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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
Herrera, Cheryl; Blair, Jennifer – Research in Higher Education Journal, 2015
As the U.S. population ages and policy changes emerge, such as the Patient Protection and Affordable Care Act of 2010, the U.S. will experience a significant shortage of Registered Nurses (RNs). Many colleges and universities are attempting to increase the size of nursing cohorts to respond to this imminent shortage. Notwithstanding a 2.6%…
Descriptors: Prediction, Success, Nursing Education, Nursing Students
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
Quatrano, Louis A. – 1981
The derivation of a model of management success potential in hospitals or health services administration is described. A questionnaire developed to assess management success potential in health administration students was voluntarily completed by approximately 700 incoming graduate students in 35 university health services administration programs…
Descriptors: Administrator Characteristics, Administrators, Health Personnel, High Achievement
Downes, Beverley – Australian University, 1976
A Model predicts a student's academic performance in his first year in a particular department at a university. It uses an aggregate selection score based on aggregate results obtained at a public examination along with a measure of the student's ability in one or more specific subjects or areas relevant to the department. (LBH)
Descriptors: Academic Achievement, Admission (School), College Freshmen, Foreign Countries
Peer reviewedKapes, Jerome T.; O'Reilly, Patrick A. – Journal of Industrial Teacher Education, 1973
The three in-school success measures used for the model was overall GPA, shop grade, and proficiency score on Ohio Trade and Industrial Education Achievement Test. A relationship between the predictor variables and the criteria in terms of time was found with predictiveness increasing from ninth to eleventh grade. (MS)
Descriptors: Educational Research, Grade Point Average, Grades (Scholastic), Models
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
Crossley, Craig D.; Stanton, Jeffrey M. – Journal of Vocational Behavior, 2005
The present study examined a longitudinal model of state and trait negative affect as predictors of job-search success. Job-search self-efficacy and job-search intensity were also examined as mediators of the negative affect--job-search success relation. Overall the model offered mixed support for Kasl's (1982) Reverse Causation Hypothesis.…
Descriptors: Job Search Methods, Hypothesis Testing, Self Efficacy, Models
Peer reviewedOman, Paul W. – AEDS Journal, 1986
Describes a study of 38 students at Eastern Oregon State College which identified mathematics proficiency as the key student characteristic leading to successful completion of computer science courses, and developed a model that accounts for 82 percent of the variation in students' final grades in computer courses for use by advisers. (LRW)
Descriptors: Academic Achievement, Academic Advising, Analysis of Variance, Computer Science Education

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