Randolph E. Hutchison

Assessing student knowledge transfer during group work

Successful group work requires that students transfer relevant prior knowledge to solve problems. This paper establishes a method to assess dynamic knowledge transfer in a group setting through analysis of a group project in a biomechanics class. Transcripts of student-student and student-instructor interactions were coded for evidence of target tools (students identifying relevant problem features), source tools (students activating prior knowledge), answers (stopping points), external inputs (resources and prompts from individuals or the instructor), and workbench explanations (student explanations of connections between source tools and target tools). Knowledge transfer was identified when a source tool and a target tool were coded within a phrase. The frequencies of codes were quantified to provide an overall picture of knowledge transfer for each group member throughout the project. Analysis for one group (a sophomore and junior bioengineering student, and a freshman engineering student) revealed that the junior was the largest contributor in the group, followed by the sophomore and freshman. The group mentioned source tools most frequently, followed by external inputs and target tools. The analysis provided evidence of knowledge transfer within the group through their identification of target tools and use of prior knowledge to explain their observations.

An Experimental Protocol to Model Recovery of Anaerobic Work Capacity

Models of fatigue are based on physiological parameters such as Critical Power (CP) and Anaerobic Work Capacity (AWC). CP is a theoretical threshold value that a human can generate for an indefinite amount of time and AWC represents a finite expendable amount of anaerobic energy at intensities above CP. There is an increasing interest in developing mathematical models of energy expenditure and recovery for athletic training and human performance. The objective of this research is to propose and validate a model for recovery of AWC during a post exertion recovery interval of cycling. A cycling ergometer study is proposed which involves a VO2max ramp test to determine gas exchange threshold, a 3-min all-out intensity test to determine CP and AWC, and exertion-recovery interval tests to understand recovery of AWC. The results will be used to build a human in the loop control system to optimize cycling performance.

Prediction of Ground Reaction Forces of Flexible Barbells using their Bar End Displacement: 1830 Board #91 May 31 3

433 CONCLUSION: We observed a decrease in MMG QAMP during submaximal isometric contractions performed at the same absolute torques following 3 and 6 weeks of 80% 1RM, but not 30% 1RM resistance training. These decreases are similar to the reductions in voluntary activation that we observed previously at submaximal torques following 3 and 6 weeks of high-, but not low-load training. Therefore, we suggest that MMG amplitude is sensitive to training-induced changes in motor unit activation during highversus low-load training.

Comparison of Threshold Determinations between Blood Lactate Samples and Near Infrared Spectroscopy: 1580 Board #233 June 2, 9: 00 AM - 10: 30 AM.

Utilization of blood samples from the fingertip (LA) has become a standard protocol when conducting maximal ergometer tests in a laboratory setting due to the validity and reliability of the determination of lactate thresholds (LT1, LT2). Near Infrared Spectroscopy (NIRS) has shown to be a non-invasive wearable alternative to blood samples from finger pricks. The BSXInsight is a wearable NIRS device with a proposed testing procedure that measures the oxygen levels of the blood that correlate to traditional lactate threshold values (AT, LT). To our knowledge, no scientific research has been conducted independently testing the BSXInsight device. PURPOSE:The purpose of this study was to compare the predicted threshold powers at LT1 and LT2 via LA to AT and LT via BSXInsight device, respectively. METHODS: Five volunteer male subjects (21.2±0.84 years, 72.81±8.31 kg, 175.44±6.13 cm) completed a maximal step-wise test to volitional exhaustion. Before each test, the bike was fit to each individual using the recommended knee angles measured by a goniometer. A standardized warm-up of 5 min at 40W preceded each individual’s stepwise test. The step-wise test began at 60W and increased 20W after each 3 min interval at a cadence of 70 rpm or greater. This protocol continued until the subject reached volitional exhaustion or until the cadence dropped below 60 rpm. RESULTS: A simple linear regression was used to predict the blood lactates, LT1 and LT2, based on the AT and LT of the BSXInsight. A significant regression for LT1 was found (F (1,3) = 40.30, p<0.01), with an R^2 of 0.931. The standard error of estimate is 17.722 watts based on 95% confidence intervals. A significant regression for LT2 was found (F (1,3) = 29.903, p<0.02), with an R^2 of 0.909. The standard error of estimate is 22.378 watts based on 95% confidence intervals. CONCLUSIONS: Based on the results, there is a strong positive relationship between the powers predicted by invasive blood lactate samples and the BSXInsight for lactate thresholds. This study showed that the BSXInsight device using NIRS technology may be an acceptable, non-invasive technique for determining LT1 and LT2; however, further testing must be done to examine the level of fitness of the subject for a successful prediction of lactate thresholds using the BSXInsight device.