scholarly journals Impacts Resulting from a Large-scale First-year Engineering and Computer Science Program on Students’ Successful Persistence Toward Degree Completion

2020 ◽  
Author(s):  
Gisele Ragusa ◽  
Emily Allen ◽  
Gustavo Menezes
Keyword(s):  
Computer Science ◽  
Degree Completion ◽  
Large Scale ◽  
First Year ◽  
Science Program

THE CONNECTION MACHINE IN A UNIVERSITY ENVIRONMENT: GESAMTHOCHSCHULE WUPPERTAL

1993 ◽  
Vol 04 (01) ◽  
pp. 137-141
Author(s):  
KLAUS SCHILLING
Keyword(s):  
Parallel Computing ◽  
Computer Science ◽  
Large Scale ◽  
Early History ◽  
Short Account ◽  
University Environment ◽  
Connection Machine ◽  
The University

A short account is presented on the early history, the intentions and the development of large scale parallel computing at the University of Wuppertal. It might serve as an illustration how common activities between computational and computer science can be stimulated, in the university environment.

scholarly journals Gender, achievement, and persistence in an undergraduate computer science program

2006 ◽  
Vol 37 (4) ◽  
pp. 42-57 ◽  
Author(s):  
Sandra Katz ◽  
David Allbritton ◽  
John Aronis ◽  
Christine Wilson ◽  
Mary Lou Soffa
Keyword(s):  
Computer Science ◽  
Science Program ◽  
Gender Achievement

scholarly journals Predictive Analysis of Higher Education Graduation and Retention in Saudi Arabia u sing Multinomial Logistic Regression

2021 ◽  
Vol 3 (6) ◽  
pp. 1-8
Author(s):  
Ahmed Bagabir ◽  
◽  
Mohammad Zaino ◽  
Ahmed Abutaleb ◽  
Ahmed Fagehi ◽  
...  
Keyword(s):  
Higher Education ◽  
Logistic Regression ◽  
Undergraduate Students ◽  
Degree Completion ◽  
Multinomial Logistic Regression ◽  
At Risk Students ◽  
Retention Rates ◽  
First Year ◽  
Important Indicator ◽  
Admissions Policy

It is suggested that this study contributes by establishing a robust methodology for analyzing the longitudinal outcomes of higher education. The current research uses multinomial logistic regression. To the knowledge of the authors, this is the first logistic regression analysis performed at Saudi higher education institutions. The study can help decision-makers take action to improve the academic performance of at-risk students. The analyses are based on enrollment and completion data of 5,203 undergraduate students in the colleges of engineering and medicine. The observation period was extended for ten academic years from 2010 to 2020. Four outcomes were identified for students: (i) degree completion on time, (ii) degree completion with delay, (iii) dropout, and (iv) still enrolled in programs. The objectives are twofold: (i) to study the present situation by measuring graduation and retention rates with benchmarking, and (ii) to determine the effect of twelve continuous and dummy predictors (covariates) on outcomes. The present results show that the pre-admission covariates slightly affect performance in higher education programs. The results indicate that the most important indicator of graduation is the student's achievement in the first year of the program. Finally, it is highly suggested that initiatives be taken to increase graduation and retention rates and to review the admissions policy currently in place.

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