Attracting and Retaining Workers in Rural Communities

Many rural communities focus their development efforts on job creation. In the non-metropolitan portions of the Northern Great Plains, job creation efforts in the first half of this decade were quite successful. According to the Bureau of Economic Analysis (BEA, 2005), 167 of the 223 non-metropolitan counties in Nebraska, North Dakota and South Dakota saw an actual aggregate increase in total jobs (full and part-time) of 28,734, between the years 2001 and 2005.

Time series analysis of jaw muscle contraction and tissue deformation during mastication in miniature pigs

Masticatory muscle contraction causes both jaw movement and tissue deformation during function. Natural chewing data from 25 adult miniature pigs were studied by means of time series analysis. The data set included simultaneous recordings of electromyography (EMG) from bilateral masseter (MA), zygomaticomandibularis (ZM) and lateral pterygoid muscles, bone surface strains from the left squamosal bone (SQ), condylar neck (CD) and mandibular corpus (MD), and linear deformation of the capsule of the jaw joint measured bilaterally using differential variable reluctance transducers. Pairwise comparisons were examined by calculating the cross-correlation functions. Jaw-adductor muscle activity of MA and ZM was found to be highly cross-correlated with CD and SQ strains and weakly with MD strain. No muscle’s activity was strongly linked to capsular deformation of the jaw joint, nor were bone strains and capsular deformation tightly linked. Homologous muscle pairs showed the greatest synchronization of signals, but the signals themselves were not significantly more correlated than those of non-homologous muscle pairs. These results suggested that bone strains and capsular deformation are driven by different mechanical regimes. Muscle contraction and ensuing reaction forces are probably responsible for bone strains, whereas capsular deformation is more likely a product of movement.

Student and Faculty Perceptions of Technology’s Usefulness in Community College General Education Courses

Educational institutions of all levels invest large amounts of time and resources into instructional technology, with the goal of enhancing the educational effectiveness of the learning environment. The decisions made by instructors and institutions regarding the implementation of technology are guided by perceptions of usefulness held by those who are in control. The primary objective of this mixed methods study was to examine the student and faculty perceptions of technology being used in general education courses at a community college. This study builds upon and challenges the assertions of writers such as Prensky (2001a, 2001b) and Tapscott (1998) who claim that a vast difference in technology perception exists between generational groups, resulting in a diminished usefulness of technology in instruction. In this study, data were gathered through student surveys and interviews, and through faculty surveys and interviews. Analysis of the data used Kendall’s Tau test for correlation between various student and faculty variables in various groupings, and also typological analysis of the transcribed interview data. The analysis of the quantitative data revealed no relationship between age and perception of technology’s usefulness. A positive relationship was found to exist between the perception of the frequency of technology use and the perception of technology’s effectiveness, suggesting that both faculty members and students believed that the more technology is used, the more useful it is in instruction. The analysis of the qualitative data revealed that both faculty and students perceive technology to be useful, and that the most significant barriers to technology’s usefulness include faulty hardware and software systems,lack of user support, and lack of training for faculty. The results of the study suggest that the differences in perception of technology between generations that are proposed by Prensky may not exist when comparing adults from the younger generation with adults from the older generation. Further, the study suggests that institutions continue to invest in instructional technology, with a focus on high levels of support and training for faculty, and more universal availability of specific technologies, including web access, in class video, and presentation software. Adviser: Ronald Joekel

Generalizing the dynamic field theory of spatial cognition across real and developmental time scales

Within cognitive neuroscience, computational models are designed to provide insights into the organization of behavior while adhering to neural principles. These models should provide sufficient specificity to generate novel predictions while maintaining the generality needed to capture behavior across tasks and/or time scales. This paper presents one such model, the Dynamic Field Theory (DFT) of spatial cognition, showing new simulations that provide a demonstration proof that the theory generalizes across developmental changes in performance in four tasks—the Piagetian A-not-B task, a sandbox version of the A-not-B task, a canonical spatial recall task, and a position discrimination task. Model simulations demonstrate that the DFT can accomplish both specificity—generating novel, testable predictions—and generality—spanning multiple tasks across development with a relatively simple developmental hypothesis. Critically, the DFT achieves generality across tasks and time scales with no modification to its basic structure and with a strong commitment to neural principles. The only change necessary to capture development in the model was an increase in the precision of the tuning of receptive fields as well as an increase in the precision of local excitatory interactions among neurons in the model. These small quantitative changes were sufficient to move the model through a set of quantitative and qualitative behavioral changes that span the age range from 8 months to 6 years and into adulthood. We conclude by considering how the DFT is positioned in the literature, the challenges on the horizon for our framework, and how a dynamic field approach can yield new insights into development from a computational cognitive neuroscience perspective.