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- All Subjects: community college
- Creators: Thompson, Marilyn S
- Creators: Beardsley, Audrey
Description
Many methodological approaches have been utilized to predict student retention and persistence over the years, yet few have utilized a Bayesian framework. It is believed this is due in part to the absence of an established process for guiding educational researchers reared in a frequentist perspective into the realms of Bayesian analysis and educational data mining. The current study aimed to address this by providing a model-building process for developing a Bayesian network (BN) that leveraged educational data mining, Bayesian analysis, and traditional iterative model-building techniques in order to predict whether community college students will stop out at the completion of each of their first six terms. The study utilized exploratory and confirmatory techniques to reduce an initial pool of more than 50 potential predictor variables to a parsimonious final BN with only four predictor variables. The average in-sample classification accuracy rate for the model was 80% (Cohen's κ = 53%). The model was shown to be generalizable across samples with an average out-of-sample classification accuracy rate of 78% (Cohen's κ = 49%). The classification rates for the BN were also found to be superior to the classification rates produced by an analog frequentist discrete-time survival analysis model.
ContributorsArcuria, Philip (Author) / Levy, Roy (Thesis advisor) / Green, Samuel B (Committee member) / Thompson, Marilyn S (Committee member) / Arizona State University (Publisher)
Created2015
Description
Although U.S. rates of college enrollment among 18-24 year olds have reached historic highs, rates of degree completion have not kept pace. This is especially evident at community colleges, where a disproportionate number of students from groups who, historically, have had low college-completion rates enroll. One way community colleges are attempting to address low completion rates is by implementing institutional interventions intended to increase opportunities for student engagement at their colleges. Utilizing logistic and linear regression analyses, this study focused on community college students, examining the association between participation in institutional support activities and student outcomes, while controlling for specific student characteristics known to impact student success in college. The sample included 746 first-time, full-time, degree-seeking students at a single community college located in the U.S. Southwest. Additional analyses were conducted for the 440 first-time, full-time, degree-seeking students in this sample who placed into at least one developmental education course. Findings indicate that significant associations exist between different types of participation in institutional interventions and various student outcomes: Academic advising was found to be related to increased rates of Fall to Spring and Fall to Fall persistence and, for developmental education students, participation in a student success course was found to be related to an increase in the proportion of course credit hours earned. The results of this study provide evidence that student participation in institutional-level support may relate to increased rates of college persistence and credit hour completion; however, additional inquiry is warranted to inform specific policy and program decision-making at the college and to determine if these findings are generalizable to populations outside of this college setting.
ContributorsBeckert, Kimberly Marrone (Author) / De Los Santos Jr., Alfredo G (Thesis advisor) / Thompson, Marilyn S (Thesis advisor) / Berliner, David C. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Data use in higher education continues to increase as college and university leaders aim to meet accreditor and governmental expectations to use data to improve student outcomes. However, the steady increase in data use over the past decades has not been accompanied by an increase in employee data literacy in order for employees to use the data effectively. Further, inequitable student outcomes continue to persist in higher education, and more specifically at two-year community colleges, as potentially exacerbated by a lack of employee equity-mindedness. These concurrent problems—inadequate employee data literacy and persistent inequitable student outcomes—provide an opportunity to address both with one intervention. In this critical race, mixed-methods, action research study, I piloted an online professional development course, aimed at community college employees with the purpose to build data literacy and equity-mindedness. I used Bandura’s (1989) Social Cognitive Theory as a guiding theoretical framework paired with a quasi-experimental, delayed-start research design to study the effectiveness of the course in building employee data literacy and equity-mindedness, in addition to better understanding the impacts of environmental factors (i.e., organizational culture) on the implementation of the course. Using pre- and post-intervention surveys, pre- and post-intervention knowledge assessments, and post-intervention participant interviews, I determined that the professional development course contributed to improvements in employee data literacy and equity-mindedness. In particular, the course helped increase employee self-efficacy for data use, increased employee knowledge of data use and equity-mindedness, and increased employee intent to use data in the future. I also found that the organization’s culture related to data and equity to be complex and evolving, both hindering and facilitating data use, in general, and data use specifically, to address inequitable student outcomes.
ContributorsMitchell, Dennis Shane (Author) / Beardsley, Audrey (Thesis advisor) / Ott, Molly (Committee member) / Jacobsen, Craig (Committee member) / Arizona State University (Publisher)
Created2022