Near-peer teaching in an anatomy course with a low faculty-to-student ratio     

Durán CE  Bahena EN  Rodríguez Mde L  Baca GJ  Uresti AS  Elizondo-Omaña RE  López SG 《Anatomical sciences education》2012,5(3):171-176

Near-peer teaching is an educational format which utilizes tutors who are more advanced in a curriculum's content to supervise students' activities and to act as instructors in laboratory settings. This format is often used in anatomy laboratory courses. The goal of the present study is to describe the design and implementation of near-peer teaching in an anatomy course and to evaluate students' perceptions of the program. A total of 700 students were registered for this anatomy course which employed near-peer instructors. Of enrolled students, 558 (79.7%) agreed to participate in this study. In general, the practical section (e.g., the clinical hour, image-based anatomy session, and gross anatomy laboratory) of the course was viewed more favorably compared to the theory section (54.8%, n = 306), with dissection and prosection in the laboratory rated as the most valued experiences (34.9%, n = 195). Near-peer teaching is a viable option that satisfies the demands of modern curricula using small groups. This format stimulates learning within courses that have large numbers of students and low faculty-to-student ratios.  相似文献   

Predicting shifts in generalization gradients with perceptrons     

Wisniewski MG  Radell ML  Guillette LM  Sturdy CB  Mercado E 《Learning & behavior》2012,40(2):128-144

Perceptron models have been used extensively to model perceptual learning and the effects of discrimination training on generalization, as well as to explore natural classification mechanisms. Here, we assess the ability of existing models to account for the time course of generalization shifts that occur when individuals learn to distinguish sounds. A set of simulations demonstrates that commonly used single-layer and multilayer perceptron networks do not predict transitory shifts in generalization over the course of training but that such dynamics can be accounted for when the output functions of these networks are modified to mimic the properties of cortical tuning curves. The simulations further suggest that prudent selection of stimuli and training criteria can allow for more precise predictions of learning-related shifts in generalization gradients in behavioral experiments. In particular, the simulations predict that individuals will show maximal peak shift after different numbers of trials, that easier contrasts will lead to slower development of shifted peaks, and that whether generalization shifts persist or dissipate will depend on which stimulus dimensions individuals use to distinguish stimuli and how those dimensions are neurally encoded.  相似文献