Objective To explore the application and the effect of the case based learning(CBL)method in clinical probation teaching of the integrated curriculum of hematology among eight-year-program medical students.Methods The...Objective To explore the application and the effect of the case based learning(CBL)method in clinical probation teaching of the integrated curriculum of hematology among eight-year-program medical students.Methods The CBL method was applied to the experimental group,and the traditional approach for the control group.After the lecture,a questionnaire survey was conducted to evaluate the teaching effect in the two groups.Results The CBL method efficiently increased the students’interest in learning and autonomous learning ability,enhanced their ability to solve clinical problems with basic theoretic knowledge and cultivated their clinical thinking ability.Conclusion The CBL method can improve the quality of clinical probation teaching of the integrated curriculum of hematology among eight-year-program medical students.展开更多
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
Based on detailed study on several kinds of fuzzy neural networks, we propose a novel compensationbased recurrent fuzzy neural network （CRFNN） by adding recurrent element and compensatory element to the conventional...Based on detailed study on several kinds of fuzzy neural networks, we propose a novel compensationbased recurrent fuzzy neural network （CRFNN） by adding recurrent element and compensatory element to the conventional fuzzy neural network. Then, we propose a sequential learning method for the structure identification of the CRFNN in order to confirm the fuzzy rules and their correlative parameters effectively. Furthermore, we improve the BP algorithm based on the characteristics of the proposed CRFNN to train the network. By modeling the typical nonlinear systems, we draw the conclusion that the proposed CRFNN has excellent dynamic response and strong learning ability.展开更多
目的探讨学生主导的角色扮演联合以问题为基础的教学法(problem based learning,PBL)/案例教学法(case based learning,CBL)在儿科临床教学中的效果。方法选取2021年2—6月重庆医科大学儿科学院本科见习生中的105名学生作为研究对象,按...目的探讨学生主导的角色扮演联合以问题为基础的教学法(problem based learning,PBL)/案例教学法(case based learning,CBL)在儿科临床教学中的效果。方法选取2021年2—6月重庆医科大学儿科学院本科见习生中的105名学生作为研究对象,按照教学方法的不同将2021年2—3月的44名本科见习生设为非演员组,将2021年4—6月的61名本科见习生设为演员组。非演员组采用PBL联合CBL教学法,演员组在非演员组的基础上给予学生主导的角色扮演教学法。比较2组的准备指标、教学效果、教学满意度。结果演员组的表演前花费时间[(7.13±3.58)分]短于非演员组[(8.23±5.38)分]、文献阅读量[(2.66±2.54)分]少于非演员组[(4.05±4.79)分],差异有统计学意义(P<0.05)。演员组的提高临床思维能力[(2.36±0.23)分]、增强团队协作[(2.33±0.32)分]、提高组织能力[(2.35±0.24)分]、提高学生之间亲密度[(2.47±0.14)分]和提高与老师的亲密度[(2.33±0.26)分]得分均高于非演员组[(2.01±0.17)分、(2.01±0.11)分、(2.03±0.14)分、(2.04±0.09)分、(1.94±0.11)分],差异有统计学意义(P<0.05)。演员组的教学满意度评分为(75.0±15.0)分,高于非演员组的(61.0±14.0)分,差异有统计学意义(P<0.05)。结论学生主导的角色扮演教学法在儿科实践教学中的应用效果显著,能够缩短表演前花费时间,减少文献阅读量,提升教学效果,提高教学满意度。展开更多
文摘Objective To explore the application and the effect of the case based learning(CBL)method in clinical probation teaching of the integrated curriculum of hematology among eight-year-program medical students.Methods The CBL method was applied to the experimental group,and the traditional approach for the control group.After the lecture,a questionnaire survey was conducted to evaluate the teaching effect in the two groups.Results The CBL method efficiently increased the students’interest in learning and autonomous learning ability,enhanced their ability to solve clinical problems with basic theoretic knowledge and cultivated their clinical thinking ability.Conclusion The CBL method can improve the quality of clinical probation teaching of the integrated curriculum of hematology among eight-year-program medical students.
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.
基金Supported by the National High-Tech Research and Development Program of China (Grant No. 2006AA05A107)Special Fund of JiangsuProvince for Technology Transfer (Grant No. BA2007008)
文摘Based on detailed study on several kinds of fuzzy neural networks, we propose a novel compensationbased recurrent fuzzy neural network （CRFNN） by adding recurrent element and compensatory element to the conventional fuzzy neural network. Then, we propose a sequential learning method for the structure identification of the CRFNN in order to confirm the fuzzy rules and their correlative parameters effectively. Furthermore, we improve the BP algorithm based on the characteristics of the proposed CRFNN to train the network. By modeling the typical nonlinear systems, we draw the conclusion that the proposed CRFNN has excellent dynamic response and strong learning ability.
文摘目的探讨学生主导的角色扮演联合以问题为基础的教学法(problem based learning,PBL)/案例教学法(case based learning,CBL)在儿科临床教学中的效果。方法选取2021年2—6月重庆医科大学儿科学院本科见习生中的105名学生作为研究对象,按照教学方法的不同将2021年2—3月的44名本科见习生设为非演员组,将2021年4—6月的61名本科见习生设为演员组。非演员组采用PBL联合CBL教学法,演员组在非演员组的基础上给予学生主导的角色扮演教学法。比较2组的准备指标、教学效果、教学满意度。结果演员组的表演前花费时间[(7.13±3.58)分]短于非演员组[(8.23±5.38)分]、文献阅读量[(2.66±2.54)分]少于非演员组[(4.05±4.79)分],差异有统计学意义(P<0.05)。演员组的提高临床思维能力[(2.36±0.23)分]、增强团队协作[(2.33±0.32)分]、提高组织能力[(2.35±0.24)分]、提高学生之间亲密度[(2.47±0.14)分]和提高与老师的亲密度[(2.33±0.26)分]得分均高于非演员组[(2.01±0.17)分、(2.01±0.11)分、(2.03±0.14)分、(2.04±0.09)分、(1.94±0.11)分],差异有统计学意义(P<0.05)。演员组的教学满意度评分为(75.0±15.0)分,高于非演员组的(61.0±14.0)分,差异有统计学意义(P<0.05)。结论学生主导的角色扮演教学法在儿科实践教学中的应用效果显著,能够缩短表演前花费时间,减少文献阅读量,提升教学效果,提高教学满意度。
