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Herodotou, Christothea; Naydenova, Galina; Boroowa, Avi; Gilmour, Alison; Rienties, Bart – Journal of Learning Analytics, 2020
Despite the potential of Predictive Learning Analytics (PLAs) to identify students at risk of failing their studies, research demonstrating effective application of PLAs to higher education is relatively limited. The aims of this study are: (1) to identify whether and how PLAs can inform the design of motivational interventions; and (2) to capture…
Descriptors: Learning Analytics, Predictive Measurement, Student Motivation, Intervention
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Foster, Gigi – Higher Education: The International Journal of Higher Education and Educational Planning, 2010
Tertiary education is now accessible even to those who appear unlikely "ex ante" to succeed in jobs requiring post-high school education. Institutions that have broadened access to their programs must rely on two things to protect the quality of the degrees they award: selection mechanisms operating during students' tenure, and effective…
Descriptors: Core Curriculum, Teacher Effectiveness, Academic Achievement, Foreign Countries
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Staehr, Lorraine; Martin, Mary; Byrne, Graeme – Journal of College Student Retention, 2001
Evaluated an intervention program for women in the first year of a computing degree at La Trobe University (Australia). Found a sustained increase in female retention after the program was introduced. Identified the best predictors of success in the first programming course to be age and having studied mathematics in the final years of secondary…
Descriptors: Academic Persistence, Computer Science, Females, Foreign Countries
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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