Using structural equation modeling to assess two theoretical models that explain dropout

This study evaluated the relative fit of both Finn's (1989) Participation-Identification and Wehlage, Rutter, Smith, Lesko and Fernandez's (1989) School Membership models of high school completion to a sample of 4,597 eighth graders taken from the National Educational Longitudinal Study of 1988, (NELS:88), utilizing structural equation modeling techniques. This study found support for the importance of educational engagement as a factor in understanding academic achievement. The Participation-Identification model was particularly well fitting when applied to the sample of high school completers, dropouts (both overall and White dropouts) and African-American students. This study also confirmed the contribution of school environmental factors (i.e., size, diversity of economic and ethnic status among students) and family resources (i.e., availability of learning resources in the home and parent educational level) to students' educational engagement. Based on these findings, school social workers will need to be more attentive to utilizing macro-level interventions (i.e., community organization, interagency coordination) to achieve the organizational restructuring needed to address future challenges. The support found for the Participation-Identification model supports a shift in school social workers' attention from reactive attempts to improve the affective-interpersonal lives of students to proactive attention to their academic lives. The model concentrates school social work practices on the central mission of schools, which is educational engagement. School social workers guided by this model would be encouraged to seek changes in school policies and organization that would facilitate educational engagement. ^

Structural Health Monitoring using Index Based Reasoning for Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) may develop cracks, erosion, delamination or other damages due to aging, fatigue or extreme loads. Identifying these damages is critical for the safe and reliable operation of the systems. ^ Structural Health Monitoring (SHM) is capable of determining the conditions of systems automatically and continually through processing and interpreting the data collected from a network of sensors embedded into the systems. With the desired awareness of the systems’ health conditions, SHM can greatly reduce operational cost and speed up maintenance processes. ^ The purpose of this study is to develop an effective, low-cost, flexible and fault tolerant structural health monitoring system. The proposed Index Based Reasoning (IBR) system started as a simple look-up-table based diagnostic system. Later, Fast Fourier Transformation analysis and neural network diagnosis with self-learning capabilities were added. The current version is capable of classifying different health conditions with the learned characteristic patterns, after training with the sensory data acquired from the operating system under different status. ^ The proposed IBR systems are hierarchy and distributed networks deployed into systems to monitor their health conditions. Each IBR node processes the sensory data to extract the features of the signal. Classifying tools are then used to evaluate the local conditions with health index (HI) values. The HI values will be carried to other IBR nodes in the next level of the structured network. The overall health condition of the system can be obtained by evaluating all the local health conditions. ^ The performance of IBR systems has been evaluated by both simulation and experimental studies. The IBR system has been proven successful on simulated cases of a turbojet engine, a high displacement actuator, and a quad rotor helicopter. For its application on experimental data of a four rotor helicopter, IBR also performed acceptably accurate. The proposed IBR system is a perfect fit for the low-cost UAVs to be the onboard structural health management system. It can also be a backup system for aircraft and advanced Space Utility Vehicles. ^

Undergraduate Students’ Perceptions of Study Abroad and Their Level of Achievement of Global Learning Outcomes

This study expanded on current research on study abroad and global learning, using the Global Perspective Inventory (GPI), and conducted at Florida International University (FIU) in Miami, FL. The GPI assesses the holistic development of a global perspective in higher education within three domains and their respective FIU-determined equivalents: cognitive (global awareness), intrapersonal (global perspective), and interpersonal (global engagement). The main purpose of this study was to assess FIU’s undergraduate students’ perceptions of study abroad on their level of achievement of global awareness, global perspective, and global engagement. The secondary purpose was to determine how the students described their study abroad experience and achievement of global learning. The research design for this study consisted of parallel mixed methods. The quantitative component was an ex post facto with hypothesis design, using a pretest/posttest nonequivalent group methodology. FIU undergraduates (N=147) who studied abroad for one semester or more completed the GPI pre- and post-tests. Descriptive statistics and paired t-tests were conducted to compare the means. The interviews included 10 students, and were analyzed through Structural coding, Saldaña’s In Vivo coding, and Value coding. Quantitative analyses indicated positive changes in the students’ global awareness and global perspective. These analyses also showed that the FIU students achieved higher post-test means on all the domains of the GPI compared to other studies. Qualitative analyses showed that the students’ experiences incorporated all three global learning outcomes, most notably global awareness and perspective.