Technological factors of students’ persistence in online courses in higher education: The moderating role of gender,age and prior online course experience

Education and Information Technologies - The aim of this study was to verify if technological factors have an influence on persistence in online courses. A theoretical model encompassing seven...     

Predicting VO2max in College-Aged Participants Using Cycle Ergometry and Perceived Functional Ability

The purpose of this study was to develop a multiple linear regression model to predict treadmill VO_2max scores using both exercise and non-exercise data. One hundred five college-aged participants (53 male, 52 female) successfully completed a submaximal cycle ergometer test and a maximal graded exercise test on a motorized treadmill. The submaximal cycle protocol required participants to achieve a steady-state heart rate equal to at least 70% of age-predicted maximum heart rate (220-age), while the maximal treadmill graded exercise test required participants to exercise to volitional fatigue. Relevant submaximal cycle ergometer test data included a mean (±SD) ending steady-state heart rate and ending workrate equal to 164.2 ± 13.0 bpm and 115.3 ± 27.0 watts, respectively. Relevant non-exercise data included a mean (±SD) body mass (kg), perceived functional ability score, and physical activity rating score of 74.2 ± 15.1, 15.7 ± 4.3, and 4.7 ± 2.1, respectively. Multiple linear regression was used to generate the following prediction of (R = .91, standard error of estimates (SEE) = 3.36 ml·kg^?1·min^?1): VO_2max = 54.513 + 9.752 (gender, 1 = male, 0 = female) – .297 (body mass, kg) + .739 (perceived functional ability, 2–26) + .077 (work rate, watts) – .072 (steady-state heart rate). Each predictor variable was statistically significant (p < .05) with beta weights for gender, body mass, perceived functional ability, exercise workrate, and steady-state heart rate equal to .594, –.544, .388, .305, and –.116, respectively. The predicted residual sums of squares (PRESS) statistics reflected minimal shrinkage (R_PRESS = .90, SEE_PRESS = 3.56 ml·kg^?1·min^?1) for the multiple linear regression model. In summary, the submaximal cycle ergometer protocol and accompanying prediction model yield relatively accurate VO_2max estimates in healthy college-aged participants using both exercise and non-exercise data.     

Assessing Body Composition of Children and Adolescents Using Dual-Energy X-Ray Absorptiometry,Skinfolds, and Electrical Impedance

To determine the validity and reliability of percent body fat estimates in 177 boys and 154 girls between 12–17 years of age, percent body fat was assessed once using dual-energy X-ray absorptiometry and twice using the sum of two skinfolds and three bioelectrical impedance analysis devices. The assessments were repeated on 79 participants on a second day. The agreement between the percent body fat estimates from the four prediction methods and dual-energy X-ray absorptiometry was evaluated using Bland–Altman analyses and a mixed linear model. All methods were reliable within and between days. The sum of two skinfolds (±6.8% body fat), OMRON (OMRON Healthcare Inc., Vernon Hills, Illinois, USA) bioelectrical impedance analysis (0 ± 7.3% body fat), and TANITA 521 (TANITA Corporation, Tokyo, Japan) bioelectrical impedance analysis (±7.6% body fat) had wide prediction intervals and are acceptable for use in large population-based studies. The TANITA 300A bioelectrical impedance analysis is not recommended for use in children and adolescents due to its wide prediction interval (±8.1% body fat) and large bias. A criterion method should be used when the accurate assessment of body composition of an individual is critical.     

Index for Volume 54 (1983)

Abstract

The purpose of this study was to develop a regression equation to predict maximal oxygen uptake (VO₂max) based on nonexercise (N-EX) data. All participants (N= 100), ages 18–65 years, successfully completed a maximal graded exercise test (GXT) to assess VO₂max (M= 39.96 mL·kg ^-1· min ^-1 , SD = 9.54). The N-EX data collected just before the maximal GXT included the participant's age; gender; body mass index (BMI); perceived functional ability (PFA) to walk, jog, or run given distances; and current physical activity (PA-R) level. Multiple linear regression generated the following N-EX prediction equation (R = .93, SEE = 3.45 mL·kg ^-1· min ^-1 , %SEE= 8.62): VO₂max (mL·kg ^-1· min ^-1 ) = 48.0730 + (6.1779 x gender; women = 0, men = 1) – (0.2463 x age) – (0.6186 x BMI) + (0.7115 x PFA) + (0.6709 x PA-R). Cross validation using PRESS (predicted residual sum of squares) statistics revealed minimal shrinkage (R _p = .91 and SEE _p = 3.63 mL·kg ^-1· min ^-1 ); thus, this model should yield acceptable accuracy when applied to an independent sample of adults (ages 18–-65 years) with a similar cardiorespiratory fitness level. Based on standardized β-weights, the PFA variable (0.41) was the most effective at predicting VO₂max followed by age (-0.34), gender (0.33), BMI (-0.27), and PA-R (0.16). This study provides a N-EX regression model that yields relatively accurate results and is a convenient way to predict VO₂max in adult men and women.     

Building the university/public school partnership: A workshop for mentor teachers

Abstract

Strong teacher education programs acknowledge the importance of a partnership between teacher education and public school faculties and the important role mentor teachers play in the education of student teachers. Studies suggest that mentor teachers trained in supervision are more effective than those who are not. This article describes the development, implementation, and evaluation of a workshop for mentor teachers that reflects the collaborative effort of university and public school faculties. The particular focus of this workshop was on giving feedback to student teachers. Evaluation data suggest that mentor teachers felt the workshop provided helpful information.     

Tennis forehand kinematics change as post-impact ball speed is altered

Peak joint angles and joint angular velocities were evaluated for varying speed forehands in an attempt to better understand what kinematic variables are most closely related to increases in post-impact ball velocity above 50% of maximal effort. High-speed video was used to measure three-dimensional motion for 12 highly skilled tennis players who performed forehands at three different post-impact ball speeds: fast (42.7 +/- 3.8 m/s), medium (32.1 +/- 2.9 m/s), and slow (21.4 +/- 2.0 m/s). Several dominant-side peak joint angles (prior to ball impact) increased as post-impact ball speed increased from slow to fast: wrist extension (16%), trunk rotation (28%), hip flexion (38%), knee flexion (27%), and dorsiflexion (5%). Between the aforementioned peak joint angles and ball impact, dominant-side peak angular velocities increased as ball speed increased from slow to fast: peak wrist flexion (118%), elbow flexion (176%), trunk rotation (99%), hip extension (143%), knee extension (56%), and plantarflexion (87%). Most kinematic variables changed as forehand ball speed changed; however, some variables changed more than others, indicating that range of motion and angular velocity for some joints may be more closely related to post-impact ball speed than for other joints.     

Practice and Group Learning

Abstract

Although learning has always been a central topic for philosophy of education, little attention has been paid to the notion of group learning. This article outlines and discusses some plausible examples of group learning. Drawing on these examples, various principles and issues that surround the notion of group learning are identified and discussed. These principles and issues are then further elaborated in the context of a detailed discussion of practice theory and its relevance for thinking about group learning and group practice.     

Estimating VO2max Using a Personalized Step Test

The purpose of this study was to develop a step test with a personalized step rate and step height to predict cardiorespiratory fitness in 80 college-aged males and females using the self-reported perceived functional ability scale and data collected during the step test. Multiple linear regression analysis yielded a model (R = 0.90, SEE = 3.43 mL·kg^?1·min^?1) that included gender, body mass, perceived functional ability, step rate, and recovery heart rate. Based on the standardized β-weights, gender explained the largest proportion of variance in VO₂max values followed by perceived functional ability. The cross validation predicted residual sum of squares statistics show minimal shrinkage (R_PRESS = 0.88, SEE_PRESS = 3.57 mL·kg^?1·min^?1) in the accuracy of the regression model. This study provides a model to predict VO₂max from non-exercise data and data collected during an individualized multistage step test that is accurate, time-efficient, and easy to administer.