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CannistrĂ , Marta; Masci, Chiara; Ieva, Francesca; Agasisti, Tommaso; Paganoni, Anna Maria – Studies in Higher Education, 2022
This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading…
Descriptors: Dropouts, Potential Dropouts, Dropout Prevention, Dropout Characteristics
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Jones, Kyle M. L. – Education and Information Technologies, 2019
Institutions are applying methods and practices from data analytics under the umbrella term of "learning analytics" to inform instruction, library practices, and institutional research, among other things. This study reports findings from interviews with professional advisors at a public higher education institution. It reports their…
Descriptors: Academic Advising, Instructional Systems, Library Services, Institutional Research
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Fusilier, Marcelline; Durlabhji, Subhash; Cucchi, Alain – Journal of Educational Computing Research, 2008
National background of users may influence the process of technology acceptance. The present study explored this issue with the new, integrated technology use model proposed by Sun and Zhang (2006). Data were collected from samples of college students in India, Mauritius, Reunion Island, and United States. Questionnaire methodology and…
Descriptors: Foreign Countries, Data Analysis, Internet, Technology Integration
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Chang, Lin – New Directions for Institutional Research, 2006
Data-mining technology's predictive modeling was applied to enhance the prediction of enrollment behaviors of admitted applicants at a large state university. (Contains 4 tables and 6 figures.)
Descriptors: College Admission, Data Collection, Data Analysis, Models