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Using an Adaptive Learning Platform to Promote Underprepared Students’ Success in Corequisite Mathematics Courses: A Logistic Regression Analysis
Date
2024Type
DissertationDepartment
Curriculum and Instruction
Degree Level
Doctorate Degree
Abstract
The issue of college readiness persists in higher education, with many students entering college unprepared for the demands of college-level coursework. This challenge is particularly pronounced in math-intensive fields, where students frequently encounter struggles in corequisite math courses. The problem statement asserts that underprepared students, lacking essential math skills and knowledge and requiring varying levels of remediation, need personalized instruction and support to ensure their success in corequisite math courses.This study investigates whether an adaptive learning platform (EdReady) promotes the success of underprepared students in corequisite math courses. Through a two-sample proportion test comparing the proportion of students passing corequisite math courses between the treatment group (utilizing EdReady) and the control group (not using EdReady), the data analysis reveals a significant difference. More importantly, logistic regression analysis in the study demonstrates that the use of EdReady and students' prior math experience in Arithmetic are significant predictors of passing corequisite math courses.
The findings of this study carry substantial implications for the design of targeted interventions and support systems aimed at enhancing the academic outcomes of underprepared students in math-intensive fields. By exploring the differentiation of passing rates and the relationship between the utilization of adaptive learning platforms and student success in their corequisite math courses, this study contributes to the ongoing dialogue on innovative strategies for supporting underprepared students in higher education.
Permanent link
http://hdl.handle.net/11714/12702Additional Information
Committee Member | Chen, Li-Ting; Quinn, Robert; Lamberg, Teruni; Zhu, Xiaoshan |
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Rights | Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 United States |
Rights Holder | Author(s) |