Browsing by Subject "Active learning"
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Item Active And Collaborative Learning At A Community College(2015) Walsh, Roy Michael; Spaid, Robin L.; Education Administration and Supervision; Doctor of EducationThe purpose of this study was to use the theory of student engagement to compare levels of active and collaborative learning using three years of Community College Survey of Student Engagement (CCSSE) data at a community college in a mid-Atlantic state. The independent variables were gender, racial identification, and levels of enrollment data for 2010, 2012, 2014. The dependent variable was the level of active and collaborative learning, as measured by CCSSE. Kuh's (2003) research in student engagement served as the theoretical framework for this study. All students surveyed for the CCSSE for years 2010, 2012, and 2014 at a community college in a mid-Atlantic state were used in this study. There was a total of 1,111, 1,291, and 1,415 students who participated in 2010, 2012, and 2014, respectively. Only the scores for one of the five CCSSE benchmarks, Active and Collaborative Learning, were used for this study for years 2010, 2012, and 2014. Three research questions were developed to assess the levels of engagement of students at the community college in a mid-Atlantic state. This researcher sought to determine if there were differences in the levels of engagement among all students participating in the CCSSE survey, among the seven racial identification categories, and between male and female students. The ex post facto data collected for the study was analyzed using the SPSS statistical package. An alpha level of .05 was used to test the null hypotheses. Inferential statistics, including t-test and ANOVA, were used to analyze the data and reach conclusions about the levels of active and collaborative learning at the community college. This study contributes to the limited body of literature that examines student engagement on the community college level. Recommendations for professional practice and further research are provided.Item An expeditionary learning approach to effective curriculum mapping : formalizing the process by exploring a user centered framework(2014-05) Carnaghan, Ian; Blodgett, Bridget; Atkin, Susan; University of Baltimore. School of Information Arts and Technologies; University of Baltimore. Doctor of Science in Information and Interaction DesignMonarch Academy is an Expeditionary Learning (EL) institution, which utilizes a non-traditional educational model that combines all subjects into semester-long projects known as expeditions. In order to properly track the progress of students and to ensure the school is meeting its educational goals, including alignment with Common Core, a process called curriculum mapping has been implemented informally; however, the process has not been centralized nor is it easily accessible by staff and administrators. Commercial curriculum mapping software was researched by administrators, but none met the unique requirements of EL. This study explores and defines a curriculum mapping solution that meets Monarch Academy's needs by providing a centralized, accessible, manageable, and user-centered framework.Item Iterative training sampling and active learning approaches to hyperspectral image classification(2020-01-01) Ma, Kenneth Yeonkong; Chang, Chein-I; Computer Science and Electrical Engineering; Engineering, ElectricalAs one of fundamental tasks in remote sensing, hyperspectral image classification (HSIC) has attracted considerable interest. However, two challenging issues arise in HSIC. One is inconsistent classification, which is mainly caused by random training sampling (RTS) of selecting training samples. The other is mixed pixels classification problem which cannot be resolved by conventional pure pixel-based classifier.The first part of this dissertations is to develop a new framework for training sample selection, called iterative training sampling (ITS) which aims to improving the traditional RTS, while reducing the classification inconstancy at the same time. The ITS can be implemented in conjunction with any arbitrary spectral-spatial (SS) classification systems, referred to as ITS spectral-spatial (ITS-SS) classification where ITS-SS expands data cubes iteratively by adding new spatial-filtered (SF-ed) classification maps via feedback loops to the current being processed data cube then regenerates a new set of training samples from expanded data cube to perform classification through an iterative process. What is more is that ITS can be further combined with active learning (AL), to derive a joint semi-supervised HSIC technique, to be called iterative training sample augmentation by AL (ITSA-AL). The central idea of ITSA-AL is to include training sample augmentation by AL in the iterative feedback process implemented in ITS at each iteration so that the a posteriori probability maps not only can be utilized to augment the current training samples but also can be fed back to expand the current data cube to include new a posteriori spectral-spatial information simultaneously. In general, a hyperspectral data sample is a mixture of multiple material signatures including background (BKG) which cannot be resolved by traditional pure pixel-based classifier. As a second part of this dissertations, we present a kernel-based approach to hyperspectral mixed pixel classification (HMPC) which includes two nonlinear mixed pixel classifiers, kernel constrained energy minimization (KCEM) and kernel linearly constrained minimum variance (KLCMV). Since KCEM/KLCMV produce real-valued abundance fractional maps for classification, the traditional discreate value-based evaluation tool is not directly applicable. In this case, the commonly used hard classification measures, average accuracy (AA) and overall accuracy (OA) can be further generalized to real-valued mixed class abundance fractional map-based soft classification measures via 3D receiver operating characteristic (3D ROC) analysis-derived detection measures. Finally, in order to relax the high computational complexity resulting from huge number of spectral bands, a novel concept called iterative band sampling (IBSam) is proposed. The central idea of IBSam is to re-sample bands in each iteration then feeds the SF-ed maps produced by different bands back to expand data cube in iterative manner. Extensive experiments are conducted to demonstrate the utility of IBSam where IBSam not only significantly reduce computing time but also produced the results that comparable to the results obtained by full bands.Item Using Nsse Data To Evaluate The Association Between Student Engagement And Retention At A Maryland Hbcu(2016) Saunders, Mark; Spaid, Robin L.; Higher Education Program; Doctor of PhilosophyUndergraduate retention and student engagement are two problems faced by most colleges and universities. Amid a more than adequate body of research into these phenomena, the possible relationship between student engagement and first-year retention at HBCUs appears to be an understudied phenomenon. The researcher used ex post facto data from the National Survey of Student Engagement (NSSE) to isolate three of the five NSSE benchmarks as independent variables: Level of Academic Challenge (LAC), Active and Collaborative Learning (ACL), and Student-faculty Interaction (SFI), to determine if they had any influence on the dependent variable of retention to Year Two at Mid-Atlantic State University. Kuh's Theory of Student Engagement (2001) provided the theoretical framework for the study. Data from Cohorts 2007, 2009, and 2011 (N = 474) at Mid-Atlantic University, a public HBCU, were analyzed inferentially using the binary logistic regression function in the SPSS statistical software package. The results were that statistically significant relationships existed between LAC and retention and between SFI and retention. The full statistical model of LAC, ACL, and SFI was also statistically significant in predicting retention for the 2007 cohort. This study adds to the body of literature concerning student engagement at HBCUs, with recommendations for future research and professional practice provided.