Thesis (Ed. D.)--University of California, Los Angeles, 1989. / Vita. Includes bibliographical references (leaves 98-103).

Thesis (M.A.)--Boise State University, 2009. / Title from t.p. of PDF file (viewed May 4, 2010). Includes abstract. Includes bibliographical references (leaves 32-34).

Thesis (Ph. D.)--Illinois State University, 1995. / Title from title page screen, viewed April 21, 2006. Dissertation Committee: Ronald S. Halinski (chair), Kenneth H. Strand, James C. Palmer, George Padavil. Includes bibliographical references (leaves 109-116) and abstract. Also available in print.

Thesis (M.A.)--York University, 2004. Graduate Programme in Psychology. / Typescript. Includes bibliographical references (leaves 101-109). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url%5Fver=Z39.88-2004&res%5Fdat=xri:pqdiss &rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR11910

Determining which students are at-risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of both research and practice in K-12 education. The models produced from this type of predictive modeling research are increasingly used by high schools in Early Warning Systems to identify which students are at risk and intervene to support better outcomes. It has become common practice to re-build and validate these detectors, district-by-district, due to different data semantics and various risk factors for students in different districts. As these detectors become more widely used, however, a new challenge emerges in applying these detectors across a broad spectrum of school districts with varying availability of past student data. Some districts have insufficient high-quality past data for building an effective detector. Novel approaches that can address the complex data challenges a new district presents are critical for advancing the field. Using an ensemble-based algorithm, I develop a modeling approach that can generate a useful model for a previously unseen district. During the ensembling process, my approach, District Similarity Ensemble Extrapolation (DSEE), weights districts that are more similar to the Target district more strongly during ensembling than less similar districts. Using this approach, I can predict student-at-risk status effectively for unseen districts, across a range of grade ranges, and achieve prediction goodness but ultimately fails to perform better than the previously published Knowles (2015) and Bowers (2012) EWS models proposed for use across districts.

The report presents an implemention of a classification algorithm for the Institutional Analysis Project. The algorithm used in this project is the decision tree classification algorithm which uses a gain ratio attribute selectionmethod. The algorithm discovers the hidden rules from the student records, which are used to predict whether or not other students are at risk of dropping out. It is shown that special rules exist in different data sets, each with their natural hidden knowledge. In other words, the rules that are obtained depend on the data that is used for classification. In our preliminary experiments, we show that between 55-78 percent of data with unknown class lables can be correctly classified, using the rules obtained from data whose class labels are known. We feel this is acceptable, given the large number of records, attributes, and attribute values that are used in the experiments. The project results are useful for large data set analysis. / viii, 38 leaves ; 29 cm. --

The influence of background characteristics on dropouts from a Job Corps Center was investigated using a Biographical Information Blank. Successful and unsuccessful male and female volunteer Corpsmembers were compared and the data analyzed by univariate and multivariate statistical techniques. Results strongly support the prediction that biographical characteristics are important in determining Corpsmember failure in the program. It was also found that the nature of family and peer relationships; previous social adjustment and structured activity and factors related to ethnicity and cultural attitudes are influential. There are also indications that potential dropouts may be affected in a positive manner to complete their training.

School districts constantly struggle to find solutions to address the high school dropout problem. Literature supports the need to identify and intervene with these students earlier and in more systemic ways. The purpose of this study was to conduct a longitudinal examination of the relationship between sustained afterschool participation and the host district’s early warning index (EWI) associated with school dropout. Data included 65,341 students participating in an urban school district’s after school program from school years 2000-2001 through 2011-2012. The district serves more than 80,000 students annually. Data represented students in Pre-Kindergarten through Grade 12, and length of participation ranged from 1 through 12 years. Results indicated that student risk increased over time and that persistent participation in afterschool programming had a significant relationship with student individual growth trajectories. Slower growth rates, as evidenced through successive models, supported students being positively impacted by program participation. Additionally, participation was more meaningful if students persisted, as noted in the lower EWI rates, as compared to students who attended less consistently.

The purpose of this study was to develop a prediction model for graduation rate at non-residential research universities. As well, this study investigated, described, and compared the student characteristics of non-residential and residential institutions. Making distinctions between significant predictor variables at non-residential research universities and significant predictor variables at residential institutions was also an aim. The researcher obtained data from the Integrated Postsecondary Data System. Student and institutional variables were analyzed using descriptive statistics, independent samples t-tests, analysis of variance, and regression analyses. Results indicated that student and institutional characteristics can be used to significantly predict graduation rate at nonresidential institutions with student variables yielding greater predictive power than institutional variables. As well, residential status was found to moderate the relationship between undergraduate enrollment and graduation rate. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

This study examines the relationship between exercise adherence and several factors: self-motivation; attitudinal commitment; predisposing, enabling, and reinforcing (PER) factors; and barriers related to exercise. The sample (N=431) consists of employees at Texas Instruments, Incorporated in Dallas, Texas. The sample was placed into six comparison groups: high adherers, low adherers, nonparticipants who exercise, nonparticipants who do not exercise, dropouts who exercise and dropouts who do not exercise. Using a one-way ANOVA, the results show significance (p<.01) among the groups for: self-motivation and barriers. Attitudinal commitment and PER factors did not show significance. The results can be applied to worksite health programs to increase exercise adherence among employee populations.