This study utilized a combination of intrinsic and instrumental case studies to describe the experience of four dying persons and their significant relations, as they engaged in music therapy sessions designed with the goal of facilitating relationship completion. The four primary participants were inpatients of the Baycrest palliative care program who were diagnosed with a terminal illness and a life expectancy of less than six months. Two spouses who were involved in music therapy sessions were co-participants. I developed four case studies to represent each of the rich and detailed stories. Through the use of narrative research methods I was able to describe, interpret, and understand the complexity held within the multiple data sources that informed each case study. Data sources included: music created, utilized, recorded, and/or discussed in music therapy sessions; discussions during music therapy sessions; the researcher’s field notes; formal notes placed in the participant’s medical charts; the formal written assessment; transcriptions of audio-taped music therapy sessions; interviews; interview transcriptions; artistic pieces crafted by myself that emerged from the experiences of the participants as reflected in their interviews, and weekly participation in sessions which were verified by the participants; and other artistic material. The rich knowledge that emerged from the individual case studies informed a cross-case analysis where global themes were identified from a thematic analysis of participants’ experiences; and process motifs arising from the progression of participant engagement in music therapy are described. Global themes included: love; loss; gratitude; growth/transformation; courage/strength; and goodbye. The five process motifs that emerged were: (1) music therapy helps and was valued as a means of sharing the participants’ perceptions of their situation. (2) music therapy provides a safe place to become aware of, explore, and express feelings. (3) music enhances communication. (4) music therapy techniques provide creative avenues for self-expression. (5) music therapy provided a vehicle for revisiting and reminiscing. The thesis concludes with a summary of the knowledge revealed and a discussion of implications for music therapists and health care professionals, as well as a presentation of final thoughts and reflections on my role as researcher in this study.

This study sought to understand why some students at Eagle University (pseudo.) complete the doctoral program in instructional technology while others do not. The study explores factors and issues affecting doctoral attrition and completion of the Ph.D. in instructional technology (IT) in the College of Education at Eagle University, a major research university with very high research activity. Participants in the study were eleven former doctoral students from Eagle University (pseudo.), six of whom met the requirements for graduation (completers) and five of whom ended the pursuit of the doctoral degree in instructional technology at EU (non-completers). A qualitative study informed by phenomenology, the purpose of the study was to explore these phenomena from the perspective of the students. Postmodernism served as the theoretical framework. Participants were interviewed using the structured interview guide developed by the researcher. Two important findings were that only one of the eleven students knew what to expect from the program; and that completers were more likely to report that their primary motivation for pursuing the Ph.D. was for personal satisfaction. Recommendations were made based on student feedback, and included implications for students as well as implications for the university/program. Examples of advice for students were: 1) contemplate their goal(s) in pursuing the Ph.D. and consider the impact if something happened to alter that goal, and 2) seek out doctoral support groups and begin to establish relationships with current members. Two selected recommendations for the university/program were 1) develop a pre-application seminar or eLearning module to provide potential doctoral students with a realistic understanding of the program, and 2) consider developing a mentoring program that matched more experienced students or non-advisory professors to new students. Results of the study indicated that multiple factors affected both completers and non-completers; and these factors were often similar. However, among the key factors separating completers from non-completers were the determination of the student and the quality of the advisor relationship.

Finding the maximum size of a matching in an undirected graph and finding the maximum size of branching in a directed graph can be formulated as matrix rank problems. The Tutte matrix, introduced by Tutte as a representation of an undirected graph, has rank equal to the maximum number of vertices covered by a matching in the associated graph. The branching matrix, a representation of a directed graph, has rank equal to the maximum number of vertices covered by a branching in the associated graph. A mixed graph has both undirected and directed edges, and the matching forest problem for mixed graphs, introduced by Giles, is a generalization of the matching problem and the branching problem. A mixed graph can be represented by the matching forest matrix, and the rank of the matching forest matrix is related to the size of a matching forest in the associated mixed graph. The Tutte matrix and the branching matrix have indeterminate entries, and we describe algorithms that evaluate the indeterminates as rationals in such a way that the rank of the evaluated matrix is equal to the rank of the indeterminate matrix. Matroids in the context of graphs are discussed, and matroid formulations for the matching, branching, and matching forest problems are given.

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Extensive research has been conducted on college student retention and graduation and many studies have found certain characteristics to be predictive of successful completion of college. However, few studies have focused on a target population which is primarily Hispanic. This study examined the 1990 entering freshmen class of students at Texas A&M University-Kingsville (TAMUK), of which more than 68% were Hispanic. An attempt was made to examine characteristics that would predict success, defined as graduation from TAMUK. Data derived from institutional records and the Texas Higher Education Coordinating Board were examined using descriptive statistics and stepwise multiple logistic regression. Pre-college characteristics studied included age, gender, ethnicity, marital status, high school GPA (Grade Point Average), high school class rank, high school of origin, county of origin, and American College Test (ACT) and the Scholastic Assessment Test (SAT) scores. In-college characteristics studied included residency status, admission status, enrollment status, number of hours enrolled fall 1990, college major, the Texas Academic Skills Program (TASP) scores, developmental courses, semester GPA??s, academic standing, and finally, attrition, transfer or graduation status. The fall 1990 entering students were evenly divided between males (53.4%) and females (46.6%), were young (79% were age 19 or less), single (91.4%), and Hispanic (68.2%). Almost half (46%) of the students came from high schools within 50 miles of Kingsville. The mean college entrance exam scores (ACT=16.76 and SAT=766) were well below the national means of 21 and 999, respectively. Of the 1106 entering freshmen, 307 (27.7%) graduated from TAMUK within the 10 years under study. An additional 490 (44.3%) transferred to other state institutions, and 309 (27.9%) dropped out of TAMUK and did not enroll in any other state college or university. The fall-tospring attrition rate was only 16.5%; however, the fall-to-fall attrition rate was 50.0% at the end of the first year. Stepwise multiple logistic regression (backward) analysis revealed that only high school GPA and the ACT composite score were statistically significant predictors of graduation.

Third generation full-waveform (FW) LIDAR systems image an entire scene by emitting laser pulses in particular directions and measuring the echoes. Each of these echoes provides range measurements about the objects intercepted by the laser pulse along a specified direction. By scanning through a specified region using a series of emitted pulses and observing their echoes, connected 1D profiles of 3D scenes can be readily obtained. This extra information has proven helpful in providing additional insight into the scene structure which can be used to construct effective characterizations and classifications. Unfortunately, massive amounts of data are typically collected which impose storage, processing and transmission limitations. To address these problems, a number of compression approaches have been developed in the literature. These, however, generally require the initial acquisition of large amounts of data only to later discard most of it by exploiting redundancies, thus sampling inefficiently. Based on this, our main goal is to apply efficient and effective LIDAR sampling schemes that achieve acceptable reconstruction quality of the 3D scenes. To achieve this goal, we propose on using compressive sampling by emitting pulses only into random locations within the scene and collecting only the corresponding returned FW signals. Under this framework, the number of emissions would typically be much smaller than what traditional LIDAR systems require. Application of this requires, however, that scenes contain many degrees of freedom. Fortunately, such a requirement is satisfied in most natural and man-made scenes. Here, we propose to use a measure of rank as the measure of degrees of freedom. To recover the connected 1D profiles of the 3D scene, matrix completion is applied to the tensor slices. In this paper, we test our approach by showing that recovery of compressively sampled 1D profiles of actual 3D scenes is possible using only a subset of measurements.

Statement of the Problem The national six-year graduation rate is less than 60 percent. This alarming yet consistent feature of higher education has led researchers like Bean (1980), Tinto (1975, 1993), Astin (1993), Adelman (1999), Braxton (2000) and DesJardins (2002) to create a body of research which attempts to explain the causes of student attrition and suggest possible interventions. Learning Communities and Freshman Experience courses are two efforts to improve retention which are derived from this research. The purpose of this study was to test portions of Tinto’s longitudinal model of institutional departure that relates academic fit to persistence and degree completion. The study examined placement testing and advising procedures and the effects these procedures have on eventual degree completion. The results of this study should inform the academic community about the efficacy of using a placement test to promote academic fit for first time freshmen enrolled in mathematics courses. Method This quantitative study was an ex-post facto, quasi experimental design which compared three procedures for placement into the initial college mathematics course and the impact on retention and eventual degree completion. The data for this study was obtained from existing data sources. Logistic regression was used to compare the three placement methods and the effect on the odds of eventual degree completion. Results While the placement instrument did provide some useful information for placement decisions about some courses, it does not provide as much information as other available measures, in particular, the high school history expressed as the grade point average. Quality point production at the end of the first year was found to be a strong predictor of eventual graduation. The results suggest that for each one unit increase in the quality points earned the odds of graduation are 1.042 times better. Statistically significant differences were found in the efficacy of the different placement methods; however these differences were overshadowed by the effect of the introduction of a new mathematics course. The average grade in the initial collegiate math class for the groups in this study has risen from a low of 1.87 to 2.37 after the introduction of Math Modeling to the curriculum.

This study sought to understand why some students at Eagle University (pseudo.) complete the doctoral program in instructional technology while others do not. The study explores factors and issues affecting doctoral attrition and completion of the Ph.D. in instructional technology (IT) in the College of Education at Eagle University, a major research university with very high research activity. Participants in the study were eleven former doctoral students from Eagle University (pseudo.), six of whom met the requirements for graduation (completers) and five of whom ended the pursuit of the doctoral degree in instructional technology at EU (non-completers). A qualitative study informed by phenomenology, the purpose of the study was to explore these phenomena from the perspective of the students. Postmodernism served as the theoretical framework. Participants were interviewed using the structured interview guide developed by the researcher. Two important findings were that only one of the eleven students knew what to expect from the program; and that completers were more likely to report that their primary motivation for pursuing the Ph.D. was for personal satisfaction. Recommendations were made based on student feedback, and included implications for students as well as implications for the university/program. Examples of advice for students were: 1) contemplate their goal(s) in pursuing the Ph.D. and consider the impact if something happened to alter that goal, and 2) seek out doctoral support groups and begin to establish relationships with current members. Two selected recommendations for the university/program were 1) develop a pre-application seminar or eLearning module to provide potential doctoral students with a realistic understanding of the program, and 2) consider developing a mentoring program that matched more experienced students or non-advisory professors to new students. Results of the study indicated that multiple factors affected both completers and non-completers; and these factors were often similar. However, among the key factors separating completers from non-completers were the determination of the student and the quality of the advisor relationship.

This dissertation investigates the problem of estimating a kernel over a large graph based on a sample of noisy observations of linear measurements of the kernel. We are interested in solving this estimation problem in the case when the sample size is much smaller than the ambient dimension of the kernel. As is typical in high-dimensional statistics, we are able to design a suitable estimator based on a small number of samples only when the target kernel belongs to a subset of restricted complexity. In our study, we restrict the complexity by considering scenarios where the target kernel is both low-rank and smooth over a graph. Using standard tools of non-parametric estimation, we derive a minimax lower bound on the least squares error in terms of the rank and the degree of smoothness of the target kernel. To prove the optimality of our lower-bound, we proceed to develop upper bounds on the error for a least-square estimator based on a non-convex penalty. The proof of these upper bounds depends on bounds for estimators over uniformly bounded function classes in terms of Rademacher complexities. We also propose a computationally tractable estimator based on least-squares with convex penalty. We derive an upper bound for the computationally tractable estimator in terms of a coherence function introduced in this work. Finally, we present some scenarios wherein this upper bound achieves a near-optimal rate. The motivations for studying such problems come from various real-world applications like recommender systems and social network analysis.

The present study investigated age differences in implicit memory for positive, negative and neutral words. We also explored how cognitive control and time of testing influence emotional memory. Participants completed a one-back picture comparison task with superimposed distracting emotional and neutral words. Memory for distracting words was tested using an implicit memory test and cognitive control by a flanker task. Priming was significant for negative but not for positive and neutral words. Memory for distracting negative words was greater at non-optimal times of day for young adults but similar across the day for older adults. A high level of cognitive control was related to greater priming for negative words in young adults and lower priming in older adults. Priming for neutral words was enhanced in high cognitive control participants when stimuli contained emotional words that were relevant to one’s goals, implicating the use of emotion regulation at an unconscious level.