Learn-Achieve-Succeed: New technologies for enhancing student performance

Tegrity Campus 2.0 is the first student achievement system that impacts learning across the entire institution, improving retention and student satisfaction. Tegrity makes class time available all the time by automatically capturing, storing and indexing every class on campus for replay by every student. With Tegrity, students quickly recall key moments or replay entire classes online, with digital notes, on their iPods and cell phones. [See PDF for complete abstract]

Streaming Video: Student Performance and Relative Use in the Preclinical Curriculum

Purpose: To assess the relationship between student utilization of learning resources, including streaming video (SV), and their performance in the pre-clinical curriculum. [See PDF for complete abstract]

The Pre-entry Program at UTMSH: Effect on Academic Performance of First-Year Medical Students

Introduction: The Pre-Entry Program at The University of Texas Medical School at Houston is presented to assist entering students who are judged to be at risk for academic difficulty. It requires a significant commitment of time on the part of faculty, staff and students. The effectiveness of this program needs to be evaluated. [See PDF for complete abstract]

Use of instructional video to prepare parents for learning infant cardiopulmonary resuscitation.

Parents of premature infants often receive infant cardiopulmonary resuscitation (CPR) training prior to discharge from the hospital, but one study showed that 27.5% of parents could not demonstrate adequate CPR skills after completing an instructor-led class. We hypothesized that parents who viewed an instructional video on infant CPR before attending the class would perform better on a standardized skills test than parents who attended the class with no preparation. Parents randomized to the intervention (video) group viewed the video within 48 hours of the CPR class. Parents in the control group attended the class with no special preparation. All parents completed the CPR skills checklist test, usually within 7 days after class and before the infant's hospital discharge. The test rated subjects' skills in the areas of assessment, ventilation, and chest compressions; each section was rated as good, fair, or fail. In this pass/fail test, students had to be rated good or fair on all three sections to pass. All 10 subjects in the video group passed the test versus only 9 of 13 in the control group, but this difference was not significant (P = 0.08). However, 8 of 10 (80%) subjects in the video group were rated as good on all three sections, versus only 3 of 13 (18.7%) in the control group, and this was a significant difference (P = 0.012). We conclude that preparation of students using an instructional video prior to infant CPR class is associated with improvement in skills performance as measured by a standardized skills test. Video preparation is relatively inexpensive, eliminates the barrier of reading ability for preparation, and can be done at the convenience of the parent.

Mechanisms of perceptual learning of depth discrimination in random dot stereograms.

Perceptual learning is a training induced improvement in performance. Mechanisms underlying the perceptual learning of depth discrimination in dynamic random dot stereograms were examined by assessing stereothresholds as a function of decorrelation. The inflection point of the decorrelation function was defined as the level of decorrelation corresponding to 1.4 times the threshold when decorrelation is 0%. In general, stereothresholds increased with increasing decorrelation. Following training, stereothresholds and standard errors of measurement decreased systematically for all tested decorrelation values. Post training decorrelation functions were reduced by a multiplicative constant (approximately 5), exhibiting changes in stereothresholds without changes in the inflection points. Disparity energy model simulations indicate that a post-training reduction in neuronal noise can sufficiently account for the perceptual learning effects. In two subjects, learning effects were retained over a period of six months, which may have application for training stereo deficient subjects.

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^

Learning -dependent plasticity of movement representations in the primate primary motor cortex

Primary motor cortex (M1) is involved in the production of voluntary movement and contains a complete functional representation, or map, of the skeletal musculature. This functional map can be altered by pathological experiences, such as peripheral nerve injury or stroke, by pharmacological manipulation, and by behavioral experience. The process by which experience-dependent alterations of cortical function occur is termed plasticity. In this thesis, plasticity of M1 functional organization as a consequence of behavioral experience was examined in adult primates (squirrel monkeys). Maps of movement representations were derived under anesthesia using intracortical microstimulation, whereby a microelectrode was inserted into the cortex to electrically stimulate corticospinal neurons at low current levels and evoke movements of the forelimb, principally of the hand. Movement representations were examined before and at several times after training on behavioral tasks that emphasized use of the fingers. Two behavioral tasks were utilized that dissociated the repetition of motor activity from the acquisition of motor skills. One task was easy to perform, and as such promoted repetitive motor activity without learning. The other task was more difficult, requiring the acquisition of motor skills for successful performance. Kinematic analysis indicated that monkeys used a consistent set of forelimb movements during pellet extractions. Functional mapping revealed that repetitive motor activity during the easier task did not produce plastic changes in movement representations. Instead, map plasticity, in the form of selective expansions of task-related movement representations, was only produced following skill acquisition on the difficult task. Additional studies revealed that, in general, map plasticity persisted without further training for up to three months, in parallel with the retention of task-related motor skills. Also, extensive additional training on the small well task produced further improvements in performance, and further changes in movement maps. In sum, these experiments support the following three conclusions regarding the role of M1 in motor learning. First, behaviorally-driven plasticity is learning-dependent, not activity-dependent. Second, plastic changes in M1 functional representations represent a neural correlate of acquired motor skills. Third, the persistence of map plasticity suggests that M1 is part of the neural substrate for the memory of motor skills. ^