ACTIVE AND PASSIVE LEARNING CONNECTIONS TO SLEEP MANAGEMENT

摘要

Strong evidence exists that active is more effective than passive learning. In fact, passive learning is more sensitive to sleep debt. Efficiencies for passive learning and passive activities, such as driving, are reduced by more than 50 percent with as little as 18 hours of sleep debt. This relationship obviously affects highway safety. Further, the relationship also affects academic success. A sleep model, SLEEP (Sleep Loss Effects on Everyday Performance) Model developed in the College of Engineering at Texas Tech University, is used to predict the growth or decline in sleep debt and to predict resulting performance. It predicts active and passive performance efficiencies, time to fall asleep, and amount of sleep needed as a function of sleep, alcohol, and caffeine inputs. A steady-state form of the sleep model is included in GREG (Grade Requirements Evaluation Game). GREG predicts college GPA (grade point average) as a function of several academic management variables including sleep and caffeine. Results from both models are presented.