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Keywords = Tinto’s student integration theory

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15 pages, 496 KB  
Article
Predictors of Early College Success in the U.S.: An Initial Examination of Test-Optional Policies
by Kaylani Rae Othman, Rachel A. Vannatta and Audrey Conway Roberts
Educ. Sci. 2025, 15(9), 1089; https://doi.org/10.3390/educsci15091089 - 22 Aug 2025
Viewed by 1004
Abstract
For decades, the U.S. college admissions process has utilized standardized exams as critical indicators of college readiness. With the onset of the COVID pandemic, the majority of 4-year universities implemented the Test-Optional policy to improve college access and enrollment. The Test-Optional policy allows [...] Read more.
For decades, the U.S. college admissions process has utilized standardized exams as critical indicators of college readiness. With the onset of the COVID pandemic, the majority of 4-year universities implemented the Test-Optional policy to improve college access and enrollment. The Test-Optional policy allows prospective high school students to apply to institutions that have implemented this policy without a SAT or ACT score. This study examined the use of the Test-Optional policy and its relationship with early college success. Forward multiple regression examined which variables of High School GPA, Students of Color, First-Generation Status, Test-Optional, Pell Eligible, and Pre-College Credits best predict undergraduate first-year GPA. The results generated a five-variable model that accounted for 31% of the variability in first-year college GPA. High School GPA was the strongest predictor, while Test-Optional was not entered into the model. Binary logistic regression examined predictors of first-year college completion. Our results revealed the model including High School GPA, which tripled the odds of first-year completion. Again, Test-Optional was not included in the model. Although Students of Color and Pell Eligibility utilized Test-Optional significantly more than their peers, Test-Optional was not a significant predictor of first-year College GPA or first-year completion. Full article
(This article belongs to the Special Issue Educational Assessment Theories and Methodologies: Trends in Standardized Testing)
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19 pages, 655 KB  
Article
Predicting Student Attrition in Higher Education through the Determinants of Learning Progress: A Structural Equation Modelling Approach
by Pavlos Nikolaidis, Maizatul Ismail, Liyana Shuib, Shakir Khan and Gaurav Dhiman
Sustainability 2022, 14(20), 13584; https://doi.org/10.3390/su142013584 - 20 Oct 2022
Cited by 29 | Viewed by 7985
Abstract
Higher education policies are designed to facilitate students’ learning progression and academic success. Following Tinto’s integration theory and Bean’s attrition model, this study proposes a research model to investigate whether students prone to attrition can be pre-emptively identified through self-evaluating academic factors contributing [...] Read more.
Higher education policies are designed to facilitate students’ learning progression and academic success. Following Tinto’s integration theory and Bean’s attrition model, this study proposes a research model to investigate whether students prone to attrition can be pre-emptively identified through self-evaluating academic factors contributing to their learning progress. Theoretically, the learning progress is identified with student success, represented by factors amenable to intervention including the interaction with peers and instructors, teaching effectiveness, exam scores, absenteeism, students’ effort, and academic course-related variables. An exploratory and confirmatory factor analysis of 530 undergraduate students revealed that the indicators of learning progress in such students were channeled into two constructs. The results indicated that the teacher effectiveness and learning materials contributed most to the learning progress. Structural equation modelling revealed that the learning progress variables have a significant impact on students’ attrition status. A multi-group analysis confirmed the academic semesters to be a moderator in the mediating effects of the students’ grade point average (GPA). This model functions as a framework to design a student-oriented learning system promoting students’ learning experience and academic success. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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