Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 0 |
Since 2016 (last 10 years) | 4 |
Since 2006 (last 20 years) | 11 |
Descriptor
Academic Persistence | 16 |
Predictive Measurement | 16 |
Predictive Validity | 16 |
Models | 10 |
Predictor Variables | 9 |
Regression (Statistics) | 7 |
Higher Education | 6 |
College Students | 5 |
Graduation Rate | 5 |
School Holding Power | 5 |
Grade Point Average | 4 |
More ▼ |
Source
Author
Cabrera, Alberto F. | 2 |
Almeda, Ma. Victoria | 1 |
Baker, Ryan S. | 1 |
Berg, Emily A. | 1 |
Braysher, Ben | 1 |
Brocato, Melissa B. | 1 |
Cohen, Frederic L. | 1 |
Davis, Lloyd | 1 |
Fisk, Paul S. | 1 |
Graham, Mark J. | 1 |
Hanauer, David I. | 1 |
More ▼ |
Publication Type
Reports - Research | 12 |
Journal Articles | 9 |
Speeches/Meeting Papers | 4 |
Reports - Evaluative | 2 |
Dissertations/Theses -… | 1 |
Reports - Descriptive | 1 |
Education Level
Higher Education | 9 |
Postsecondary Education | 6 |
Adult Education | 1 |
High Schools | 1 |
Secondary Education | 1 |
Audience
Practitioners | 1 |
Location
Florida | 1 |
Pennsylvania | 1 |
Laws, Policies, & Programs
Assessments and Surveys
Student Adaptation to College… | 1 |
What Works Clearinghouse Rating
Almeda, Ma. Victoria; Zuech, Joshua; Utz, Chris; Higgins, Greg; Reynolds, Rob; Baker, Ryan S. – Online Learning, 2018
Online education continues to become an increasingly prominent part of higher education, but many students struggle in distance courses. For this reason, there has been considerable interest in predicting which students will succeed in online courses and which will receive poor grades or drop out prior to completion. Effective intervention depends…
Descriptors: Performance Factors, Online Courses, Electronic Learning, Models
Hanauer, David I.; Graham, Mark J.; Hatfull, Graham F. – CBE - Life Sciences Education, 2016
Curricular changes that promote undergraduate persistence in science, technology, engineering, and mathematics (STEM) disciplines are likely associated with particular student psychological outcomes, and tools are needed that effectively assess these developments. Here, we describe the theoretical basis, psychometric properties, and predictive…
Descriptors: College Students, Academic Persistence, Psychometrics, Predictive Validity
Huang, Liuli; Roche, Lahna R.; Kennedy, Eugene; Brocato, Melissa B. – International Journal of Higher Education, 2017
Many researchers have explored the relationships between the likelihood of graduating from college and demographic and pre-college factors such as gender, race/ethnicity, high school grade point average (GPA), and standardized test scores. However, additional factors such as a student's college major, home address, or use of learning support in…
Descriptors: Graduation Rate, Predictor Variables, Predictive Measurement, Predictive Validity
Predicting Higher Education Outcomes and Implications for a Postsecondary Institution Ratings System
Walker, Eddie G., II – Journal of Higher Education Policy and Management, 2016
The accountability of colleges and universities is a high priority for those making policy decisions. The purpose of this study was to determine institutional characteristics predicting retention rates, graduation rates and transfer-out rates using publicly available data from the US Department of Education. Using regression analysis, it was…
Descriptors: Higher Education, Predictive Measurement, Predictive Validity, Prediction
Slanger, William D.; Berg, Emily A.; Fisk, Paul S.; Hanson, Mark G. – Journal of College Student Retention: Research, Theory & Practice, 2015
Ten years of College Student Inventory (CSI) data from one Midwestern public land-grant university were used to study the role of motivational factors in predicting academic success and college student retention. Academic success was defined as cumulative grade point average (GPA), cumulative course load capacity (i.e., the number of credits…
Descriptors: Longitudinal Studies, Cohort Analysis, Student Motivation, Academic Achievement
Lin, Jien-Jou – ProQuest LLC, 2013
Every year a group of graduates from high schools enter the engineering programs across this country with remarkable academic record. However, as reported in numerous studies, the number of students switching out of engineering majors continues to be an important issue. Previous studies have suggested various factors as predictors for student…
Descriptors: Success, Prediction, Predictive Measurement, Predictive Validity
Braysher, Ben – National Centre for Vocational Education Research (NCVER), 2012
The annual Student Outcomes Survey collects information on the outcomes of two groups of students--those that have completed a qualification (graduates) and those that have completed only part of a course and then left the vocational education and training (VET) system (module completers). At the time of selecting the survey sample, insufficient…
Descriptors: Qualifications, Eligibility, Vocational Education, Graduates
Zeidenberg, Matthew; Jenkins, Davis; Scott, Marc A. – Community College Research Center, Columbia University, 2012
Discussions of the barriers to completion in community colleges have largely focused on student success in introductory college-level math and English courses, and rightfully so, since these courses are typically required for degrees. However, there is a much broader range of courses that also serve as "gatekeepers" in the sense that they are…
Descriptors: Grade Point Average, Introductory Courses, Community Colleges, Barriers
Johnson, James – NACADA Journal, 2013
In an effort to standardize academic risk assessment, the NCAA developed the graduation risk overview (GRO) model. Although this model was designed to assess graduation risk, its ability to predict grade-point average (GPA) remained unknown. Therefore, 134 individual risk assessments were made to determine GRO model effectiveness in the…
Descriptors: Risk Assessment, College Athletics, Athletes, Graduation Rate
Miller, Thomas E.; Tyree, Tracy; Riegler, Keri K.; Herreid, Charlene – College and University, 2010
This article describes the early outcomes of an ongoing project at the University of South Florida in Tampa that involves using a logistics regression formula derived from pre-matriculation characteristics to predict the risk of individual student attrition. In this piece, the authors will describe the results of the prediction formula and the…
Descriptors: Mentors, Student Attrition, Models, Multiple Regression Analysis
Roblyer, M. D.; Davis, Lloyd – Online Journal of Distance Learning Administration, 2008
Virtual schooling has the potential to offer K-12 students increased access to educational opportunities not available locally, but comparatively high dropout rates continue to be a problem, especially for the underserved students most in need of these opportunities. Creating and using prediction models to identify at-risk virtual learners, long a…
Descriptors: Prediction, Predictor Variables, Success, Virtual Classrooms

Rownd, Carolyn; And Others – Research in Higher Education, 1981
A study of the persistence behavior of students who drop college courses included predictions concerning which students were most likely to drop courses, which courses were most likely to be dropped, and at what point in the semester students were most likely to drop courses. (Author/MLW)
Descriptors: Academic Persistence, College Students, Courses, Grade Point Average
Cabrera, Alberto F.; And Others – 1990
This study tested the convergent and discriminant validity between two theories of college persistence: the Student Attrition Model and the Student Integration Model. The study examined conceptual similarities for each theory to explain departure decisions and examined how both theoretical frameworks enhanced the understanding of the processes…
Descriptors: Academic Persistence, Decision Making, Discriminant Analysis, Higher Education
Kaase, Kristopher J. – 1994
This study tested the ability of the Student Adaptation to College Questionnaire (SACQ) to predict attrition for first-time, full-time freshmen at a predominantly white, medium-sized, liberal arts, denominational college in the southeast. A random sample of 100 students were surveyed of which 84 returned usable questionnaires (57 females, 27 male,…
Descriptors: Academic Persistence, College Freshmen, College Students, Higher Education
Sadler, William E.; Cohen, Frederic L.; Kockesen, Levent – 1997
This paper describes a methodology used in an on-going retention study at New York University (NYU) to identify a series of easily measured factors affecting student departure decisions. Three logistic regression models for predicting student retention were developed, each containing data available at three distinct times during the first…
Descriptors: Academic Persistence, College Freshmen, Dropouts, High Risk Students
Previous Page | Next Page ยป
Pages: 1 | 2