Student Characteristics That Predict Persistence in Community College Online Courses

Abstract

This study examined the student characteristics of learning style, locus of control, computer experience and access, and online course experience on persistence of community college students in online courses. An online survey instrument based on the Barsch Learning Style Inventory (1996 Barsch, J. 1996. Barsch learning style inventory, Novato, CA: Academic Therapy Publications.  [Google Scholar]), the Abbreviated Measure of Internal–External Locus of Control (1974), and a computer experience scale that was developed by the researchers was completed by 225 students enrolled in online courses at five Florida community colleges. Logistic regression analysis identified a three-variable model (auditory learning style, grade point average, and basic computer skills) that was significant in predicting online student success. Academic and student support interventions are suggested for community college students who may be affected by these predictors.     

‘It's a different world out there’: improving how academics prepare health science students for rural and Indigenous practice in Australia

Rural and Aboriginal and Torres Strait Islander (Indigenous) health content in undergraduate health science curricula in Western Australia has been limited. In 2008, a three-and-a-half-day, rurally-based, intercultural and inter-disciplinary programme for academics from three universities aimed to improve how academics prepared health science students for work in this area. Situated learning theory underpinned the programme's design, which prioritised context and participation in the construction of knowledge: academics lived ‘on country’ and participated in the lived experience of a rural and Indigenous community. Semi-structured phone interviews with 21 academics four months later indicated this approach had radically changed thinking and led to a desire to improve rural and Indigenous health and teaching practice. Targeting academics to learn about rural and Indigenous health in situ is one promising strategy for improving undergraduate health science education in this priority area.     

The role of collaborative networks in supporting the innovation performances of lagging-behind European regions

In rapidly changing regional economies, less innovative European regions (henceforth referred to as lagging-behind regions) must actively work to reduce the gap between them and knowledge-intensive regions. Recent literature has stressed that the lack of efficient institutional settings reduces the opportunities of local knowledge spillover and increases the need for local organisations to exploit collaborative networks to better support their innovation performance. In this light, since increasing attention has recently been directed at the role of inter-regional collaborations, we have measured the capacity of local innovative organisations embedded in lagging-behind European regions to develop internal and external regional inventors’ networks by exploring their collaborative patenting processes. Then, a seven-year panel dataset (2002–2008) was organised using patents data at a regional level to validate the research hypothesis that collaborations, and specifically with highly innovative (knowledge-intensive) regions, positively affect the innovation performances of lagging-behind regions. Finally, the implications of EU policies for supporting lagging-behind regions are discussed.     

Control of impact loading during distracted running before and after gait retraining in runners

Gait retraining using visual biofeedback has been reported to reduce impact loading in runners. However, most of the previous studies did not adequately examine the level of motor learning after training, as the modified gait pattern was not tested in a dual-task condition. Hence, this study sought to compare the landing peak positive acceleration (PPA) and vertical loading rates during distracted running before and after gait retraining. Sixteen recreational runners underwent a two-week visual biofeedback gait retraining program for impact loading reduction, with feedback on the PPA measured at heel. In the evaluation of PPA and vertical loading rates before and after the retraining, the participants performed a cognitive and verbal counting task while running. Repeated measures ANOVA indicated a significant interaction between feedback and training on PPA (F = 4.642; P = 0.048) but not vertical loading rates (F > 1.953; P > 0.067). Pairwise comparisons indicated a significantly lower PPA and vertical loading rates after gait retraining (P < 0.007; Cohen’s d > 0.68). Visual feedback after gait retraining reduced PPA and vertical loading rates during distracted running (P < 0.033; Cohen’s d > 0.36). Gait retraining is effective in lowering impact loading even when the runners are distracted. In dual-task situation, visual biofeedback provided beneficial influence on kinetics control after gait retraining.     

A simple method for assessment of muscle force,velocity, and power producing capacities from functional movement tasks

A range of force (F) and velocity (V) data obtained from functional movement tasks (e.g., running, jumping, throwing, lifting, cycling) performed under variety of external loads have typically revealed strong and approximately linear F–V relationships. The regression model parameters reveal the maximum F (F-intercept), V (V-intercept), and power (P) producing capacities of the tested muscles. The aim of the present study was to evaluate the level of agreement between the routinely used “multiple-load model” and a simple “two-load model” based on direct assessment of the F–V relationship from only 2 external loads applied. Twelve participants were tested on the maximum performance vertical jumps, cycling, bench press throws, and bench pull performed against a variety of different loads. All 4 tested tasks revealed both exceptionally strong relationships between the parameters of the 2 models (median R = 0.98) and a lack of meaningful differences between their magnitudes (fixed bias below 3.4%). Therefore, addition of another load to the standard tests of various functional tasks typically conducted under a single set of mechanical conditions could allow for the assessment of the muscle mechanical properties such as the muscle F, V, and P producing capacities.