Expert Perspectives on the Future of Physical Education in Higher Education

Despite physical education teacher education/physical education sport pedagogy’s (PETE/PESP’s) significant strides in the past century, there are concerns about its future. Grounded in participatory research, and through an interview-Delphi method, this study purposefully sampled five male PETE/PESP scholars’ expert critiques of current threats, projections of probable and preferable futures, and recommendations for transformation. Data were analyzed through an interpretive approach. Findings included: (a) limitations in physical education policy, teacher preparation, and status as key threats; (b) more of the same conflicts in the probable future and increased cooperation regarding conflicts in the preferable future; and (c) proactively revising the subdisciplinary framework and doctoral studies as transformative recommendations. We interpret issues critiqued as threatening PETE/PESP’s future, and projections of the probable future, as reflective of neoliberal rationality that has renormalized academic work in de-democratizing ways. We interpret that projections of preferable futures and transformative recommendations call for a re-emphasis on ethical academic attitudes to resist neoliberalization and reclaim democracy.     

A comparison of conventional linear regression methods and neural networks for forecasting educational spending

This study presents an application of neural network methods for forecasting per pupil expenditures in public elementary and secondary schools in the United States. Using annual historical data from 1959 through 1990, forecasts were prepared for the period from 1991 through 1995. Forecasting models included the multivariate regression model developed by the National Center for Education Statistics for their annual Projections of Education Statistics Series, and three neural architectures: (1) recurrent backpropagation; (2) Generalized Regression; and (3) Group Method of Data Handling. Forecasts were compared for accuracy against actual values for educational spending for the period. Regarding prediction accuracy, neural network results ranged from comparable to superior with respect to the NCES model. Contrary to expectations, the most successful neural network procedure yielded its results with an even simpler linear form than the NCES model. The findings suggest the potential value of neural algorithms for strengthening econometric models as well as producing accurate forecasts. [JEL C45, C53, I21]     

How Students Combine Resources to Make Conceptual Breakthroughs

Research in Science Education - We use the framework of cognitive resources to investigate how students construct understanding of a complex physics topic, namely, a photovoltaic cell. By observing...     

Patterns of intellectual differences of black,hispanic, and white children

Patterns of intellectual differences among children of different ethnic groups on the Wechsler Intelligence Scale for Children-Revised (WISC-R) were examined. Full Scale IQ was used as a covariate in a multivariate analysis of covariance. The results indicated that when overall IQ is held constant, Black children perform better on verbal tasks, Hispanic children perform better on visual-spatial tasks, and White children perform better on tasks requiring abstract thinking and knowledge of facts. The implications of these results regarding assessment practices are discussed.