Co-enrollment density predicts engineering students’ persistence and graduation: College networks and logistic regression analysis-Reference-Cited by-同舟云学术

Co-enrollment density predicts engineering students’ persistence and graduation: College networks and logistic regression analysis

Published:2021-09 Issue: Volume:70 Page:101025
ISSN:0191-491X
Container-title:Studies in Educational Evaluation
language:en
Short-container-title:Studies in Educational Evaluation

Author:

Huerta-Manzanilla Eric Leonardo^ORCID,Ohland Matthew W.^ORCID,Peniche-Vera Rebeca del Rocío

Funder

National Science Foundation Directorate for Engineering

Consejo Nacional de Ciencia y Tecnologia

Publisher

Elsevier BV

Subject

Education

Reference75 articles.

1. ABET & engineering accreditation - history, theory, practice: Initial findings from a national study on the governance of engineering education;Akera;2019 ASEE Annual Conference & Exposition Proceedings,2019

2. Examining the relationship between the accreditation of engineering programmes and institutional performance;Al Busaidi;Quality Assurance in Education,2020

3. A review of the contemporary international literature on student retention in higher education;Aljohani;International Journal of Education and Literacy Studies,2016

4. A comprehensive review of the major studies and theoretical models of student retention in higher education;Aljohani;Higher Education Studies,2016

5. Student involvement: A developmental theory for higher education;Astin;Journal of College Student Development,1999

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Fuzzy Artificial Intelligence—Based Model Proposal to Forecast Student Performance and Retention Risk in Engineering Education: An Alternative for Handling with Small Data;Sustainability;2022-10-28

2. Harm not only females: math-gender stereotypes and engagement of engineering students at Russian universities;European Journal of Engineering Education;2022-10-14

3. Predictive Model of Student Academic Performance in Private Higher Education Institution (Case in Undergraduate Management Program);2022 International Conference on Data Science and Its Applications (ICoDSA);2022-07-06

4. Logit models, the area under receiver characteristic curves, sensitivity, and specificity for Co-enrollment density in college networks dataset.;Data in Brief;2021-12