Research on the Diagnosis of Students’ Cognitive Level Based on Deep Learning

In order to improve teaching quality and reduce students’ learning difficulty, this study introduces the concept of Teaching for the Ability (TFA) on the basis of traditional Internet teaching, uses big data to calculate students’ cognitive ability, and uses the evaluation results of students’ cognitive ability to drive the Internet learning scheme tailored for each student one by one. In the data validation analysis, it is found that the cognitive ability evaluation results made by the system directly affect the students’ final performance in the national unified graduation examination, and the education scheme for students with low cognitive ability is found in the data analysis.

Performance of Graduation Examination of Standardized Residency Training in Zhejiang Province, China

Background Standardized residency training is an important part of post-graduation medical education for medical students in China, and the graduation examination is a necessary part to test the effect of residency training and an important measure to guarantee the quality of residency training. This study analyzed the graduation examination results of standardized residency training in Zhejiang Province in 2020. Methods This study calculated the pass rates for the practical skills examination and written examination for 5555 examinees and analyzed the relationship between examinees' demographic characteristics and pass rates. Results The pass rates for practical skills examination and written examination were 91.8% and 96.5%, respectively. Gender, age, education, and type of organizations were significantly associated with the pass rates. Examinees who retook the examination were less likely to pass the practical skills examination (AOR = 0.266, 95% CI = 0.189-0.377) and the written examination (AOR = 0.043, 95% CI = 0.025-0.074) than examinees who took the examination for the first time. If an examinee did not pass the practical skills examination initially, he or she was less likely to pass the written examination (AOR = 0.172, 95% CI = 0.120-0.249) Conclusion The pass rate of graduation examination of standardized residency training was not only influenced by the individual characteristics of the examinees but also related to the level of training hospitals and lead teachers. Therefore, it is necessary to take comprehensive measures to further improve the system of standardized residency training.

Identifying and supporting students at risk of failing the National Medical Licensure Examination in Japan using a predictive pass rate

Background Students who fail to pass the National Medical Licensure Examination (NMLE) pose a huge problem from the educational standpoint of healthcare professionals. In the present study, we developed a formula of predictive pass rate (PPR)” which reliably predicts medical students who will fail the NMLE in Japan, and provides an adequate academic support for them. Methods Six consecutive cohorts of 531 medical students between 2012 and 2017, Gifu University Graduate School of Medicine, were investigated. Using 7 variables before the admission to medical school and 10 variables after admission, we developed a prediction formula to obtain the PPR for the NMLE using logistic regression analysis. In a new cohort of 106 medical students in 2018, we applied the formula for PPR to them to confirm the capability of the PPR and predicted students who will have a strong likelihood of failing the NMLE. Results Medical students who passed the NMLE had the following characteristics: younger age at admission, graduates of high schools located in the surrounding area, high scores in the graduation examination and in the comprehensive computer-based test provided by the Common Achievement Test Organization in Japan. However, total score of examination in pre-clinical medical sciences and Pre-CC OSCE score in the 4th year were not correlated with the PPR. Ninety-one out of 531 students had a strong likelihood of failing the NMLE between 2012 and 2017 and 33 of these 91 students failed NMLE. Using the PPR, we predicted 12 out of 106 students will have a strong likelihood of failing the NMLE. Actually, five of these 12 students failed NMLE. Conclusions The PPR can be used to predict medical students who have a higher probability of failing the NMLE. This prediction would enable focused support and guidance by faculty members. Prospective and longitudinal studies for larger and different cohorts would be necessary.

Identifying and Supporting Students at Risk of Failing the National Medical Licensure Examination in Japan Using a Predictive Pass Rate

Background Students who fail to pass the National Medical Licensure Examination (NMLE) pose a huge problem from the educational standpoint of healthcare professionals. In the present study, we developed a formula of predictive pass rate (PPR)” which reliably predicts medical students who will fail the NMLE in Japan, and provides an adequate academic support for them. Methods Six consecutive cohorts of 531 medical students between 2012 and 2017, Gifu University Graduate School of Medicine, were investigated. Using 7 variables before the admission to medical school and 10 variables after admission, we developed a prediction formula to obtain the PPR for the NMLE using logistic regression analysis. In a new cohort of 106 medical students in 2018, we applied the formula for PPR to them to confirm the capability of the PPR and predicted students who will have a strong likelihood of failing the NMLE. Results Medical students who passed the NMLE had the following characteristics: younger age at admission, graduates of high schools located in the surrounding area, high scores in the graduation examination and in the comprehensive computer-based test provided by the Common Achievement Test Organization in Japan. However, total score of examination in pre-clinical medical sciences and Pre-CC OSCE score in the 4th year were not correlated with the PPR. Ninety-one out of 531 students had a strong likelihood of failing the NMLE between 2012 and 2017 and 33 of these 91 students failed NMLE. Using the PPR, we predicted 12 out of 106 students will have a strong likelihood of failing the NMLE. Actually, five of these 12 students failed NMLE. Conclusions The PPR can be used to predict medical students who have a higher probability of failing the NMLE. This prediction would enable focused support and guidance by faculty members. Prospective and longitudinal studies for larger and different cohorts would be necessary.

Identifying and Supporting Students at Risk of Failing the National Medical Licensure Examination in Japan Using a Predictive Pass Rate

Background Students who fail to pass the National Medical Licensure Examination (NMLE) pose a huge problem from the educational standpoint of healthcare professionals. In the present study, we developed a formula of predictive pass rate (PPR)” which reliably predicts medical students who will fail the NMLE in Japan, and provides an adequate academic support for them. Methods Six consecutive cohorts of 531 medical students between 2012 and 2017, Gifu University Graduate School of Medicine, were investigated. Using 7 variables before the admission to medical school and 10 variables after admission, we developed a prediction formula to obtain the PPR for the NMLE using logistic regression analysis. In a new cohort of 106 medical students in 2018, we applied the formula for PPR to them to confirm the capability of the PPR and predicted students who will have a strong likelihood of failing the NMLE. Results Medical students who passed the NMLE had the following characteristics: younger age at admission, graduates of high schools located in the surrounding area, high scores in the graduation examination and in the comprehensive computer-based test provided by the Common Achievement Test Organization in Japan. However, total score of examination in pre-clinical medical sciences and Pre-CC OSCE score in the 4th year were not correlated with the PPR. Ninety-one out of 531 students had a strong likelihood of failing the NMLE between 2012 and 2017 and 33 of these 91 students failed NMLE. Using the PPR, we predicted 12 out of 106 students will have a strong likelihood of failing the NMLE. Actually, five of these 12 students failed NMLE. Conclusions The PPR can be used to predict medical students who have a higher probability of failing the NMLE. This prediction would enable focused support and guidance by faculty members. Prospective and longitudinal studies for larger and different cohorts would be necessary.

RANCANG BANGUN APLIKASI UJIAN ONLINE STUDI KASUS: SMK 1 PGRI CIKAMPEK

The development of technology in the digital era is now very fast along with the growth of industries from all sectors. The 4.0 industrial revolution has begun to enter its era, which emphasizes the pattern of the digital economy, artificial intelligence, big data, robotic, or known as the phenomenon of disruptive innovation. Education in the era of Industrial Revolution 4.0 or known as Education 4.0 is a phenomenon that responds to the need for the emergence of the industrial revolution 4.0 where humans and machines are aligned to get solutions, solve problems and of course discover new possibilities of innovation. The Era of Education 4.0 is very influential for the world of education, as it also experiences an increase in quality, speed and practicality. Online exams can be said to belong to the era of Education 4.0 because conventional exams (manual) have shifted towards computerization. Online test (Online Test) is one of the methods of evaluating learning outcomes or measuring a person's ability level using a computer. Therefore, the authors researched the web-based online exam system at SMK PGRI Cikampek. Currently the Midterm Examination (UTS) and Graduation Examination (UKK) systems that are implemented at the school are still conventional (manual). So that teachers often experience technical obstacles in processing questions and exam results, such as broken answer sheets, uneven distribution of questions, leakage of questions caused during distribution, and so on. With the online examination system the school can reduce or even minimize the problems that occur in the implementation of conventional exams (manual) because with a computerized system everything can be managed and monitored more neatly easily and centrally.

Assessing Nursing Students’ Clinical Competencies Using a Problem-Focused Objective Structured Clinical Examination

The objectives of this study were to develop, implement, and evaluate an innovative modified Objective Structured Clinical Examination (OSCE) model, and to compare students’ performance of different clinical skills as assessed by standardized patients and OSCE examiners. Data were obtained from final year undergraduate students undergoing the modified OSCE as a graduation examination. Seventy-seven students rotated through four stations (nine substations). Standardized patients scored students higher than examiners in history taking (9.14 ± 0.92 vs. 8.42 ± 0.85), response to emergency event (8.88 ± 1.12 vs. 7.62 ± 1.54), executive medical orders (8.77 ± 0.96 vs. 8.25 ± 1.43), technical operation (18.21 ± 1.26 vs. 16.91 ± 1.35), nursing evaluation (4.53 ± 0.28 vs. 4.29 ± 0.52), and health education stations (13.79 ± 1.31 vs. 11.93 ± 2.25; p < .01). In addition, the results indicated that the difference between standardized patient and examiner scores for physical examination skills was nonsignificant (8.70 ± 1.18 vs. 8.80 ± 1.27; p > .05). The modified, problem-focused, and nursing process–driven OSCE model effectively assessed nursing students’ clinical competencies, and clinical and critical thinking.