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.

Early detection of faults in the vehicle operating systems is a research domain of high significance to sustain full control of the systems since anomalous behaviors usually result in performance loss for a long time before detecting them as critical failures. In other words, operating systems exhibit degradation when failure begins to occur. Indeed, multiple presences of the failures in the system performance are not only anomalous behavior signals but also show that taking maintenance actions to keep the system performance is vital. Maintaining the systems in the nominal performance for the lifetime with the lowest maintenance cost is extremely challenging and it is important to be aware of imminent failure before it arises and implement the best countermeasures to avoid extra losses. In this context, the timely anomaly detection of the performance of the operating system is worthy of investigation. Early detection of imminent anomalous behaviors of the operating system is difficult without appropriate modeling, prediction, and analysis of the time series records of the system. Data based technologies have prepared a great foundation to develop advanced methods for modeling and prediction of time series data streams. In this research, we propose novel methodologies to predict the patterns of multivariate time series operational data of the vehicle and recognize the second-wise unhealthy states. These approaches help with the early detection of abnormalities in the behavior of the vehicle based on multiple data channels whose second-wise records for different functional working groups in the operating systems of the vehicle. Furthermore, a real case study data set is used to validate the accuracy of the proposed prediction and anomaly detection methodologies.

No description available.

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.