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Ramesh, Arti; Goldwasser, Dan; Huang, Bert; Daume, Hal; Getoor, Lise – IEEE Transactions on Learning Technologies, 2020
Maintaining and cultivating student engagement is critical for learning. Understanding factors affecting student engagement can help in designing better courses and improving student retention. The large number of participants in massive open online courses (MOOCs) and data collected from their interactions on the MOOC open up avenues for studying…
Descriptors: Online Courses, Learner Engagement, Student Behavior, Success
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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
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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
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Mertes, Scott J.; Hoover, Richard E. – Community College Journal of Research and Practice, 2014
Retention is a complex issue of great importance to community colleges. Several retention models have been developed to help explain this phenomenon. However, these models typically have used four-year college and university environments to build their foundations. Several researchers have attempted to identify predictor variables using…
Descriptors: Community Colleges, Predictor Variables, College Freshmen, Academic Persistence
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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
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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
Synco, Tracee M. – ProQuest LLC, 2012
Tinto, Astin and countless others have researched the retention and attrition of students from college for more than thirty years. However, the six year graduation rate for all first-time full-time freshmen for the 2002 cohort was 57%. This study sought to determine the retention variables that predicted continued enrollment of entering freshmen…
Descriptors: College Entrance Examinations, School Holding Power, Grade Point Average, Supplementary Education
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de Rome, Elizabeth; Lewin, Terry – Higher Education, 1984
A study examined whether information about student approaches to making their course choices could have been used to identify those who subsequently changed or withdrew from their courses. Multivariate analysis indicated that combinations of the information could discriminate between students who persisted and those who withdrew. (Author/MLW)
Descriptors: Academic Persistence, College Environment, Higher Education, Predictive Measurement
St. John, Edward P. – 1994
This paper explores the need for a better understanding of the influences of prices and student aid on student enrollment and college budgets. The theory of net price has not been found to adequately explain changes in enrollment. Based on a critical review of recent research on student price response, this paper develops an alternative approach…
Descriptors: Academic Persistence, Budgets, Enrollment, Higher Education
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Johnes, Jill – Studies in Higher Education, 1990
Statistical analysis of a sample of the 1979 entry cohort to Lancaster University indicates that the likelihood of non-completion is determined by various characteristics including the student's academic ability, gender, marital status, work experience prior to university, school background, and location of home in relation to university.…
Descriptors: Academic Persistence, Dropout Characteristics, Dropout Research, Educational Research
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Shah, Chandra; Burke, Gerald – Higher Education, 1999
A Markov chain is used to model the movement of undergraduates through the higher education system in Australia. Given the student's age on commencing a course of study, the model provides estimates of the probability of course completion, mean time for completion, and mean time spent in the higher education system. (Author/MSE)
Descriptors: Academic Persistence, Age, College Students, Enrollment Management