Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese M...Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese Medicine. Methods: The study used the experimental control method. The study lasted from September to November 2022. The subjects of this study were 49 students of standardized training for resident doctors of traditional Chinese Medicine from grades 2020, 2021 and 2022 of Dazhou integrated TCM & Western Medicine Hospital. They were randomly divided into experiment group (25) and control group (24). The experiment group adopted flipped classroom combined with problem-based learning teaching method, and the control group adopted traditional teaching method. The teaching content was 4 basic clinical skill projects, including four diagnoses of traditional Chinese Medicine, cardiopulmonary resuscitation, dressing change procedure, acupuncture and massage. The evaluation method was carried out by comparing the students’ performance and a self-designed questionnaire was used to investigate the students’ evaluation of the teaching method. Results: The test scores of total scores in the experimental group (90.12 ± 5.89) were all higher than those in the control group (81.47 ± 7.96) (t = 4.53, P P Conclusions: The teaching process of the flipped classroom combined with problem-based learning teaching method is conducive to improving the efficiency of classroom teaching, cultivating students’ self-learning ability, and enhancing students’ willingness to learn.展开更多
In technical college English listening class,task-based teaching and learning method can not only create harmonious environment for students' learning,but also motivate students' enthusiasm in listening class,...In technical college English listening class,task-based teaching and learning method can not only create harmonious environment for students' learning,but also motivate students' enthusiasm in listening class,thus students can benefit a great deal in listening class and the listening can be carried out successfully.展开更多
This ten-week quasi-experimental study was undertaken to explore the effectiveness of strategies-based vocabulary instruction on English vocabulary learning of postgraduate learners.By the questionnaires and vocabular...This ten-week quasi-experimental study was undertaken to explore the effectiveness of strategies-based vocabulary instruction on English vocabulary learning of postgraduate learners.By the questionnaires and vocabulary tests administered before and after the instruction,the experimental group and the control group were compared to find out whether reading comprehension plus SBI method was more effective than reading only method in postgraduates' English vocabulary learning.展开更多
Background/Need for innovation: Undergraduate students in Otolaryngology are on the lookout for easy modes of learning which can help them understand concepts better as well as score more in examinations. A need was h...Background/Need for innovation: Undergraduate students in Otolaryngology are on the lookout for easy modes of learning which can help them understand concepts better as well as score more in examinations. A need was hence felt to introduce a new learning resource to supplement traditional teaching-learning methods. Methods: Digital, case based self–study modules were prepared using all open source technology and validated by experts in the specialty. The modules were uploaded on a website specifically created for the purpose. They were pilot tested on twenty consenting third year undergraduate (MBBS) students using a crossover design. Post test comprising of multiple choice questions was administered to the students after a period of two weeks. Feedback was obtained from faculty and students. Results: Test scores were found to be significantly higher amongst students who used the learning modules as a supplement to regular bedside teaching (p < 0.001;Wilcoxon signed rank test). Majority of students agreed that the modules helped them gain confidence during internal assessment examinations and would help revision. Conclusions: Online, case based, self-study modules helped students to perform better when used as a supplement to traditional teaching methods. Students agreed that it enabled easy understanding of subject and helped them gain confidence.展开更多
Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.Wit...Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.With the increase of the nodes in the hidden layers,the computation cost is greatly increased.In this paper,we propose a novel algorithm,named constrained voting extreme learning machine(CV-ELM).Compared with the traditional ELM,the CV-ELM determines the input weight and bias based on the differences of between-class samples.At the same time,to improve the accuracy of the proposed method,the voting selection is introduced.The proposed method is evaluated on public benchmark datasets.The experimental results show that the proposed algorithm is superior to the original ELM algorithm.Further,we apply the CV-ELM to the classification of superheat degree(SD)state in the aluminum electrolysis industry,and the recognition accuracy rate reaches87.4%,and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods.展开更多
AIM To develop a framework to incorporate background domain knowledge into classification rule learning for knowledge discovery in biomedicine.METHODS Bayesian rule learning(BRL) is a rule-based classifier that uses a...AIM To develop a framework to incorporate background domain knowledge into classification rule learning for knowledge discovery in biomedicine.METHODS Bayesian rule learning(BRL) is a rule-based classifier that uses a greedy best-first search over a space of Bayesian belief-networks(BN) to find the optimal BN to explain the input dataset, and then infers classification rules from this BN. BRL uses a Bayesian score to evaluate the quality of BNs. In this paper, we extended the Bayesian score to include informative structure priors, which encodes our prior domain knowledge about the dataset. We call this extension of BRL as BRL_p. The structure prior has a λ hyperparameter that allows the user to tune the degree of incorporation of the prior knowledge in the model learning process. We studied the effect of λ on model learning using a simulated dataset and a real-world lung cancer prognostic biomarker dataset, by measuring the degree of incorporation of our specified prior knowledge. We also monitored its effect on the model predictive performance. Finally, we compared BRL_p to other stateof-the-art classifiers commonly used in biomedicine.RESULTS We evaluated the degree of incorporation of prior knowledge into BRL_p, with simulated data by measuring the Graph Edit Distance between the true datagenerating model and the model learned by BRL_p. We specified the true model using informative structurepriors. We observed that by increasing the value of λ we were able to increase the influence of the specified structure priors on model learning. A large value of λ of BRL_p caused it to return the true model. This also led to a gain in predictive performance measured by area under the receiver operator characteristic curve(AUC). We then obtained a publicly available real-world lung cancer prognostic biomarker dataset and specified a known biomarker from literature [the epidermal growth factor receptor(EGFR) gene]. We again observed that larger values of λ led to an increased incorporation of EGFR into the final BRL_p model. This relevant background knowledge also led to a gain in AUC.CONCLUSION BRL_p enables tunable structure priors to be incorporated during Bayesian classification rule learning that integrates data and knowledge as demonstrated using lung cancer biomarker data.展开更多
基于问题导向式的临床教学模式(problem based learning,PBL)是指通过问题案例或场景使学生确定自己的学习目标,且经由学生自我独立式学习和小组讨论以完善知识。经过60余年的教学实践,在世界许多国家的临床医学教育中都取得了良好的效...基于问题导向式的临床教学模式(problem based learning,PBL)是指通过问题案例或场景使学生确定自己的学习目标,且经由学生自我独立式学习和小组讨论以完善知识。经过60余年的教学实践,在世界许多国家的临床医学教育中都取得了良好的效果,PBL引入我国也有近30年的历史,其在我国临床医学教育的应用中仍然处于探索和改革的阶段。文章就PBL在国内中西医结合临床各科室中的教学现状进行总结,发现其对提高学生积极性,改善学习效果,提高学生的教学满意度等方面效果较好,同时探讨其目前存在的问题,并针对现有的问题提出应对策略,如“靶向PBL教学”模式,以期为未来的医学临床教育提供思路。展开更多
文摘Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese Medicine. Methods: The study used the experimental control method. The study lasted from September to November 2022. The subjects of this study were 49 students of standardized training for resident doctors of traditional Chinese Medicine from grades 2020, 2021 and 2022 of Dazhou integrated TCM & Western Medicine Hospital. They were randomly divided into experiment group (25) and control group (24). The experiment group adopted flipped classroom combined with problem-based learning teaching method, and the control group adopted traditional teaching method. The teaching content was 4 basic clinical skill projects, including four diagnoses of traditional Chinese Medicine, cardiopulmonary resuscitation, dressing change procedure, acupuncture and massage. The evaluation method was carried out by comparing the students’ performance and a self-designed questionnaire was used to investigate the students’ evaluation of the teaching method. Results: The test scores of total scores in the experimental group (90.12 ± 5.89) were all higher than those in the control group (81.47 ± 7.96) (t = 4.53, P P Conclusions: The teaching process of the flipped classroom combined with problem-based learning teaching method is conducive to improving the efficiency of classroom teaching, cultivating students’ self-learning ability, and enhancing students’ willingness to learn.
文摘In technical college English listening class,task-based teaching and learning method can not only create harmonious environment for students' learning,but also motivate students' enthusiasm in listening class,thus students can benefit a great deal in listening class and the listening can be carried out successfully.
文摘This ten-week quasi-experimental study was undertaken to explore the effectiveness of strategies-based vocabulary instruction on English vocabulary learning of postgraduate learners.By the questionnaires and vocabulary tests administered before and after the instruction,the experimental group and the control group were compared to find out whether reading comprehension plus SBI method was more effective than reading only method in postgraduates' English vocabulary learning.
文摘Background/Need for innovation: Undergraduate students in Otolaryngology are on the lookout for easy modes of learning which can help them understand concepts better as well as score more in examinations. A need was hence felt to introduce a new learning resource to supplement traditional teaching-learning methods. Methods: Digital, case based self–study modules were prepared using all open source technology and validated by experts in the specialty. The modules were uploaded on a website specifically created for the purpose. They were pilot tested on twenty consenting third year undergraduate (MBBS) students using a crossover design. Post test comprising of multiple choice questions was administered to the students after a period of two weeks. Feedback was obtained from faculty and students. Results: Test scores were found to be significantly higher amongst students who used the learning modules as a supplement to regular bedside teaching (p < 0.001;Wilcoxon signed rank test). Majority of students agreed that the modules helped them gain confidence during internal assessment examinations and would help revision. Conclusions: Online, case based, self-study modules helped students to perform better when used as a supplement to traditional teaching methods. Students agreed that it enabled easy understanding of subject and helped them gain confidence.
基金supported by the National Natural Science Foundation of China(6177340561751312)the Major Scientific and Technological Innovation Projects of Shandong Province(2019JZZY020123)。
文摘Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.With the increase of the nodes in the hidden layers,the computation cost is greatly increased.In this paper,we propose a novel algorithm,named constrained voting extreme learning machine(CV-ELM).Compared with the traditional ELM,the CV-ELM determines the input weight and bias based on the differences of between-class samples.At the same time,to improve the accuracy of the proposed method,the voting selection is introduced.The proposed method is evaluated on public benchmark datasets.The experimental results show that the proposed algorithm is superior to the original ELM algorithm.Further,we apply the CV-ELM to the classification of superheat degree(SD)state in the aluminum electrolysis industry,and the recognition accuracy rate reaches87.4%,and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods.
基金Supported by National Institute of General Medical Sciences of the National Institutes of Health,No.R01GM100387
文摘AIM To develop a framework to incorporate background domain knowledge into classification rule learning for knowledge discovery in biomedicine.METHODS Bayesian rule learning(BRL) is a rule-based classifier that uses a greedy best-first search over a space of Bayesian belief-networks(BN) to find the optimal BN to explain the input dataset, and then infers classification rules from this BN. BRL uses a Bayesian score to evaluate the quality of BNs. In this paper, we extended the Bayesian score to include informative structure priors, which encodes our prior domain knowledge about the dataset. We call this extension of BRL as BRL_p. The structure prior has a λ hyperparameter that allows the user to tune the degree of incorporation of the prior knowledge in the model learning process. We studied the effect of λ on model learning using a simulated dataset and a real-world lung cancer prognostic biomarker dataset, by measuring the degree of incorporation of our specified prior knowledge. We also monitored its effect on the model predictive performance. Finally, we compared BRL_p to other stateof-the-art classifiers commonly used in biomedicine.RESULTS We evaluated the degree of incorporation of prior knowledge into BRL_p, with simulated data by measuring the Graph Edit Distance between the true datagenerating model and the model learned by BRL_p. We specified the true model using informative structurepriors. We observed that by increasing the value of λ we were able to increase the influence of the specified structure priors on model learning. A large value of λ of BRL_p caused it to return the true model. This also led to a gain in predictive performance measured by area under the receiver operator characteristic curve(AUC). We then obtained a publicly available real-world lung cancer prognostic biomarker dataset and specified a known biomarker from literature [the epidermal growth factor receptor(EGFR) gene]. We again observed that larger values of λ led to an increased incorporation of EGFR into the final BRL_p model. This relevant background knowledge also led to a gain in AUC.CONCLUSION BRL_p enables tunable structure priors to be incorporated during Bayesian classification rule learning that integrates data and knowledge as demonstrated using lung cancer biomarker data.
文摘基于问题导向式的临床教学模式(problem based learning,PBL)是指通过问题案例或场景使学生确定自己的学习目标,且经由学生自我独立式学习和小组讨论以完善知识。经过60余年的教学实践,在世界许多国家的临床医学教育中都取得了良好的效果,PBL引入我国也有近30年的历史,其在我国临床医学教育的应用中仍然处于探索和改革的阶段。文章就PBL在国内中西医结合临床各科室中的教学现状进行总结,发现其对提高学生积极性,改善学习效果,提高学生的教学满意度等方面效果较好,同时探讨其目前存在的问题,并针对现有的问题提出应对策略,如“靶向PBL教学”模式,以期为未来的医学临床教育提供思路。
