A Stacking Machine Learning Model for Student Performance Prediction Based on Class Activities in E-Learning

After the spread of COVID-19,e-learning systems have become crucial tools in educational systems worldwide,spanning all levels of education.This widespread use of e-learning platforms has resulted in the accumulation of vast amounts of valuable data,making it an attractive resource for predicting student performance.In this study,we aimed to predict student performance based on the analysis of data collected from the OULAD and Deeds datasets.The stacking method was employed for modeling in this research.The proposed model utilized weak learners,including nearest neighbor,decision tree,random forest,enhanced gradient,simple Bayes,and logistic regression algorithms.After a trial-and-error process,the logistic regression algorithm was selected as the final learner for the proposed model.The results of experiments with the above algorithms are reported separately for the pass and fail classes.The findings indicate that the accuracy of the proposed model on the OULAD dataset reached 98%.Overall,the proposed method improved accuracy by 4%on the OULAD dataset.

Efficient Intelligent E-Learning Behavior-Based Analytics of Student’s Performance Using Deep Forest Model

E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures.This article proposes a new analytics systemto support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning environments.The proposed e-learning analytics system includes a new deep forest model.It consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural networks.The developed forest model can analyze each student’s activities during the use of an e-learning platform to give accurate expectations of the student’s performance before ending the semester and/or the final exam.Experiments have been conducted on the Open University Learning Analytics Dataset(OULAD)of 32,593 students.Our proposed deep model showed a competitive accuracy score of 98.0%compared to artificial intelligence-based models,such as ConvolutionalNeuralNetwork(CNN)and Long Short-TermMemory(LSTM)in previous studies.That allows academic advisors to support expected failed students significantly and improve their academic level at the right time.Consequently,the proposed analytics system can enhance the quality of educational services for students in an innovative e-learning framework.

E-learning在医学教育领域内的实践与研究

科学技术与医学教育的发展,对医学教育的过程及途径提出新要求。电子学习(E-learning)作为一种新型教学方式,早已在教育、培训等领域广泛应用,尤其是在国外。在国内医学教育领域中,E-learning也得到迅速发展,并发挥积极作用。随着这种应用趋势的展开,不少学者对此进行相关研究。本文在对E-learning方式进行解读的基础上,追溯其发展历程与在医学教育领域内的应用现状,对相关的国内外研究资料进行回顾,为E-learning在国内医学教育的实践研究提供建设性启示。

基于E-learning的翻转课堂模式在消化系统疾病实习教学中的应用效果

目的 探讨基于E-learning的翻转课堂模式在消化系统疾病实习教学中的应用效果。方法 选取2021年8月至2022年7月在新疆医科大学第一临床医学院就读的200名临床医学专业学生,采用随机数字表法分为研究组(100名)与对照组(100名)。研究组采用基于E-learning的翻转课堂模式教学,对照组实施传统教学模式教学。比较两组学生自我评价、教师对学生能力评价以及理论考核和临床操作考核成绩。结果 研究组学生学习兴趣、学习效率、学习信心、总体满意度的自我评价均高于对照组(P均<0.05);带教教师对研究组学生自主学习能力、团队协作能力、临床思维能力、临床技能能力评价均高于对照组(P均<0.05);研究组学生理论考核和临床操作考核成绩均高于对照组(P均<0.05)。结论 基于E-learning的翻转课堂模式可有效培养学生临床思维和操作能力,提升消化系统疾病实习教学效率及教学效果。

基于HubGLasso注意力机制的脑网络分类研究

脑网络分类有助于脑疾病的早期诊断,也有益于理解脑疾病发病机理,具有重要的研究与应用价值。其中,卷积神经网络应用广泛,可以提取脑网络的拓扑特征,是脑网络分类中的一个前沿热点。然而,现有方法未考虑脑网络中Hub节点对脑功能的重要贡献,这可能会导致特征提取不充分,限制了它们的分类性能。为此,该文提出了一种基于HubGLasso注意力机制的卷积神经网络模型,用于进行脑网络分类任务。该方法包含了一种新的卷积层结构,首先利用GLasso模型去除脑网络中的冗余信息,然后引入Hub约束与注意力机制,使其能够提取与异常Hub结构相关的重要特征,并用于脑疾病诊断。实验结果表明,该方法在包含1112个被试的真实自闭症数据集上取得了68.67%的准确率,显著优于目前已有方法,证明了其应用价值。更进一步,通过对训练后的模型进行特征分析,能够得到与脑疾病相关的脑区信息与Hub节点结构信息,为脑疾病病理机制的研究提供了全新的视角。

基于加权基因共表达网络和癌症基因组图谱临床数据分析并鉴定肝细胞癌的Hub基因研究

背景 肝细胞癌(HCC)是全球常见的癌症相关死亡的第三大原因,约占所有原发性肝癌病例的90%,其复发率和死亡率较高,目前发生的分子机制仍不清楚。目的 探索HCC潜在的分子机制,发掘新的生物标志物。方法 从TCGA数据库下载RNA-seq表达数据和临床相关信息,通过差异基因表达分析正常肝脏组织与HCC组织的差异基因;对差异表达基因进行富集分析;基于TCGA中HCC的基因表达数据概况,使用WGCNA R包建立共表达网络,进行加权基因共表达网络分析(WGCNA),选择具有临床意义的模块,并筛选候选Hub基因;进一步分析候选Hub基因在HCC组织和正常肝脏组织显著差异表达、与HCC患者总体生存期和无病生存期是否显著相关,最终确定Hub基因;通过人类蛋白质图谱数据库对Hub基因蛋白表达进行验证。结果 本研究的基因表达数据来自50个正常肝脏组织样本和373个HCC组织样本。通过差异基因表达分析发现7 230个在HCC和正常肝脏组织之间差异表达的基因(HCC中3 691个上调基因和3 539个下调基因)。富集分析表明,上调的差异表达基因主要参与细胞周期调控和有丝分裂过程;下调的差异表达基因主要参与小分子代谢和有机酸代谢等过程。WGCNA确定了19个与HCC患者临床特征相关基因模块,通过分析模块与临床特征之间的关系,筛选出青色模块和紫色模块。青色模块基因中同时与患者总生存期和无病生存期强烈相关的前两个基因为VPS45和FAM189B;紫色模块基因中同时与患者总生存期和无病生存期强烈相关的前两个基因分别为CLEC1B和FCN3,因此将VPS45、FAM189B、CLEC1B和FCN3确定为最终的Hub基因。人类蛋白质图谱数据库免疫组织化学染色显示:VPS45和FAM189B在HCC组织中的表达高于正常肝脏组织,FCN3在HCC组织中的表达低于正常肝脏组织,CLEC1B在HCC组织和正常肝脏组织中表达差异不明显。结论 初步确定VPS45、FAM189B、CLEC1B和FCN3可能是HCC的新型潜在生物标志物,这些Hub基因可能为HCC的靶向治疗提供理论基础。

Blockchain-Based Decentralized Authentication Model for IoT-Based E-Learning and Educational Environments

In recent times,technology has advanced significantly and is currently being integrated into educational environments to facilitate distance learning and interaction between learners.Integrating the Internet of Things(IoT)into education can facilitate the teaching and learning process and expand the context in which students learn.Nevertheless,learning data is very sensitive and must be protected when transmitted over the network or stored in data centers.Moreover,the identity and the authenticity of interacting students,instructors,and staff need to be verified to mitigate the impact of attacks.However,most of the current security and authentication schemes are centralized,relying on trusted third-party cloud servers,to facilitate continuous secure communication.In addition,most of these schemes are resourceintensive;thus,security and efficiency issues arise when heterogeneous and resource-limited IoT devices are being used.In this paper,we propose a blockchain-based architecture that accurately identifies and authenticates learners and their IoT devices in a decentralized manner and prevents the unauthorized modification of stored learning records in a distributed university network.It allows students and instructors to easily migrate to and join multiple universities within the network using their identity without the need for user re-authentication.The proposed architecture was tested using a simulation tool,and measured to evaluate its performance.The simulation results demonstrate the ability of the proposed architecture to significantly increase the throughput of learning transactions(40%),reduce the communication overhead and response time(26%),improve authentication efficiency(27%),and reduce the IoT power consumption(35%)compared to the centralized authentication mechanisms.In addition,the security analysis proves the effectiveness of the proposed architecture in resisting various attacks and ensuring the security requirements of learning data in the university network.

Real Objects Understanding Using 3D Haptic Virtual Reality for E-Learning Education

In the past two decades,there has been a lot of work on computer vision technology that incorporates many tasks which implement basic filtering to image classification.Themajor research areas of this field include object detection and object recognition.Moreover,wireless communication technologies are presently adopted and they have impacted the way of education that has been changed.There are different phases of changes in the traditional system.Perception of three-dimensional(3D)from two-dimensional(2D)image is one of the demanding tasks.Because human can easily perceive but making 3D using software will take time manually.Firstly,the blackboard has been replaced by projectors and other digital screens so such that people can understand the concept better through visualization.Secondly,the computer labs in schools are now more common than ever.Thirdly,online classes have become a reality.However,transferring to online education or e-learning is not without challenges.Therefore,we propose a method for improving the efficiency of e-learning.Our proposed system consists of twoand-a-half dimensional(2.5D)features extraction using machine learning and image processing.Then,these features are utilized to generate 3D mesh using ellipsoidal deformation method.After that,3D bounding box estimation is applied.Our results show that there is a need to move to 3D virtual reality(VR)with haptic sensors in the field of e-learning for a better understanding of real-world objects.Thus,people will have more information as compared to the traditional or simple online education tools.We compare our result with the ShapeNet dataset to check the accuracy of our proposed method.Our proposed system achieved an accuracy of 90.77%on plane class,85.72%on chair class,and car class have 72.14%.Mean accuracy of our method is 70.89%.

Student’s Health Exercise Recognition Tool for E-Learning Education

Due to the recently increased requirements of e-learning systems,multiple educational institutes such as kindergarten have transformed their learning towards virtual education.Automated student health exercise is a difficult task but an important one due to the physical education needs especially in young learners.The proposed system focuses on the necessary implementation of student health exercise recognition(SHER)using a modified Quaternion-basedfilter for inertial data refining and data fusion as the pre-processing steps.Further,cleansed data has been segmented using an overlapping windowing approach followed by patterns identification in the form of static and kinematic signal patterns.Furthermore,these patterns have been utilized to extract cues for both patterned signals,which are further optimized using Fisher’s linear discriminant analysis(FLDA)technique.Finally,the physical exercise activities have been categorized using extended Kalmanfilter(EKF)-based neural networks.This system can be implemented in multiple educational establishments including intelligent training systems,virtual mentors,smart simulations,and interactive learning management methods.

无人车载网络高可靠HUB的设计

为满足无人车技术的不断发展及日趋复杂的车载网络带来的高带宽和高可靠性的使用要求,设计一种车载网络集线器(HUB)。采用时间触发协议(TTP),设计时间触发以太网(TTE)加星型TTP总线的架构。TTE提高数据带宽,在物理层与驱动设备采用点对点的连接方式代替总线型通信方式,提高可靠性,解决总线型通信方式一点断开,整体通信失效的问题。针对HUB的设计做详细阐述,通过实验验证了设计的可行性和可靠性,对车载网络的优化提供了可行的解决途径。

应用生物信息学筛选结直肠癌Hub基因及验证

目的通过运用生物信息学分析,筛选出与结直肠癌相关的差异表达基因(Differentially Expressed genes,DEGs),并验证其生物学功能。方法云浮市人民医院检验科从基因表达综合数据库(Gene Expression Omnibus,GEO)中下载结直肠癌芯片数据GSE 21815、GSE 31905、GSE 35279资料,应用GEO2R语言进行处理得出结直肠癌和正常结直肠组织之间的差异表达基因,并通过生物信息学工具DAVID、STRING、Cytoscape构建差异表达基因的蛋白互作网络,筛选Hub基因,应用基因本体论(Gene Ontology,GO)、基因百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)分析筛选出的Hub基因的生物功能,并利用MiRDB工具找出可能调控Hub基因的miRNA,并于2017—2022年8月收集30例结直肠癌组织和30例正常结直肠组织样本,通过实时荧光定量PCR(Quantitative Real-time PCR,qPCR)验证。结果经生物学信息分析和蛋白质相互作用网络图分析催产素受体基因、基质金属蛋白酶11基因、间质上皮转化因子基因、基质金属蛋白酶7基因、激肽释放酶8基因、激肽释放酶10基因为结直肠癌组织发生发展的关键Hub基因。结直肠癌组织中基质金属蛋白酶11基因(4.38±1.58)、间质上皮转化因子基因(2.69±0.29)、基质金属蛋白酶7基因(0.88±0.14)、激肽释放酶8基因(11.09±3.90)、激肽释放酶10基因mRNA(7.88±2.20)的表达,显著高于正常结直肠组织组织,差异有统计学意义(t=9.605、25.339、26.376、9.541、3.726,P均<0.001)。结论结直肠癌组织中基质金属蛋白酶11基因、间质上皮转化因子基因、基质金属蛋白酶7基因、激肽释放酶8基因、激肽释放酶10基因异常表达,可能参与结直肠癌发生过程,有望为后续结直肠癌基础研究及临床诊疗提供依据。

Identification of hub genes associated with Helicobacter pylori infection and type 2 diabetes mellitus:A pilot bioinformatics study

BACKGROUND Helicobacter pylori(H.pylori)infection is related to various extragastric diseases including type 2 diabetes mellitus(T2DM).However,the possible mechanisms connecting H.pylori infection and T2DM remain unknown.AIM To explore potential molecular connections between H.pylori infection and T2DM.METHODS We extracted gene expression arrays from three online datasets(GSE60427,GSE27411 and GSE115601).Differentially expressed genes(DEGs)commonly present in patients with H.pylori infection and T2DM were identified.Hub genes were validated using human gastric biopsy samples.Correlations between hub genes and immune cell infiltration,miRNAs,and transcription factors(TFs)were further analyzed.RESULTS A total of 67 DEGs were commonly presented in patients with H.pylori infection and T2DM.Five significantly upregulated hub genes,including TLR4,ITGAM,C5AR1,FCER1G,and FCGR2A,were finally identified,all of which are closely related to immune cell infiltration.The gene-miRNA analysis detected 13 miRNAs with at least two gene cross-links.TF-gene interaction networks showed that TLR4 was coregulated by 26 TFs,the largest number of TFs among the 5 hub genes.CONCLUSION We identified five hub genes that may have molecular connections between H.pylori infection and T2DM.This study provides new insights into the pathogenesis of H.pylori-induced onset of T2DM.

A Study on Integrated Construction of Luzhou Comprehensive Transportation Hub

The target of integrated construction of comprehensive transportation hub is to integrate the traffic resources,achieve butt joint of pan-regional transportation mode,and finally realize the"seamless connection"of the goods and"zero transfer"of the passenger traffic.Relying on the particularity of the geographical location and the convenience of the Yangtze River channel,Luzhou puts forth effort to build a comprehensive transport hub in Southern Sichuan and has made great efforts in traffic infrastructure construction.However,there are still some problems.Combining practice of Luzhou,using the advanced experience of foreign and domestic cities for reference,the paper pointed out that for the sake of constructing a comprehensive transportation hub,we need to build the traffic integration on the basis of the efficiency.Besides,the paper proposed the strategies for construction.

Mind+结合XEduHub开发AI交互作品

在图形化编程工具Mind+和通用AI推理库XEdu Hub的支持下,中小学生使用简短的代码就能设计出有趣的AI交互作品。本文在介绍Mind+和XEdu Hub的同时,分析了AI交互作品的核心环节,指出作品可以由感知输入、智能处理和执行呈现三个部分组成,希望为中小学开展AI教育提供新的思路。

Analysis of Roadbed Splicing at Hub Interchanges

This article discusses the roadbed splicing for hub interchanges.The article starts with a description of the characteristics of junction roadbed splicing.The application of splicing technology is explained using a subgrade splicing scheme of a project.Roadbed splicing involves stepwise excavation and preparative measures like surface cleaning and backfilling.This article serves to provide a valuable reference for road and bridge construction and improve the quality of China’s road and bridge projects,so as to achieve sustainable development of the road and bridge engineering industry.

TVU推出媒体信号路由新品MediaHub云调度

近期,TVU推出重磅新品——TVU MediaHub云调度。这一产品采用混合云构架的分布式处理技术,实现了任意地点、任意格式的媒体信号以多种格式传输到多个目的地。TVU MediaHub通过其全能的转换与调度功能,可以实现物理硬件与云端资源的集结,为用户提供一个能够灵活转换和高效调度全格式信号的平台。它采用基于先进微服务技术的架构,构建了一个灵活可靠的媒体信号调度中心,确保与TVU各类终端和系统的无缝集成,实现了输入输出的高效管理。此外,TVU MediaHub还与TVU云导播、云播出等服务实现无缝连接。它还引入了灵活的计费系统,根据用户的实际需求进行计费,从而优化成本。操作方面,TVU MediaHub通过其WebUI界面提供简化的操作流程,用户可以实时预览并通过拖拽信号来完成调度,极大地简化了操作步骤,提高了工作效率。

基于E-learning的混合式教学模式研究

在通过对当前教育环境的发展变化和E-Learning的发展与创新进行研究分析的基础上,对ELearning与传统教学模式进行比较,提出将基于先进的网络技术的E-learning教学模式与传统的教学模式有机地结合,互相补充,最大限度地体现各自教学模式的精华,强烈地激发学生主动学习的潜力,最终形成基于E-Learning的混合式教学模式。这是一种开放性、创新性的教育思想和模式,从而有效突破了传统校园教育的局限,提高了教学资源利用率,并促进了教学质量的提升,同时也增进了各高校之间的教学联系和学术交流,带来了更多的知识创新成果,最终提高了教育质量。

企业E-learning现状调查与分析——以TCL集团为研究个案

借助互联网的应用热潮,E-learning这一数字化学习方式已经成为各大企业开展员工培训的重要方式。TCL集团作为我国"数字化学习港"教学改革项目唯一的企业示范点,正努力探索"数字化学习型企业"的建设及运行规律;而企业管理者和员工对E-learning的认识、需求及习惯是创建"数字化学习型企业"的重要依据。本文以TCL集团为个案,通过对其下属公司管理者和员工的问卷调查、访谈与分析,发现企业培训主管和企业员工都对采用E-learning培训有强烈愿望并持肯定态度,但对E-learning的认识尚不够深入;同时企业在E-learning的组织实施及提供学习支持服务等方面仍需进一步探索。

本体驱动的e-Learning知识资源个性化推荐研究

e-Learning重要特点就是能够根据学习者特性、需求,适应性呈现学习资源和学习路径,实现个性化学习,有效促进学习目标达成。为实现e-Learning知识资源个性化推荐,该文采用基于知识推荐技术,同时基于本体技术创建学习者知识和知识资源,在教学模式的指导下实现知识资源个性化推荐。实验结果表明:学生能够很好理解本体概念,愿意学习本体知识,同时认为本体驱动的知识资源个性化推荐很有用,能够满足个性化学习需求、激发学习者学习动机、增强网络自主学习能力及优化学习过程。本文研究的意义:一是基于学习者特征讨论了合适的推荐技术;二是设计了学习者知识和学习资源的通用本体;三是为知识资源个性化推荐设计了有效指导教学模式。

基于情境感知和本体的E-Learning系统研究

为了合理、有效地描述E-Learning环境中学习者的学习情形,利用本体论具有知识组织清晰、概念和关系定义明确的特性,建立了E-Learning环境中的情境信息本体模型。为提高学习者的学习兴趣和学习效率,对采集到的情境信息进行情境推理后,采用资源调度控制算法调用相应的学习资源服务及时向学习者提供。应用实例验证了基于情境感知的本体E-Learning系统的服务性能。