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Student Mobility (student + mobility)
Selected AbstractsBridges to learning: international student mobilities, education agencies and inter-personal networksGLOBAL NETWORKS, Issue 4 2008FRANCIS LEO COLLINS Abstract International education is a fundamentally transnational project. It relies on the movement of individuals or knowledge across national borders, disturbs the centrality of the nation-state in educational reproduction, and is facilitated by economic and social networks that act as bridges between countries of origin and education. In this article, I address this latter point through reference to research conducted with South Korean international students in Auckland, New Zealand. In particular, I discuss the emergence of transnational social and economic activities that are facilitating the movement of international students from South Korea to Auckland , activities that might usefully be understood as forming ,bridges to learning'. These include the activities of education agencies, immigrant entrepreneurs and the interpersonal relationships with which many students engage in the negotiation of their transnational lives. In a broader sense I illustrate how the emerging mobilities of international students cannot be viewed as independent of other phenomena but must be seen as embedded within transnational processes that take place at different geographic and social scales. [source] Student and Teacher Perceptions of School Climate: A Multilevel Exploration of Patterns of DiscrepancyJOURNAL OF SCHOOL HEALTH, Issue 6 2010Mary M. Mitchell PhD BACKGROUND: School climate has been linked with improved academic achievement and reduced discipline problems, and thus is often a target of school improvement initiatives. However, few studies have examined the extent to which student and teacher perceptions vary as a function of individual, classroom, and school characteristics, or the level of congruence between teachers' and their students' perceptions of school climate. METHODS: Using data from 1881 fifth-grade students and their 90 homeroom teachers, we examined parallel models of students' and teachers' perceptions of overall school climate and academic emphasis. Two additional models were fit that assessed the congruence between teacher and student perceptions of school climate and academic emphasis. RESULTS: Multilevel analyses indicated that classroom-level factors were more closely associated with teachers' perceptions of climate, whereas school-level factors were more closely associated with the students' perceptions. Further analyses indicated an inverse association between student and teacher ratings of academic emphasis, and no association between student and teacher ratings of overall climate. CONCLUSIONS: Teacher ratings were more sensitive to classroom-level factors, such as poor classroom management and proportion of students with disruptive behaviors, whereas student ratings were more influenced by school-level factors such as student mobility, student-teacher relationship, and principal turnover. The discrepancy in ratings of academic emphasis suggests that while all of the respondents may have shared objectively similar experiences, their perceptions of those experiences varied significantly. These results emphasize the importance of assessing both student and teacher perceptions in future research on school climate. [source] Staff- and School-Level Predictors of School Organizational Health: A Multilevel AnalysisJOURNAL OF SCHOOL HEALTH, Issue 6 2007Katherine Bevans PhD ABSTRACT Background:, An organizationally healthy school environment is associated with favorable student and staff outcomes and thus is often targeted by school improvement initiatives. However, few studies have differentiated staff-level from school-level predictors of organizational health. Social disorganization theory suggests that school-level factors, such as faculty turnover, student mobility, and concentration of student poverty, would be negatively associated with school organizational health, but these relationships may be moderated by staff-level factors. Methods:, The present study examined the association among school- and staff-level predictors of staff-perceived school organizational health (eg, academic emphasis, collegial leadership, and staff affiliation), as measured by the Organizational Health Inventory. Results:, Multilevel analyses on data from 1395 staff across 37 elementary schools indicated that school membership accounted for between 26% and 35% of the variance in different components of staff-perceived organizational health. Two-level hierarchical analyses indicated that both school- and staff-level characteristics are important predictors of organizational health. Furthermore, some school and staff characteristics interacted to predict staff affiliation and collegial leadership. Conclusions:, Findings suggest that factors at both the school and staff level are important potential targets for school improvement. Administrators aiming to improve relationships among staff members should be cognizant of staff-level characteristics (race, age, and role in school) associated with less favorable perceptions of the school environment, whereas efforts to enhance student work ethic and discipline should target schools with specific school-level characteristics (high rates of faculty turnover and student mobility). [source] Building a Partnership to Evaluate School-Linked Health Services: The Cincinnati School Health Demonstration ProjectJOURNAL OF SCHOOL HEALTH, Issue 10 2005Barbara L. Rose Partners from the Cincinnati Health Department, Cincinnati Public Schools, Cincinnati Children's Hospital Medical Center, and The Health Foundation of Greater Cincinnati wanted to determine if levels of school-linked care made a difference in student quality of life, school connectedness, attendance, emergency department use, and volume of referrals to health care specialists. School nurses, principals and school staff, parents and students, upper-level managers, and health service researchers worked together over a 2.5-year period to learn about and use new technology to collect information on student health, well-being, and outcome measures. Varying levels of school health care intervention models were instituted and evaluated. A standard model of care was compared with 2 models of enhanced care and service. The information collected from students, parents, nurses, and the school system provided a rich database on the health of urban children. School facilities, staffing, and computer technology, relationship building among stakeholders, extensive communication, and high student mobility were factors that influenced success and findings of the project. Funding for district-wide computerization and addition of school health staff was not secured by the end of the demonstration project; however, relationships among the partners endured and paved the way for future collaborations designed to better serve urban school children in Cincinnati. (J Sch Health. 2005;75(10):363-369) [source] Using evaluation data to strike a balance between stakeholders and accountability systemsNEW DIRECTIONS FOR EVALUATION, Issue 117 2008Lisa N. T. Schmitt A district evaluator in a large Texas district examines new challenges arising since implementation of No Child Left Behind, relating to (1) navigating competing requirements in state and federal accountability systems; (2) evaluating effectiveness of sanctions districts are required to address; (3) using scientifically based research (SBR) to select effective programs and interventions; and (4) initiating SBR given high student mobility, inefficient data-management systems, and competing priorities of local schools. This chapter details these challenges for district-level evaluators and highlights how they can implement processes that strike a balance between supporting decision making and conducting rigorous evaluation. © Wiley Periodicals, Inc. [source] Considering student mobility in retention outcomesNEW DIRECTIONS FOR INSTITUTIONAL RESEARCH, Issue 131 2006Sutee Sujitparapitaya Director This case study represents an initial attempt by a university to employ data-mining techniques to study a ternary attrition variable produced by integrating multiple internal and external databases. [source] | ||||||||||