Matching Items (3)
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- All Subjects: Higher Education
- Genre: Academic theses
- Creators: Dorn, Sherman
Description
The transition from high school to college can cause an undue amount of attrition for fully qualified, college-intending first-generation students. Although the students may have overcome multiple obstacles to be accepted to a college and arrive at the transition over summer, it can feel overwhelming to complete the flood of tasks without access to a supportive network to guide and interpret the intricate steps. Many programs focus on college preparation and access to college but do not devote attention to the delicate transition from access to enrollment during the summer months. The term opportunity melt for students who confirm their enrollment and do not enroll in any institution of higher education in the fall semester. This study identified the influence of strategic peer mentor support during the summer months for Chico State students who applied, were admitted, and accepted their college admission. This action research intervention applies key concepts of academic capital theory and follows up on previous cycles of action research in the California State University system to identify barriers for those who intended to enroll but decided not to attend any Cal State or other institution of higher education in the Fall semester after high school graduation.
ContributorsRyan, Shawn (Author) / Dorn, Sherman (Thesis advisor) / Kim, Jeongeun (Committee member) / Weston, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2022
Educational Data Mining: An Application of a Predictive Model of Online Student Enrollment Decisions
Description
College and university enrollment has decreased nationwide every year for more than a decade as educational consumers increasingly question the value of higher education and discover alternatives to the traditional university system. Enrollment professionals seeking growth are tasked to develop and implement innovative solutions to address increasing enrollment challenges by being responsive to consumer values, interests, and needs. This multi-phase mixed methods action research study explores educational data mining and machine learning to understand and predict the enrollment decisions of admitted applicants (n=3,843) at the online campus of a public research university (phase one). Then, this innovation is distributed to understand how university enrollment professionals (n=7) interpret and are affected by the factors that influence online student enrollment decisions (phase two). Logistic regression was used to evaluate 24 independent variables to classify each applicant into a dichotomous dependent outcome: will an applicant enroll or will they not. The model identified 10 significant predictors and accurately categorized 81% of the enrollment outcomes at its peak. The population was comprised of online adult learners and the findings were carefully compared to the findings of previous studies which differed in institutional settings (on campus) and student populations (first-year students). Additionally, the study aimed to extend the work of previous literature through a second application phase within the local context. The second phase was guided by distributed leadership theory and the four-stage theory of organizational change and introduced the model to enrollment professionals within the local context through participation in a workshop coupled with a pre-/post-workshop survey. This convergent parallel mixed methods design resulted in themes that demonstrated enrollment managers had a genuine desire to understand and apply the model to assist in solving complex enrollment challenges and were interested in using the model to inform their perspectives, decision-making, and strategy development. This study concludes that educational data mining and machine learning can be used to predict the enrollment decisions of online adult students and that enrollment managers can use the data to inform the many enrollment challenges they are tasked to overcome.
ContributorsSinger, Cody Gene (Author) / Ross, Lydia (Thesis advisor) / Dorn, Sherman (Thesis advisor) / Cillay, David (Committee member) / Arizona State University (Publisher)
Created2023
Description
This dissertation investigates the impact of a pedagogical class and a Community of Practice (CoP) on the implementation of reading strategies by faculty at a community college. It explores the types of reading strategies instructors plan to use, their integration into classroom practices, the factors enabling or impeding this implementation, and the influence of attitudes, social norms, and perceived behavioral control on their intentions to use these strategies. The study employs a mixed-methods research design, incorporating both qualitative and quantitative data collection and analysis methods. The findings reveal that instructors intend to adopt various reading strategies, with the pedagogical class and CoP playing significant roles in their professional development and instructional practices. The research identifies enablers and barriers to implementing reading strategies, highlighting the importance of supportive institutional contexts, professional development opportunities, and reflective teaching practices. By examining the application of reading strategies in the context of community college instruction, this dissertation contributes to the broader understanding of effective teaching practices and faculty development in higher education.
ContributorsMatthesen, Cathy Jeane (Author) / Dorn, Sherman (Thesis advisor) / Buss, Ray R (Committee member) / Brooks, Eric (Committee member) / Arizona State University (Publisher)
Created2024