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。

基于文献检索课的E-Learning Portfolio的设计与实现

随着信息技术的发展,学习观念、内容、形式等均发生了较大变化。本文从提高学生的信息素养的角度简要分析E-Learning Portfolio适用于文献检索课的学习的基础上,重点阐述了文献检索课ELP的设计思想、电子学档开发的不同层次、电子学档的软件平台及简易电子学档的申请制作举例。

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。

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.

A Portfolio Selection Method Based on Pattern Matching with Dual Information of Direction and Distance

Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.

Using Return and Risk Model for Choosing Perfect Portfolio Applied Study in Cairo Stock Exchange

Modern financial theory, commonly known as portfolio theory, provides an analytical framework for the investment decision to be made under uncertainty. It is a well-established proposition in portfolio theory that whenever there is an imperfect correlation between returns risk is reduced by maintaining only a portion of wealth in any asset, or by selecting a portfolio according to expected returns and correlations between returns. The major improvement of the portfolio approaches over prior received theory is the incorporation of 1) the riskiness of an asset and 2) the addition from investing in any asset. The theme of this paper is to discuss how to propose a new mathematical model like that provided by Markowitz, which helps in choosing a nearly perfect portfolio and an efficient input/output. Besides applying this model to reality, the researcher uses game theory, stochastic and linear programming to provide the model proposed and then uses this model to select a perfect portfolio in the Cairo Stock Exchange. The results are fruitful and the researcher considers this model a new contribution to previous models.

A Novel Momentum-Based Measure for Online Portfolio Algorithm

In recent years, digital investment portfolios have become a significant area of interest in the field of machine learning. To tackle the issue of neglecting the momentum effect in risk asset prices within the follow-the-winner strategy and to evaluate the significance of this effect, a novel measure of risk asset price momentum trend is introduced for online investment portfolio research. Firstly, a novel approach is introduced to quantify the momentum trend effect, which is determined by the product of the slope of the linear regression model and the absolute value of the linear correlation coefficient. Secondly, a new investment portfolio optimization problem is established based on the prediction of future returns. Thirdly, the Lagrange multiplier method is used to obtain the analytical solution of the optimization model, and the soft projection optimization algorithm is used to map the analytical solution to obtain the investment portfolio of the model. Finally, experiments are conducted on five benchmark datasets and compared with popular investment portfolio algorithms. The empirical findings indicate that the algorithm we are introduced is capable of generating higher investment returns, thereby establishing its efficacy for the management of the online investment portfolios.

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。

E-learning网络平台的构建及应用效果分析

目的:传统课程以面对面授课为主,不能满足现代教学发展的需要。Elearning是在线学习新方式,通过计算机互联网或手机无线网络进行网络授课、答疑和论坛交流。方法:通过E-learning网络平台的构建及应用,对比分析网络平台应用前后学生掌握知识的程度和师生交流的密切程度,实验组与对照组医学生的实验考核、理论考试成绩输入Excel 2007,并用Stata 19.0统计分析。结果:常规课堂教学以外开发E-learning平台并强化利用,确能提高学生成绩,促进师生互动,不失为传统授课方式的有益补充。结论:课堂教学为主,E-learning学习为辅的教学模式符合当前信息社会的发展趋势和学生的学习规律,相辅相成,共同促进医学生知识的掌握和临床技能水平的提高。但E-learning平台建设是一项系统工程,必须长远规划,循序渐进,逐步完善,方能发挥更大效能。

面向e-Learning的概念知识元表征模型研究

知识表征是个性化学习系统的基础。由于知识本身具有多粒度、多维度的特征,而且知识点与知识点之间存在着复杂的语义联系,因此通用的"资源—索引—元数据"的三级资源组织模式不能很好地满足学习者知识学习的需要。针对这一现象,提出了面向e-Learning的概念知识元表征模型,并给出了基于概念知识元的知识组织方法。通过知识元的链接、聚合和融合,形成领域的全局主题图,以描述领域的全局知识结构。为了说明模型的有效性,以教育技术学中的"调查研究法"和初等数学中的"勾股定理"为例,给出了概念知识元的实例,并通过RDF的描述方式,对概念知识元进行了描述和存储。最后探讨了知识元模型在e-Learning中的典型应用,并介绍了基于概念知识元模型的教育技术学科元知识搜索系统。实践证明,该系统在概念学习中能起到良好的辅助作用。

知识管理技术与e-Learning资源库建设研究

资源库是网络化学习的重要基础 ,是教育领域一个全新的增长点。而知识管理是目前在国际管理领域成功运作的一种科学的、高效的管理办法。把知识管理技术引入资源库建设 ,是一种尝试 ,它可以使我们高瞻远瞩 ,清晰地把握资源库建设的方方面面 ,成功建设与管理 e- L earning资源库。本文从资源库的现状出发 ,剖析和介绍了知识管理技术 ,并用知识管理技术分析 e- L earning资源库建设的几个问题 ,探讨教学资源库建设的途径与方法 ,并对资源库的发展提出了一些看法。

学习交互的现状与未来发展——从课堂学习到e-Learning,m-Learning再到u-Learning

学习交互已成为影响教育活动的关键因素之一。本文以学习环境发展历程作为学习交互的分析视角,以交互作为探讨的经纬,分别从不同学习形式,即从课堂学习,到e-Learning,再到m-Learning,未来发展为u-learning学习来研究交互在教育教学中的不同表现形式、理论基础和特征等,并对未来学习交互进行了展望。笔者希望通过本文对未来学习交互的探讨,能够引起更多的研究者对此问题的关注,并引发对未来教育改革的思考。

混合学习模式下e-Learning平台使用意愿的影响因素研究

e-Learning平台的普及不仅依赖于技术的先进性,更在于用户的接受和使用程度。从网络外部性和交互性两个视角拓展了技术接受模型,构建了e-Learning平台使用意愿影响因素的理论模型,并提出了一系列研究假设。通过问卷调查的方式收集到316份有效问卷,利用SmartPLS软件进行结构方程模型分析。实证结果表明:提出的理论模型具有良好的信度和效度;相关网络规模、感知补充性、人与系统交互、人与人交互、感知易用性和感知有用性对使用意愿有显著的正向影响,其中感知有用性的影响最强,其次是感知易用性,然后是相关网络规模。通过对该结果进行讨论,指出了e-Learning平台未来发展的重要方向,包括亟待改善因素和重点保持因素。