Application of Cooperative Learning and Flipped Classrooms in University Basketball Courses in China

This study investigates the application of the teaching model combining cooperative learning and flipped classrooms in university basketball courses in China.By analyzing the advantages and disadvantages of the traditional basketball teaching model and students’satisfaction with the course,the necessity of implementing cooperative learning and flipped classrooms is proposed.The study planned in detail the implementation strategies before class,in the classroom,and after class,and compared them with the control group through an experimental design.The experimental results showed that the new teaching mode demonstrated significant advantages in terms of learning outcomes,student satisfaction,and teacher evaluation.This study provides a valuable reference for the future reform of the physical education curriculum.

Statistical Analysis of Students’Learning Effectiveness in Online Courses Offered by British Partners in China-UK Joint Education Program

In the context of internationalization,China-UK Joint Education Programs are receiving increasing attention from universities.Based on the difficulties faced in China-UK Joint Education Program,this paper adopts a questionnaire survey method to study the learning effectiveness of students majoring in digital media technology in the China-UK Joint Education Program at Guangxi University of Finance and Economics,focusing on four aspects:learning materials,learning content,teacher conditions,and student learning outcomes.The research analysis in this paper not only provides strong support for the construction of China-UK Joint Education Program but also offers references for other China-UK Joint Education Programs.

Squeezing More Past Knowledge for Online Class-Incremental Continual Learning

Continual learning(CL)studies the problem of learning to accumulate knowledge over time from a stream of data.A crucial challenge is that neural networks suffer from performance degradation on previously seen data,known as catastrophic forgetting,due to allowing parameter sharing.In this work,we consider a more practical online class-incremental CL setting,where the model learns new samples in an online manner and may continuously experience new classes.Moreover,prior knowledge is unavailable during training and evaluation.Existing works usually explore sample usages from a single dimension,which ignores a lot of valuable supervisory information.To better tackle the setting,we propose a novel replay-based CL method,which leverages multi-level representations produced by the intermediate process of training samples for replay and strengthens supervision to consolidate previous knowledge.Specifically,besides the previous raw samples,we store the corresponding logits and features in the memory.Furthermore,to imitate the prediction of the past model,we construct extra constraints by leveraging multi-level information stored in the memory.With the same number of samples for replay,our method can use more past knowledge to prevent interference.We conduct extensive evaluations on several popular CL datasets,and experiments show that our method consistently outperforms state-of-the-art methods with various sizes of episodic memory.We further provide a detailed analysis of these results and demonstrate that our method is more viable in practical scenarios.

CLIL理论下混合式英语专业课程思政融合教学实例

随着全球化趋势的加速发展,英语作为一门国际性交流语言,受到越来越多的重视。混合式教学作为一种融合传统教学和现代信息技术的新兴教学模式,在提高学生英语能力的同时,也为传递思想价值观提供了便利。因此,在英语专业课程中融合思政教育,已逐渐成为一种教学趋势和应对全球化挑战的需要。该研究旨在通过CLIL(内容与语言整合性学习)框架下的混合式英语专业课程思政融合教学实践,深入探讨如何促进思政理念的传递和落实,提升课程的教学效果和学生的综合素质,为英语专业课程思政融合教学提供了新的思路和实践参考,也为混合式教学和CLIL教学带来一些启示。

An Ensemble Learning Model for Early Dropout Prediction of MOOC Courses

Massive open online courses(MOOCs)have become a way of online learning across the world in the past few years.However,the extremely high dropout rate has brought many challenges to the development of online learning.Most of the current methods have low accuracy and poor generalization ability when dealing with high-dimensional dropout features.They focus on the analysis of the learning score and check result of online course,but neglect the phased student behaviors.Besides,the status of student participation at a given moment is necessarily impacted by the prior status of learning.To address these issues,this paper has proposed an ensemble learning model for early dropout prediction(ELM-EDP)that integrates attention-based document representation as a vector(A-Doc2vec),feature learning of course difficulty,and weighted soft voting ensemble with heterogeneous classifiers(WSV-HC).First,A-Doc2vec is proposed to learn sequence features of student behaviors of watching lecture videos and completing course assignments.It also captures the relationship between courses and videos.Then,a feature learning method is proposed to reduce the interference caused by the differences of course difficulty on the dropout prediction.Finally,WSV-HC is proposed to highlight the benefits of integration strategies of boosting and bagging.Experiments on the MOOCCube2020 dataset show that the high accuracy of our ELM-EDP has better results on Accuracy,Precision,Recall,and F1.

Research on College Studentst'Autonomous EFL Learning Affer Course Exemption

By analyzing the English learning logs of 12 students in a provincial university in south-west China after they had been exempted from taking college English courses,this study investigated college students’autonomous EFL(English as a foreign language)learning after course exemption,including the use of mediational means in EFL learning,EFL learning hours,and other factors affecting EFL learning,in the hope of giving new perspectives on college ELF curriculum design,teaching,and education management.

无负采样的正样本增强图对比学习推荐方法PAGCL

对比学习(CL)因能够提取数据本身包含的监督信号而被广泛应用于推荐任务。最近的研究表明,CL在推荐方面的成功依赖于对比损失——互信息噪声对比估计(InfoNCE)损失带来的节点分布的均匀性。此外,另一项研究证明贝叶斯个性化排序(BPR)损失的正项与负项分别带来的对齐性和均匀性有助于提高推荐性能。由于在CL框架中对比损失能够带来比BPR负项更强的均匀性,BPR负项存在的必要性值得商榷。实验分析表明在对比框架中BPR的负项是不必要的,并基于这一观察提出了无需负采样的联合优化损失,可应用于经典的CL方法并达到相同或更高的性能。此外,与专注于提高均匀性的研究不同,为进一步加强对齐性,提出一种新颖的正样本增强的图对比学习方法(PAGCL),该方法使用随机正样本在节点表示层面进行扰动。在多个基准数据集上的实验结果表明,PAGCL在召回率及归一化折损累积增益(NDCG)这两个常用指标上均优于SOTA方法自监督图学习(SGL)、简单图对比学习(SimGCL)等,且相较于基模型轻量化图卷积(LightGCN)的NDCG@20提升最大可达17.6%。

Research on the Influencing Factors of College Students’Learning Experience in Livestreaming Courses

The learning experience of online courses has always been a hot topic.As livestreaming courses are online courses with real-time interaction between instructors and students,its learning experience directly affects the learning behavior and effect.The evaluation indicators related to online course learning experience were combined with the attitudes of scholars,enterprise practitioners,and learners in livestreaming courses.When the questionnaire was designed,the exploratory and confirmatory factors were analyzed,the Questionnaire on Learning Experience of College Students in Livestreaming Courses was developed to evaluate the learning experience of college students attending livestreaming courses.Last but not the least,based on the survey data,important factors affecting college students’learning experience in livestreaming courses,including course content,learning environment,course interaction,and learning incentives were discussed and analyzed;strategies to optimize the learning experience in livestreaming courses were proposed.

An Analysis of the Research Trends in Blended Learning Methods in Teaching College English Reading Courses

Curtis J.Bonk defined blended learning as a combination of face-to-face and computer-assisted online learning instruction since it was gradually formed after the emergence of the Internet[1,2].Breen pointed out that blended learning is a learning method that combines online and offline learning activities and resources[3].The blended learning approach is popular in school and e-learning.Moreover,it is one of the important trends in promoting higher education reform in the coming years.Therefore,it has attracted the attention of international researchers[4].In this paper,42 articles in Scopus(established by international institutions)and 30 articles in CNKI(found by Chinese mainland institutions)on the query of“college English reading blended learning”in the field of education were analyzed.Furthermore,CiteSpace software was used to analyze the research hotspots and trends with the keyword clusters and citations that occurred in the last two decades(1999 to 2022)to foresee future research prospects.

Problem-Based Learning(PBL)Teaching Design in Enhancing College Students’Innovation Ability:Taking the Traditional Dress Design Course as An Example

This paper combines the cultivation of innovation ability with the content of problem-based learning(PBL),analyzes the current situation of the traditional dress design course,discusses the problems existing in the cultivation of innovation ability of college and university traditional dress design,and searches for the strategies to improve students’innovation ability based on PBL.This paper argues that PBL can provide assistance to the teaching design of traditional dress design courses,which is conducive to improving students’innovation ability in traditional dress design and realizing the desired teaching effect.

What Is the Impact of Major Diversion Attitude on Undergraduates' Learning Gains?--The Mediating Role of Course Perception

Major diversion is an important part of the large-category student enrollment and training model.The degree to which undergraduates recognize the logic of the major diversion system,their satisfaction with the diverted major,and their major identity after diversion all influence their subsequent learning process and outcomes.The questionnaire survey of undergraduates in this study discovered that major diversion attitude has a significant positive effect on undergraduates'learning gains;the mediating effect test discovered that course perception plays a partially mediating role between major diversion attitude and learning gains.Therefore,under the large-category student enrollment and training model,it is necessary to improve the major diversion system in terms of formulation,major selection guidance,and major identity promotion.Furthermore,strengthening the logical connection and content coupling of different types of courses,dealing with the proportion,priority,and sequence of courses,optimizing the allocation of course resources,and reasonably planning and setting courses all play an important role in improving undergraduate learning gains.

Research on the Reform of the Course“Reading of Concrete Structure Plan and Construction Drawings”Under the Background of“Promoting Teaching and Learning Through Competitions”

The inherent teaching approach can no longer meet the demands of society.In this paper,current issues within the teaching landscape of architectural engineering technology in higher vocational colleges as well as the policies and teaching demands that formed the basis of this model were analyzed.The study shows the importance of the implementation of the teaching model“promoting teaching and learning through competitions.”This model puts emphasis on the curriculum and teaching resources,while also integrating the teaching process and evaluation with competition.These efforts aim to drive education reform in order to better align with the objectives of vocational education personnel training,while also acting as a reference for similar courses.

On the Teaching Design of Graduates’EAP Course:Enhancing Language Proficiency and Critical Thinking Skills

This paper explores the integration of the bridge-in,objectives,pre-assessment,participatory activities,post-assessment and summary(BOPPPS)teaching model within the context of the post-graduates Academic English course.It discusses how this structured approach can effectively enhance students’language proficiency,foster critical thinking skills,and align with the multifaceted objectives of advanced English language education.The study provides a detailed examination of each BOPPPS component as applied to the post-graduates Academic English curriculum,supported by theoretical underpinnings and practical implications.

MCL4DGA:基于多视角对比学习的DGA域名检测方法

在网络安全领域,由域名生成算法(domain generation algorithm,DGA)产生的虚假域名被称为DGA域名.与正常域名类似的是,DGA域名通常是字母或数字的随机组合,这使得DGA域名具有较强的伪装性.网络黑客利用DGA域名的伪装性实施网络攻击,以达到绕过安全检测的目的.如何有效地对DGA域名进行检测,进而维护信息系统安全,成为当前的研究热点.传统的统计机器学习检测方法需要人工构建域名字符特征集合.然而,人工或者半自动化方式构建的域名特征存在质量参差不齐的情况,进而影响检测的准确性.鉴于深度神经网络强大的特征自动化抽取和表示能力,提出一种基于多视角对比学习的DGA域名检测方法(MCL4DGA).与现有方法不同的是,所提方法结合了注意力神经网络、卷积神经网络和循环神经网络,能够有效地捕获域名字符序列中的全局、局部和双向多视角特征依赖关系.除此之外,通过多视角表示向量之间的对比学习而产生的自监督信号,能够增强模型的学习能力,进而提高检测的准确性.通过在真实数据集上与当前DGA域名检测方法实验对比验证了所提方法的有效性.

The Obstacles on Learning European Communication at Foreign Language Course in Malang Raya

Communication inevitably becomes an essential part of interaction among people in every part of the world.Foreign language course provides opportunities for those who need to improve their foreign language skills like English,French,and German.However,this course finds some obstacles to the learning process like different curriculum,teaching technique,the background of the target language,pattern,as well as its culture.This research aims to dig out the learner’s obstacles and solutions in learning European language on communication at a foreign language course in Malang Raya.As the number of participants who put their interest more in learning European language course is growing,this research applied descriptive quantitative method by taking the population from the learners of European foreign language courses with purposive sampling.Meanwhile,the instrument used was a questionnaire,and it was analyzed by using the percentage formula.The finding shows that the obstacles to learning European communication consist of linguistic and non-linguistic obstacles,whereas the solutions done are various.

Evaluation of Technical Education by Using a Modern Structured MOODLE Laboratory Course, in Relation to Recent Data

E-learning platforms support education systems worldwide, transferring theoretical knowledge as well as soft skills. In the present study high-school pupils’, and adult students’ opinions were evaluated through a modern structured MOODLE interactive course, designed for the needs of the laboratory course “Automotive Systems”. The study concerns Greek secondary vocational education pupils aged 18 and vocational training adult students aged 20 to 50 years. The multistage, equal size simple random cluster sample was used as a sampling method. Pupils and adult students of each cluster completed structured 10-question questionnaires both before and after attending the course. A total of 120 questionnaires were collected. In general, our findings disclosed that the majority of pupils and adult students had significantly improved their knowledge and skills from using MOODLE. They reported strengthening conventional teaching, using the new MOODLE technology. The satisfaction indices improved quite, with the differences in their mean values being statistically significant.

Empowerment of Artificial Intelligence in Teaching Reform of Ideological and Political Courses in Universities

The rapid development of artificial intelligence(AI)technology has brought new opportunities and challenges to the field of education.As an important link in cultivating students’comprehensive quality and socialist core values,it is necessary to carry out continuous teaching reform and innovation in ideological and political courses in colleges and universities.Based on the concept of AI empowering the teaching reform of ideological and political courses,this study aims to explore how to use artificial intelligence technology to improve the teaching effect and learning experience of ideological and political courses.The research first analyzes the application status of artificial intelligence technology in education,and then discusses the application potential of artificial intelligence in ideological and political courses.Subsequently,the teaching reform strategy of ideological and political courses based on artificial intelligence is proposed,including the use of virtual reality technology,the application of intelligent auxiliary teaching tools to enhance personalized teaching,and the construction of an intelligent learning management system.Lastly,a case analysis is conducted to explore the implementation effect of the teaching reform of ideological and political courses in universities.The results showed that the application of artificial intelligence technology can effectively improve the teaching effect and learning experience of ideological and political courses,and provide new ideas and methods for the teaching reform of ideological and political courses in universities.

On Translation Courses Teaching and Learning in Blended Learning

Translation is a work requiring both independence and collaboration.Blended learning is an education mode that combines online digital media with traditional classroom methods.In adoption of this mode in translation courses teaching and learning,the researcher aims at improving the teaching outcomes and enhancing the students’learning participation as well as sense of translation and further positively changing their learning behaviors.

Towards an Understanding of Situated Learning:Students' Perceptions in an Interpretation Course

Situated learning approach has been applied in various teaching contexts, and increasing advocations have been shown in the particular context of language teaching. This study applied situated learning appoach in the interpretation course of college English major students and is designed to gain the students' perception of stuated learning. From the study, it is found that most of the students showed their positive attitudes towards situated learning approach applied in their interpretation course and enjoyed their learning in such learning enviroment. However, there is still a long way to go to achieve their satisfaction. Teachers need to explore more for creating authentic atmosphere in the interpretation course to arise students' interest and passion to a greater degree.

Course Evaluation Based on Deep Learning and SSA Hyperparameters Optimization

Sentiment analysis attracts the attention of Egyptian Decisionmakers in the education sector.It offers a viable method to assess education quality services based on the students’feedback as well as that provides an understanding of their needs.As machine learning techniques offer automated strategies to process big data derived from social media and other digital channels,this research uses a dataset for tweets’sentiments to assess a few machine learning techniques.After dataset preprocessing to remove symbols,necessary stemming and lemmatization is performed for features extraction.This is followed by several machine learning techniques and a proposed Long Short-Term Memory(LSTM)classifier optimized by the Salp Swarm Algorithm(SSA)and measured the corresponding performance.Then,the validity and accuracy of commonly used classifiers,such as Support Vector Machine,Logistic Regression Classifier,and Naive Bayes classifier,were reviewed.Moreover,LSTM based on the SSA classification model was compared with Support Vector Machine(SVM),Logistic Regression(LR),and Naive Bayes(NB).Finally,as LSTM based SSA achieved the highest accuracy,it was applied to predict the sentiments of students’feedback and evaluate their association with the course outcome evaluations for education quality purposes.