A Deep Learning-Based Teaching Design for High School Geography Units:Taking the Example of Landforms of the Humanistic Education Edition

The traditional teaching methods of one-way cultivation of students can no longer meet the requirements of talent cultivation at this stage.The issue of how to promote students from passive acceptance to the independent cognitive understanding stage(i.e.deep learning)has become the focus of geography teaching.Therefore,under the guidance of deep learning theory,this paper takes the“landforms”knowledge unit of the Humanistic Education Edition as an example,improves the classroom teaching means through the unit teaching mode,reconstructs the“landforms”teaching unit,and explores the specific teaching of high school geography unit based on deep learning.This study provides a good example and guidelines for high school geography teaching and learning.

Test Case Generation Evaluator for the Implementation of Test Case Generation Algorithms Based on Learning to Rank

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.

Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County,Three Gorges Reservoir, China

Landslide susceptibility mapping is vital for landslide risk management and urban planning.In this study,we used three statistical models[frequency ratio,certainty factor and index of entropy(IOE)]and a machine learning model[random forest(RF)]for landslide susceptibility mapping in Wanzhou County,China.First,a landslide inventory map was prepared using earlier geotechnical investigation reports,aerial images,and field surveys.Then,the redundant factors were excluded from the initial fourteen landslide causal factors via factor correlation analysis.To determine the most effective causal factors,landslide susceptibility evaluations were performed based on four cases with different combinations of factors("cases").In the analysis,465(70%)landslide locations were randomly selected for model training,and 200(30%)landslide locations were selected for verification.The results showed that case 3 produced the best performance for the statistical models and that case 2 produced the best performance for the RF model.Finally,the receiver operating characteristic(ROC)curve was used to verify the accuracy of each model's results for its respective optimal case.The ROC curve analysis showed that the machine learning model performed better than the other three models,and among the three statistical models,the IOE model with weight coefficients was superior.

Hydraulic Circuit Design and Dynamic Learning Using Case-based Reasoning

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.

Machine learning technique for prediction of magnetocaloric effect in La(Fe,Si/Al)_(13)-based materials

Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on composition design for La（Fe,Si/Al）（13）-based materials, which are regarded as one of the most promising magnetic refrigerants in practice. Three prediction models are built by using a machine learning algorithm called gradient boosting regression tree（GBRT） to essentially find the correlation between the Curie temperature（TC）, maximum value of magnetic entropy change（（？SM）（max））,and chemical composition, all of which yield high accuracy in the prediction of TC and（？SM）（max）. The performance metric coefficient scores of determination（R^2） for the three models are 0.96, 0.87, and 0.91. These results suggest that all of the models are well-developed predictive models on the challenging issue of generalization ability for untrained data, which can not only provide us with suggestions for real experiments but also help us gain physical insights to find proper composition for further magnetic refrigeration applications.

A Teaching Case of Subject Clause Based on Implicit Learning

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-

Case and Questions Design in Case- based Learning Used in Medical-nursing English Teaching

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.

Prediction and interpretation of photocatalytic NO removal on g-C_(3)N_(4)-based catalysts using machine learning

Predictive modeling of photocatalytic NO removal is highly desirable for efficient air pollution abatement.However,great challenges remain in precisely predicting photocatalytic performance and understanding interactions of diverse features in the catalytic systems.Herein,a dataset of g-C_(3) N_(4)-based catalysts with 255 data points was collected from peer-reviewed publications and machine learning(ML)model was proposed to predict the NO removal rate.The result shows that the Gradient Boosting Decision Tree(GBDT)demonstrated the greatest prediction accuracy with R 2 of 0.999 and 0.907 on the training and test data,respectively.The SHAP value and feature importance analysis revealed that the empirical categories for NO removal rate,in the order of importance,were catalyst characteristics>reaction process>preparation conditions.Moreover,the partial dependence plots broke the ML black box to further quantify the marginal contributions of the input features(e.g.,doping ratio,flow rate,and pore volume)to the model output outcomes.This ML approach presents a pure data-driven,interpretable framework,which provides new insights into the influence of catalyst characteristics,reaction process,and preparation conditions on NO removal.

An Insight of the First Community Infected COVID-19 Patient in Beijing by Imported Case: Role of Deep Learning-Assisted CT Diagnosis

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.

A Retrieval Matching Method Based Case Learning for 3D Model

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.

Case Studies from Literature and Movies: A Best Practice for Learning

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.

A case study of changing motivation in foreign language learning

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.

An innovative approach of using online problem‑based learning and case‑based learning in teaching disaster nursing during the COVID‑19 pandemic

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.

Preparing EFL Learners for Native English-Speaking Context Through Vocabulary Learning： Two Chinese Cases in New Zealand

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.

Towards a Collaborative Learning Environment Through ICT： A Case Study

This paper expounds how the possibility of collaboration and construction of knowledge being put into practice in a group of ICT （information and communication technologies）-based teaching and learning programmes for Mother Tongue languages, collectively known as 10＇CMT. 10＇CMT, which is initiated by the ETD （Educational Technology Division） of MOE （Ministry of Education） Singapore, embodies a focus on the development of relevant pedagogy by which web-based technologies are embedded in meaningful learning activities in the classroom. Through a case study of a primary school in Singapore, this paper exemplifies how 10＇CMT has the ability to promote collective knowledge and, by doing so, essentially supporting the growth of the individual student＇s knowledge. It draws on the students＇ engagement in peer editing, peer evaluation, peer interaction, and feedback with self-reflective practices through the affordances of an array of online tools. This paper will also discuss how the 10＇CMT approach promotes the ability to respond flexibly to complex problems, to communicate effectively, to manage information, to work in teams, to use technology, and to produce new knowledge which are deemed to be crucial competencies for 21 st century.

Case Study of a Chinese Girl's English Learning Methods in the American Elementary School

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

Research of Genetic Training Algorithm for Identifying Mechanical Failure Modes within the Framework of Case-Based Reasoning

The combination of case-based reasoning （CBR） and genetic algorithm （GA） is considered in the problem of failure mode identification in aeronautical component failure analysis. Several imple- mentation issues such as matching attributes selection, similarity measure calculation, weights learning and training evaluation policies are carefully studied. The testing applications illustrate that an accuracy of 74.67 % can be achieved with 75 balanced-distributed failure cases covering 3 failure modes, and that the resulting learning weight vector can be well applied to the other 2 failure modes, achieving 73.3 % of recognition accuracy. It is also proved that its popularizing capability is good to the recognition of even more mixed failure modes.

企业E-Learning实施与活动设计个案研究

企业E-Learning在当前社会中的重要性有目共睹,具有广阔的发展前景,深入研究企业E-Learning的实施与活动个案对于企业E-Learning的发展具有重要意义。基于实地调研,本案对企业E-Learning的实施设计从平台设计、课件资源设计和学习支持设计三个维度进行分析,对企业E-Learning的活动设计与策略从推广活动、应用策略、考核策略三个方面进行分析,并以一个企业E-Learning项目实施案例进行佐证,解析其中的特色与不足,为企业E-Learning推进与改革提供借鉴。

基于E-learning平台的流行病学案例库的开发与实践

目的:将E-learning互动平台应用于流行病学案例教学中,评价其教学效果;方法:将预防医学专业本科生随机分为实验组和对照组,实验组采用传统课堂与E-learning互动平台教学相结合的混合教学模式,对照组采用传统的课堂教学,课程结束后评价其效果及学生满意度;结果:两组学生成绩差异无统计学意义,实验组学生表示平台案例库的应用更有助于掌握和运用流行病学知识;结论:E-learning互动平台可以作为流行病学案例教学有效的辅助工具。