Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for opti...Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.展开更多
The New English curriculum criteria suggest teaching English grammar based on the students’cognitive characteristics and emotional needs,helping them discover the rules and encouraging them to master the grammar by u...The New English curriculum criteria suggest teaching English grammar based on the students’cognitive characteristics and emotional needs,helping them discover the rules and encouraging them to master the grammar by using it.But due to the limited time in a lesson,many English teachers adopt a simple approach to teach grammar,in which students are required to memorize the rules first and then practice a lot.This approach is effec-展开更多
In the era of coronavirus disease 2019(COVID-19)pandemic,imported COVID-19 cases pose great challenges to many countries.Chest CT examination is considered to be complementary to nucleic acid test for COVID-19 detecti...In the era of coronavirus disease 2019(COVID-19)pandemic,imported COVID-19 cases pose great challenges to many countries.Chest CT examination is considered to be complementary to nucleic acid test for COVID-19 detection and diagnosis.Wie report the first community infected COVID-19 patient by an imported case in Beijing,which manifested as nodular lesions on chest CT imaging at the early stage.Deep Learning(DL)-based diagnostic systems quantitatively monitored the progress of pulmonary lesions in 6 days and timely made alert for suspected pneumonia,so that prompt medical isolation was taken.The patient was confirmed as COVID-19 case after nucleic acid test,for which the community transmission was prevented timely.The roles of DL-assisted diagnosis in helping radiologists screening suspected COVID cases were discussed.展开更多
Case-based learning(CBL) is gradually replacing the traditional lecturing-based learning in nursing English teaching.In the process of CBL, selecting and compiling a good case is key to the success of CBL. In the mean...Case-based learning(CBL) is gradually replacing the traditional lecturing-based learning in nursing English teaching.In the process of CBL, selecting and compiling a good case is key to the success of CBL. In the meantime, designing questions is an important factor for successful CBL. In this article, we discuss how to select and compile cases and how to design questions in CBL used in Medical-nursing English Teaching.展开更多
The manufacturing processes of casing rings are prone to multi-type defects such as holes,cracks,and porosity,so ultrasonic testing is vital for the quality of aeroengine.Conventional ultrasonic testing requires manua...The manufacturing processes of casing rings are prone to multi-type defects such as holes,cracks,and porosity,so ultrasonic testing is vital for the quality of aeroengine.Conventional ultrasonic testing requires manual analysis,which is susceptible to human omission,inconsistent results,and time-consumption.In this paper,a method for automated detection of defects is proposed for the ultrasonic Total Focusing Method(TFM)inspection of casing rings based on deep learning.First,the original datasets of defect images are established,and the Mask R-CNN is used to increase the number of defects in a single image.Then,the YOLOX-S-improved lightweight model is proposed,and the feature extraction network is replaced by Faster Net to reduce redundant computations.The Super-Resolution Generative Adversarial Network(SRGAN)and Convolutional Block Attention Module(CBAM)are integrated to improve the identification precision.Finally,a new test dataset is created by ultrasonic TFM inspection of an aeroengine casing ring.The results show that the mean of Average Precision(m AP)of the YOLOX-S-improved model reaches 99.17%,and the corresponding speed reaches 77.6 FPS.This study indicates that the YOLOX-S-improved model performs better than conventional object detection models.And the generalization ability of the proposed model is verified by ultrasonic B-scan images.展开更多
As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework...As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain.The proposed framework comprises three core modules:legal feature extraction,semantic similarity assessment,and verdict recommendation.For legal feature extraction,a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts.Semantic similarity between cases is evaluated using a hybrid method that combines rule-based logic with an LSTM model,analyzing the feature vectors of query cases against a legal knowledge base.Verdicts are then recommended through a rule-based retrieval system,enhanced by predefined legal statutes and regulations.By merging rule-based methodologies with deep learning,this framework addresses the interpretability challenges often associated with contemporary AImodels,thereby enhancing both transparency and generalizability across diverse legal contexts.The system was rigorously tested using a legal corpus of 43,000 case laws across six categories:Criminal,Revenue,Service,Corporate,Constitutional,and Civil law,ensuring its adaptability across a wide range of judicial scenarios.Performance evaluation showed that the feature extraction module achieved an average accuracy of 91.6%with an F-Score of 95%.The semantic similarity module,tested using Manhattan,Euclidean,and Cosine distance metrics,achieved 88%accuracy and a 93%F-Score for short queries(Manhattan),89%accuracy and a 93.7%F-Score for medium-length queries(Euclidean),and 87%accuracy with a 92.5%F-Score for longer queries(Cosine).The verdict recommendation module outperformed existing methods,achieving 90%accuracy and a 93.75%F-Score.This study highlights the potential of hybrid AI frameworks to improve judicial decision-making and streamline legal processes,offering a robust,interpretable,and adaptable solution for the evolving demands of modern legal systems.展开更多
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
The similarity metric in traditional content based 3D model retrieval method mainly refers the distance metric algorithm used in 2D image retrieval. But this method will limit the matching breadth. This paper proposes...The similarity metric in traditional content based 3D model retrieval method mainly refers the distance metric algorithm used in 2D image retrieval. But this method will limit the matching breadth. This paper proposes a new retrieval matching method based on case learning to enlarge the retrieval matching scope. In this method, the shortest path in Graph theory is used to analyze the similarity how the nodes on the path between query model and matched model effect. Then, the label propagation method and k nearest-neighbor method based on case learning is studied and used to improve the retrieval efficiency based on the existing feature extraction.展开更多
Pedagogy today has a greater need to focus on relevance for students between course content and the real world of student life and work.Educators across disciplines have found a challenge to engage students in learnin...Pedagogy today has a greater need to focus on relevance for students between course content and the real world of student life and work.Educators across disciplines have found a challenge to engage students in learning activities that carry value for them.The case study is a pedagogical tool that can connect student learning to real life examples.Narratives from books and movies present characters and situations that provide opportunity for student analysis within course concepts and student application to both personal and professional experience.Students enter example experiences that they might encounter and gain insight of potential response.This qualitative study examined graduate course work with case studies in Life-span Development,Multi-cultural Counseling,and Traumatology in which students reviewed books and movies with case examples aligned with course content.The instructor provided structured analysis format in which students were required to reflect on both personal and professional application relevant to the focus of the course.Student responses indicated perception of strong value for insight and application gained from the case studies and also high perception of use of these as tools in their own future work as educators or mental health professionals.展开更多
In software testing,the quality of test cases is crucial,but manual generation is time-consuming.Various automatic test case generation methods exist,requiring careful selection based on program features.Current evalu...In software testing,the quality of test cases is crucial,but manual generation is time-consuming.Various automatic test case generation methods exist,requiring careful selection based on program features.Current evaluation methods compare a limited set of metrics,which does not support a larger number of metrics or consider the relative importance of each metric to the final assessment.To address this,we propose an evaluation tool,the Test Case Generation Evaluator(TCGE),based on the learning to rank(L2R)algorithm.Unlike previous approaches,our method comprehensively evaluates algorithms by considering multiple metrics,resulting in a more reasoned assessment.The main principle of the TCGE is the formation of feature vectors that are of concern by the tester.Through training,the feature vectors are sorted to generate a list,with the order of the methods on the list determined according to their effectiveness on the tested assembly.We implement TCGE using three L2R algorithms:Listnet,LambdaMART,and RFLambdaMART.Evaluation employs a dataset with features of classical test case generation algorithms and three metrics—Normalized Discounted Cumulative Gain(NDCG),Mean Average Precision(MAP),and Mean Reciprocal Rank(MRR).Results demonstrate the TCGE’s superior effectiveness in evaluating test case generation algorithms compared to other methods.Among the three L2R algorithms,RFLambdaMART proves the most effective,achieving an accuracy above 96.5%,surpassing LambdaMART by 2%and Listnet by 1.5%.Consequently,the TCGE framework exhibits significant application value in the evaluation of test case generation algorithms.展开更多
After perceiving a common phenomenon of English learning at college in China, this paper presents a case study of motivation. In this study, a questionnaire is conducted twice among 156 Chinese college students when t...After perceiving a common phenomenon of English learning at college in China, this paper presents a case study of motivation. In this study, a questionnaire is conducted twice among 156 Chinese college students when they are sophomores and seniors. It intends to identify some components of foreign language learning motivation and probes into the causes for the changing motivations of students throughout 4-year college education. The findings suggest that the instrumental motivation plays a crucial role in foreign language learning context.展开更多
Objective:The objective of this study is to evaluate the learning experience effect of online problem‑based learning(PBL)and case‑based learning(CBL)in teaching disaster nursing.Methods:According to the characteristic...Objective:The objective of this study is to evaluate the learning experience effect of online problem‑based learning(PBL)and case‑based learning(CBL)in teaching disaster nursing.Methods:According to the characteristics of online PBL and CBL,the revised curriculum experience questionnaire(CEQ)was used to evaluate the teaching quality.Cronbach’s coefficient and the reliability of the split‑half reliability questionnaire were calculated.The exploratory factor analysis of 26 items was carried out by principal component analysis and maximum variance rotation method.Kaiser‑Meyer‑Olkin(KMO)and Bartlett’s tests were used to test the validity of the questionnaire.The comparison between groups was performed by one‑way analysis of variance.Results:A total of 191 questionnaires were issued and 183 copies were recovered,with a recovery rate of 95.8%.The Cronbach’s alpha coefficient of CEQ is 0.929,and the Cronbach’s alpha coefficient of each dimension is between 0.713 and 0.924.After factor analysis,the KMO value was 0.817.The 26 items finally returned to 6 principal components,and all factor load values were above 0.7,indicating good factor analysis effect.This study found that students who learned disaster nursing had an ideal online learning experience,and the average value of CEQ was 3.74±0.42.In particular,male students,senior students or medical students had a higher curriculum experience score.In addition,compared with the national recruitment,the international students have higher curriculum experience score on the dimension of Appropriate Assessment Scale,indicating that the international students are more inclined to use online PBL and CBL.Conclusions:Using the revised CEQ is an innovative approach to evaluate the effect of online PBL and CBL in teaching disaster nursing,which can improve students’experience and curriculum quality.展开更多
While numerous studies in English as a second/foreign language （EFL） have examined vocabulary learning and teaching in the perspective of theories and practical tips, there is a paucity of research on the impact of ...While numerous studies in English as a second/foreign language （EFL） have examined vocabulary learning and teaching in the perspective of theories and practical tips, there is a paucity of research on the impact of high-frequency words learning on preparing new EFL residents for the life in English-speaking countries. In order to fill this gap, this study draws on the experience of two EFL learners in New Zealand （NZ）, so as to explore the effectiveness of a 16-week daily-English-focused vocabulary learning program, which might generate useful implications about the effective adaption of new EFL residents to their target countries.展开更多
The goal of this research is to explore a more successful method for Mainland Chinese students to learn English.This newmethod will be illustrated by using the experiences of my daughter while a student in an elementa...The goal of this research is to explore a more successful method for Mainland Chinese students to learn English.This newmethod will be illustrated by using the experiences of my daughter while a student in an elementary school in the United States for oneyear.From the analysis of her learning experiences,the following conclusions were drawn:1) Immerse language learning is important tolanguage input.2) Phonics is an effective tool to improve reading for Chinese English展开更多
This paper describes the design and implementation of a hydraulic circuit design system using case-based reasoning (CBR) paradigm from AI community The domain of hydraulic circuit design and case-based reasoning are ...This paper describes the design and implementation of a hydraulic circuit design system using case-based reasoning (CBR) paradigm from AI community The domain of hydraulic circuit design and case-based reasoning are briefly reviewed Then a proposed methodology in compuer-aided circuit design and dynamic leaning with the use of CBR is described Finally an application example is selected to illustrate the ussfulness of applying CBR in hydraulic circuit design with leaming.展开更多
Language teaching is demanding.The focus of language teachers nowadays has been extended to a broader area.In the TESOL context,the findings and research of learning style have raised the attention of language teacher...Language teaching is demanding.The focus of language teachers nowadays has been extended to a broader area.In the TESOL context,the findings and research of learning style have raised the attention of language teachers,who have to take the responsibility to study how to improve the teaching results according to different learners.This essay attempts to demonstrate a language-teaching activity in a typical English class and discuss the application of learning style theory in it so as to improve the practical English teaching in China.展开更多
文摘Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.
文摘The New English curriculum criteria suggest teaching English grammar based on the students’cognitive characteristics and emotional needs,helping them discover the rules and encouraging them to master the grammar by using it.But due to the limited time in a lesson,many English teachers adopt a simple approach to teach grammar,in which students are required to memorize the rules first and then practice a lot.This approach is effec-
文摘In the era of coronavirus disease 2019(COVID-19)pandemic,imported COVID-19 cases pose great challenges to many countries.Chest CT examination is considered to be complementary to nucleic acid test for COVID-19 detection and diagnosis.Wie report the first community infected COVID-19 patient by an imported case in Beijing,which manifested as nodular lesions on chest CT imaging at the early stage.Deep Learning(DL)-based diagnostic systems quantitatively monitored the progress of pulmonary lesions in 6 days and timely made alert for suspected pneumonia,so that prompt medical isolation was taken.The patient was confirmed as COVID-19 case after nucleic acid test,for which the community transmission was prevented timely.The roles of DL-assisted diagnosis in helping radiologists screening suspected COVID cases were discussed.
文摘Case-based learning(CBL) is gradually replacing the traditional lecturing-based learning in nursing English teaching.In the process of CBL, selecting and compiling a good case is key to the success of CBL. In the meantime, designing questions is an important factor for successful CBL. In this article, we discuss how to select and compile cases and how to design questions in CBL used in Medical-nursing English Teaching.
基金supported by the Postdoctoral Fellowship Program of CPSF,China(No.GZC20232015)the China Postdoctoral Science Foundation(No.2024M752499)+3 种基金the Postdoctoral Project of Hubei Province,China(No.2024HBBHCXA076)the Wuhan East Lake New Technology Development Zone Open List Project,China(No.2022KJB128)the National Natural Science Foundation of China(No.51875428)the Fundamental Research Funds for the Central Universities,China(No.104972024RSCbs0013)。
文摘The manufacturing processes of casing rings are prone to multi-type defects such as holes,cracks,and porosity,so ultrasonic testing is vital for the quality of aeroengine.Conventional ultrasonic testing requires manual analysis,which is susceptible to human omission,inconsistent results,and time-consumption.In this paper,a method for automated detection of defects is proposed for the ultrasonic Total Focusing Method(TFM)inspection of casing rings based on deep learning.First,the original datasets of defect images are established,and the Mask R-CNN is used to increase the number of defects in a single image.Then,the YOLOX-S-improved lightweight model is proposed,and the feature extraction network is replaced by Faster Net to reduce redundant computations.The Super-Resolution Generative Adversarial Network(SRGAN)and Convolutional Block Attention Module(CBAM)are integrated to improve the identification precision.Finally,a new test dataset is created by ultrasonic TFM inspection of an aeroengine casing ring.The results show that the mean of Average Precision(m AP)of the YOLOX-S-improved model reaches 99.17%,and the corresponding speed reaches 77.6 FPS.This study indicates that the YOLOX-S-improved model performs better than conventional object detection models.And the generalization ability of the proposed model is verified by ultrasonic B-scan images.
基金funded by the Deanship of Scientific Research at Jouf University under Grant number DSR-2022-RG-0101。
文摘As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain.The proposed framework comprises three core modules:legal feature extraction,semantic similarity assessment,and verdict recommendation.For legal feature extraction,a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts.Semantic similarity between cases is evaluated using a hybrid method that combines rule-based logic with an LSTM model,analyzing the feature vectors of query cases against a legal knowledge base.Verdicts are then recommended through a rule-based retrieval system,enhanced by predefined legal statutes and regulations.By merging rule-based methodologies with deep learning,this framework addresses the interpretability challenges often associated with contemporary AImodels,thereby enhancing both transparency and generalizability across diverse legal contexts.The system was rigorously tested using a legal corpus of 43,000 case laws across six categories:Criminal,Revenue,Service,Corporate,Constitutional,and Civil law,ensuring its adaptability across a wide range of judicial scenarios.Performance evaluation showed that the feature extraction module achieved an average accuracy of 91.6%with an F-Score of 95%.The semantic similarity module,tested using Manhattan,Euclidean,and Cosine distance metrics,achieved 88%accuracy and a 93%F-Score for short queries(Manhattan),89%accuracy and a 93.7%F-Score for medium-length queries(Euclidean),and 87%accuracy with a 92.5%F-Score for longer queries(Cosine).The verdict recommendation module outperformed existing methods,achieving 90%accuracy and a 93.75%F-Score.This study highlights the potential of hybrid AI frameworks to improve judicial decision-making and streamline legal processes,offering a robust,interpretable,and adaptable solution for the evolving demands of modern legal systems.
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The similarity metric in traditional content based 3D model retrieval method mainly refers the distance metric algorithm used in 2D image retrieval. But this method will limit the matching breadth. This paper proposes a new retrieval matching method based on case learning to enlarge the retrieval matching scope. In this method, the shortest path in Graph theory is used to analyze the similarity how the nodes on the path between query model and matched model effect. Then, the label propagation method and k nearest-neighbor method based on case learning is studied and used to improve the retrieval efficiency based on the existing feature extraction.
文摘Pedagogy today has a greater need to focus on relevance for students between course content and the real world of student life and work.Educators across disciplines have found a challenge to engage students in learning activities that carry value for them.The case study is a pedagogical tool that can connect student learning to real life examples.Narratives from books and movies present characters and situations that provide opportunity for student analysis within course concepts and student application to both personal and professional experience.Students enter example experiences that they might encounter and gain insight of potential response.This qualitative study examined graduate course work with case studies in Life-span Development,Multi-cultural Counseling,and Traumatology in which students reviewed books and movies with case examples aligned with course content.The instructor provided structured analysis format in which students were required to reflect on both personal and professional application relevant to the focus of the course.Student responses indicated perception of strong value for insight and application gained from the case studies and also high perception of use of these as tools in their own future work as educators or mental health professionals.
文摘In software testing,the quality of test cases is crucial,but manual generation is time-consuming.Various automatic test case generation methods exist,requiring careful selection based on program features.Current evaluation methods compare a limited set of metrics,which does not support a larger number of metrics or consider the relative importance of each metric to the final assessment.To address this,we propose an evaluation tool,the Test Case Generation Evaluator(TCGE),based on the learning to rank(L2R)algorithm.Unlike previous approaches,our method comprehensively evaluates algorithms by considering multiple metrics,resulting in a more reasoned assessment.The main principle of the TCGE is the formation of feature vectors that are of concern by the tester.Through training,the feature vectors are sorted to generate a list,with the order of the methods on the list determined according to their effectiveness on the tested assembly.We implement TCGE using three L2R algorithms:Listnet,LambdaMART,and RFLambdaMART.Evaluation employs a dataset with features of classical test case generation algorithms and three metrics—Normalized Discounted Cumulative Gain(NDCG),Mean Average Precision(MAP),and Mean Reciprocal Rank(MRR).Results demonstrate the TCGE’s superior effectiveness in evaluating test case generation algorithms compared to other methods.Among the three L2R algorithms,RFLambdaMART proves the most effective,achieving an accuracy above 96.5%,surpassing LambdaMART by 2%and Listnet by 1.5%.Consequently,the TCGE framework exhibits significant application value in the evaluation of test case generation algorithms.
文摘After perceiving a common phenomenon of English learning at college in China, this paper presents a case study of motivation. In this study, a questionnaire is conducted twice among 156 Chinese college students when they are sophomores and seniors. It intends to identify some components of foreign language learning motivation and probes into the causes for the changing motivations of students throughout 4-year college education. The findings suggest that the instrumental motivation plays a crucial role in foreign language learning context.
基金This work was supported in part by the 22nd Batch of Teaching Reform Research Projects of Jinan University(JG2020080)Teaching Quality and Teaching Reform Project of Undergraduate University of Guangdong in China(2017,2020)+2 种基金Undergraduate Training Programs for Innovation and Entrepreneurship of Jinan University in China(no.CX20157,CX20145)Traditional Chinese Medicine Bureau of Guangdong in China(no.20161065 and 20201075)National Health and Family Planning Commission of Guangdong in China(no.A2016583,A2017228,A2017140 and A2020137).
文摘Objective:The objective of this study is to evaluate the learning experience effect of online problem‑based learning(PBL)and case‑based learning(CBL)in teaching disaster nursing.Methods:According to the characteristics of online PBL and CBL,the revised curriculum experience questionnaire(CEQ)was used to evaluate the teaching quality.Cronbach’s coefficient and the reliability of the split‑half reliability questionnaire were calculated.The exploratory factor analysis of 26 items was carried out by principal component analysis and maximum variance rotation method.Kaiser‑Meyer‑Olkin(KMO)and Bartlett’s tests were used to test the validity of the questionnaire.The comparison between groups was performed by one‑way analysis of variance.Results:A total of 191 questionnaires were issued and 183 copies were recovered,with a recovery rate of 95.8%.The Cronbach’s alpha coefficient of CEQ is 0.929,and the Cronbach’s alpha coefficient of each dimension is between 0.713 and 0.924.After factor analysis,the KMO value was 0.817.The 26 items finally returned to 6 principal components,and all factor load values were above 0.7,indicating good factor analysis effect.This study found that students who learned disaster nursing had an ideal online learning experience,and the average value of CEQ was 3.74±0.42.In particular,male students,senior students or medical students had a higher curriculum experience score.In addition,compared with the national recruitment,the international students have higher curriculum experience score on the dimension of Appropriate Assessment Scale,indicating that the international students are more inclined to use online PBL and CBL.Conclusions:Using the revised CEQ is an innovative approach to evaluate the effect of online PBL and CBL in teaching disaster nursing,which can improve students’experience and curriculum quality.
文摘While numerous studies in English as a second/foreign language （EFL） have examined vocabulary learning and teaching in the perspective of theories and practical tips, there is a paucity of research on the impact of high-frequency words learning on preparing new EFL residents for the life in English-speaking countries. In order to fill this gap, this study draws on the experience of two EFL learners in New Zealand （NZ）, so as to explore the effectiveness of a 16-week daily-English-focused vocabulary learning program, which might generate useful implications about the effective adaption of new EFL residents to their target countries.
文摘The goal of this research is to explore a more successful method for Mainland Chinese students to learn English.This newmethod will be illustrated by using the experiences of my daughter while a student in an elementary school in the United States for oneyear.From the analysis of her learning experiences,the following conclusions were drawn:1) Immerse language learning is important tolanguage input.2) Phonics is an effective tool to improve reading for Chinese English
文摘This paper describes the design and implementation of a hydraulic circuit design system using case-based reasoning (CBR) paradigm from AI community The domain of hydraulic circuit design and case-based reasoning are briefly reviewed Then a proposed methodology in compuer-aided circuit design and dynamic leaning with the use of CBR is described Finally an application example is selected to illustrate the ussfulness of applying CBR in hydraulic circuit design with leaming.
文摘Language teaching is demanding.The focus of language teachers nowadays has been extended to a broader area.In the TESOL context,the findings and research of learning style have raised the attention of language teachers,who have to take the responsibility to study how to improve the teaching results according to different learners.This essay attempts to demonstrate a language-teaching activity in a typical English class and discuss the application of learning style theory in it so as to improve the practical English teaching in China.
