Retention and matriculation are topics of heavy debate and inquiry in higher education as rising tuition costs, coupled with declining state support, have fueled the need for increased accountability. In Mississippi, few studies have been conducted that are unique to the public universities in the state in order to analyze success factors in higher education. The purpose of this study was to analyze these success factors at Mississippi’s public universities as measured by successful degree completion within a six-year time period. This study analyzed High School GPA, ACT Scores, Parental Income Levels, Parental Education Levels, Ethnicity, and Gender. Academic, demographic and socioeconomic data were gathered on two cohorts of resident first-time, full-time students attending a Mississippi Institution of Higher Learning. A total of 5,603 students were included in the study from the fall 2001 and 2002 semesters. Transfer students were not included in the study. A successful completer was defined as completing a bachelor’s degree within six-years of enrollment. Students still enrolled in the seventh year were not included. In addition, students seeking an Associate’s Degree were also not included. Descriptive statistics revealed that graduation rates fluctuated depending on high school GPA, ACT scores, income and parental education levels. The most significant differences in graduation rates occurred when analyzing high school GPA and income statistics. These findings were supported when logistic regression analysis was employed. Logistic regression analysis was used to analyze these factors compared to graduation rates for the state, and by type of institution. In Mississippi, there are four regional universities and four research universities. High school GPA and parental income were significant predictors in all three models, while ACT was significant when analyzing data for the system. For research universities, the education level of the father was significant. For regional universities, ethnicity was a significant predictor. In all, universities should develop a deeper understanding of the socioeconomic background of students in order to ensure that proper scaffolding is in place to ensure successful matriculation.

No description available.

No description available.

No description available.

No description available.

No description available.

How to handle large multi-dimensional datasets such as hyperspectral images and video information both efficiently and effectively plays an important role in big-data processing. The characteristics of tensor low-rank decomposition in recent years demonstrate the importance of capturing the tensor structure adequately which usually yields efficacious approaches. In this dissertation, we first aim to explore the tensor singular value decomposition (t-SVD) with the nonconvex regularization on the multi-view subspace clustering (MSC) problem, then develop two new tensor decomposition models with the Bayesian inference framework on the tensor completion and tensor robust principal component analysis (TRPCA) and tensor completion (TC) problems. Specifically, the following developments for multi-dimensional datasets under the mathematical tensor framework will be addressed. (1) By utilizing the t-SVD proposed by Kilmer et al. \cite{kilmer2013third}, we unify the Hyper-Laplacian (HL) and exclusive $\ell_{2,1}$ (L21) regularization with Tensor Log-Determinant Rank Minimization (TLD) to identify data clusters from the multiple views' inherent information. Whereby the HL regularization maintains the local geometrical structure that makes the estimation prune to nonlinearities, and the mixed $\ell_{2,1}$ and $\ell_{1,2}$ regularization provides the joint sparsity within-cluster as well as the exclusive sparsity between-cluster. Furthermore, a log-determinant function is used as a tighter tensor rank approximation to discriminate the dimension of features. (2) By considering a tube as an atom of a third-order tensor and constructing a data-driven learning dictionary from the observed noisy data along the tubes of a tensor, we develop a Bayesian dictionary learning model with tensor tubal transformed factorization to identify the underlying low-tubal-rank structure of the tensor substantially with the data-adaptive dictionary for the TRPCA problem. With the defined page-wise operators, an efficient variational Bayesian dictionary learning algorithm is established for TPRCA that enables to update of the posterior distributions along the third dimension simultaneously. (3) With the defined matrix outer product into the tensor decomposition process, we present a new decomposition model for a third-order tensor. The fundamental idea is to decompose tensors mathematically in a compact manner as much as possible. By incorporating the framework of Bayesian probabilistic inference, the new tensor decomposition model on the subtle matrix outer product (BPMOP) is developed for the TC and TRPCA problems. Extensive experiments on synthetic data and real-world datasets are conducted for the multi-view clustering, TC, and TRPCA problems to demonstrate the desirable effectiveness of the proposed approaches, by detailed comparison with currently available results in the literature.

A growing trend in high schools across the state is the use of dual credit and dual enrollment courses to better prepare high school students for college or the work force. Given the increase in dual credit and dual enrollment participation and the goal of creating a more seamless transition from high school to college, the effectiveness of these programs needs to be researched. The research hypothesis for this study states that students who participate in a dual credit and dual enrollment program during high school are more likely to complete an associate degree within 3 years than students who do not participate in dual credit and dual enrollment, when accounting for covariates such as gender, race, and socioeconomic status. This study examined the effectiveness of dual credit and dual enrollment programs, particularly with regard to associate degree completion, credit hour accumulation, and college GPA. The participants in this study were 1st-time, full-time students enrolled during Academic Year 2007 at 5 of the 15 community and junior colleges in state of Mississippi. The sample included 6,029 students, of which 255 had previously participated in a dual enrollment or dual credit program. This study revealed that dual credit and dual enrollment participation positively affects postsecondary outcomes for students enrolling in community colleges in the areas of associate degree completion and college GPA. Students who started college with prior experience in a dual credit or dual enrollment program were 2.51 times more likely to complete an associate degree within 3 years of first-time, full-time college enrollment than individuals who did not participate. Additionally, the study revealed that factors such as SES, gender, and race had an effect on college GPA; and that SES and race affected the number of credit hours earned by community college students. Given the positive outcomes resulting from participation in dual credit and dual enrollment programs, these programs certainly bear consideration for expansion and further study in the future, particularly given the growing availability of longitudinal data within statewide longitudinal data systems that have launched in recent years across the United States.

Community college completion rates have remained stagnate over the past decade; therefore, college leaders and policy makers continue to seek institutional factors that positively affect graduation rates. The purpose of this study was to determine the effectiveness of Phi Theta Kappa Honor Society (PTKHS), the nation’s largest and oldest honor society for community college students. The completion rates of PTKHS members and rates of other students were investigated using simple statistical procedures for determining significance of differences in proportions. Data sources included published data from the National Center for Education Statistics Integrated Postsecondary Education Data System surveys, the National Student Clearinghouse, and Beginning Postsecondary Student Longitudinal Study. Results indicate completion gaps between PTKHS and other community college students were substantially high, so much so in fact, that tests of significance were not needed to assist the audience of this research in determining the definite impact of PTKHS on student success.

Episodic memory binds together the people, objects, and locations that make up the specific events of our lives, and allows the recall of our past in the service of current and future goals. Recent models of memory have posited that the hippocampus instantiates computations critical for episodic memory including mnemonic discrimination, relational binding, and holistic retrieval. Collectively, this set of studies aim to chart the ontogeny of each key components of episodic memory. We found robust improvements in children’s abilities to form complex relational structures and to make fine-grained discrimination for individual items from age 4 to age 6. However, relational memory dependent on context discrimination appears to follow a more protracted development. Furthermore, relational binding and mnemonic discrimination (item and context levels) undergo age-related decrements in senescence. Despite relatively poor relational binding capabilities, children as young as age 4 are able to retrieve multi-element events holistically, such as successfully retrieving of one aspect of an event predicts the retrieval success of other aspects from the same event. Critically, the degree of holistic episodic retrieval increases from age 4 to young adulthood. This multi-process approach provides important theoretical insights into lifespan profile of episodic memory. / Psychology