Purpose： It is used for judging the advantages and disadvantages of information technology foundation course teaching in health vocational colleges. Method： In teaching, it takes the two classes of 2012 grade nursin...Purpose： It is used for judging the advantages and disadvantages of information technology foundation course teaching in health vocational colleges. Method： In teaching, it takes the two classes of 2012 grade nursing major as the experiment object. The comparison class adopts traditonal and speaking-practice combination teaching method and the experiment class adopts task-driving teaching method. When the semester finishes, it conducts testing andd questionnaire survey, collecting the relevant data, analyzing the changes of students in the aspects of performance, learning interest and attitude, autonomous learning consciousness and ability after experiment class adopting new teaching methods. Result： The exam performance of experiment class is obviously higher than the comparison class, and the experiment class has an obvious improvement in the aspects of learning interest, autonomous learning consciousness and ability, and the difference has statistical significance. Conclusion： The task driving teaching method is suitable for the status of information foundation teaching in health vocational colleges, which improves students＇ performance significantly and is good for students＇ learning interest and enthusiasm, obtaining good classroom effect. Also, it makes students＇ autonomous learning consciousness and ability improve greatly.展开更多
The aim of this study was to predict drivers＇ drowsy states with high risk of encountering a crash and prevent drivers from continuing to drive under such drowsy states with high risk of crash. While the participants...The aim of this study was to predict drivers＇ drowsy states with high risk of encountering a crash and prevent drivers from continuing to drive under such drowsy states with high risk of crash. While the participants were required to carry out a simulated driving task, EEG （Electro encephalography） （EEG-MPF and EEG-α/β）, ECG （Electrocradiogram） （RRV3）, t racking error, an d subjective rating on drowsiness were measured. On the basis of such measurements, an attempt was made to predict the point in time with high crash risk using Bayesian estimation of posterior probability of drowsiness, tracking error, and subjective drowsiness. As a result of applying the proposed method to the data of each participant, it was verified that the proposed method could predict the point in time with high crash risk before the point in time of crash.展开更多
文摘Purpose： It is used for judging the advantages and disadvantages of information technology foundation course teaching in health vocational colleges. Method： In teaching, it takes the two classes of 2012 grade nursing major as the experiment object. The comparison class adopts traditonal and speaking-practice combination teaching method and the experiment class adopts task-driving teaching method. When the semester finishes, it conducts testing andd questionnaire survey, collecting the relevant data, analyzing the changes of students in the aspects of performance, learning interest and attitude, autonomous learning consciousness and ability after experiment class adopting new teaching methods. Result： The exam performance of experiment class is obviously higher than the comparison class, and the experiment class has an obvious improvement in the aspects of learning interest, autonomous learning consciousness and ability, and the difference has statistical significance. Conclusion： The task driving teaching method is suitable for the status of information foundation teaching in health vocational colleges, which improves students＇ performance significantly and is good for students＇ learning interest and enthusiasm, obtaining good classroom effect. Also, it makes students＇ autonomous learning consciousness and ability improve greatly.
文摘The aim of this study was to predict drivers＇ drowsy states with high risk of encountering a crash and prevent drivers from continuing to drive under such drowsy states with high risk of crash. While the participants were required to carry out a simulated driving task, EEG （Electro encephalography） （EEG-MPF and EEG-α/β）, ECG （Electrocradiogram） （RRV3）, t racking error, an d subjective rating on drowsiness were measured. On the basis of such measurements, an attempt was made to predict the point in time with high crash risk using Bayesian estimation of posterior probability of drowsiness, tracking error, and subjective drowsiness. As a result of applying the proposed method to the data of each participant, it was verified that the proposed method could predict the point in time with high crash risk before the point in time of crash.
