descriptors: Enrollment Trends, Data Analysis, Undergraduate Students, Models
Peer reviewedStafford, Kathy L.; And Others – Journal of Higher Education, 1984
States vary in the proportion of their populations who pursue higher education. A study that assesses the relationship between a state's economic and social characteristics and its citizens' participation in higher education is discussed. (Author/MLW)
Descriptors: College Attendance, Data Analysis, Demography, Economic Factors
Erosheva, Elena A. – Psychometrika, 2005
This paper focuses on model interpretation issues and employs a geometric approach to compare the potential value of using the Grade of Membership (GoM) model in representing population heterogeneity. We consider population heterogeneity manifolds generated by letting subject specific parameters vary over their natural range, while keeping other…
Descriptors: Mathematical Formulas, Research Methodology, Models, Comparative Analysis
Gilmore, William; And Others – 1974
Traditionally, the most commonly used methods of forecasting school enrollments have been those that looked to the past for a picture of the future. In restricting the forecaster to projections of past trends, the "percentage survival" technique ignores a host of current trends implicit in a changing society. A second problem with most…
Descriptors: Census Figures, Computer Programs, Data Analysis, Demography
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