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。

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%.

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.

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.

E-learning在医学教育中的应用及对中国医学教学改革的启示

医学科学的发展,对教育教学手段提出了新的要求。目前,在发达国家的医学教育领域中,E-learning至少是发挥了与传统的指导者所主导的学习方法相同的作用。文章论述了医学教育中开展E-learn-ing的必要性,介绍了国外E-learning1.0及E-learning2.0在医学领域中的应用情况,提出在我国医学教学改革中实施E-learning,既可以作为课堂教育的有益补充,也可以弥补教育资源的短缺。

Enablers, Difficulties and Features to Implement E-Learning Technology in Conventional Higher Education: Case Study and Prototype Implication

Supporting higher education with modern technologies like E-Learning is very important for one country to improve quality of education, to meet student’s expectations and to continue teaching-learning and training when face to face education is impossible. However, it is in its preliminary stage in developing countries like Ethiopia. This study examined the enabling factors, difficulties and opportunities of E-Learning implementation in Assosa University (ASU), Ethiopia. Its purpose is to find the enabling factors, difficulties and opportunities of E-Learning implementation in ASU and developing prototype of E-Learning system to show its practicality and to identify approaches of students’ and lecturers towards E-Learning. The study employed questionnaires, observation and interview to gather the required information. A sample of 309 students and 64 Lecturers randomly selected from 7 colleges and two schools as well as ICT workers and other concerned bodies in the university. Also prototyping as a methodology was used to implement and test the proposed system for proof of concept. This study investigating the possibility of implementing E-Learning in ASU and important enablers, difficulties and opportunities is identified. Also the E-Learning platform of the university is developed and introduced for students and lecturers to show its practicality. Most students and lecturers showed good motivation in E-Learning implementation and they assumed that it is useful for the university. The result shows that although there are difficulties to implement E-Learning in ASU, the possibility of fully implementing E-Learning in the University is relatively high with mixed method. With this, the approaches of students and lecturers are positively viewed and the opportunities are very noticeable in the University. So, conventional higher education can practically implement E-Learning with mixed approach to use as supportive tool for educational improvements and to reduce physical presence.

E-Learning研究现状和趋势探析

E-Learning作为一种全新的学习方式日益受到社会各界的重视,与此同时,我国企业E-Learning也取得了长足的发展。为了从整体上了解和掌握当前国内企业E-Learning的应用情况,从学术研究的角度对其研究现状进行详细分析,探讨目前E-Learning在我国企业应用中遇到的问题及不足,并对其未来的建设和研究趋势提出一些有针对性的建议。

Rapid E-Learning即将引领新时代

传统e-Learning因其周期长、耗费大、开发难等缺陷受到越来越多的质疑,Rapid e-Learning的出现恰好迎合了很多企业单位以及教育机构的需求。本文主要从Rapid e-Learning的特性出发,就其适用领域、实现方式、课程开发过程等进行了系统的研究,并认为随着金融危机、云时代的来临以及教育信息化发展需要,Rapid e-Learning必将掀起新的浪潮。

基于Rapid E-Learning的职业教育移动微课的交互研究

混合式教学和微课的勃兴背景下,传统的E-Learning资源开发模式内在缺陷日益凸显,传统的单播式微课也存在明显缺点。优质移动微课资源的开发,需要从E-Learning转向Rapid E-Learning,以推动交互的实现和优化。提出基于Rapid E-Learning的职业教育移动微课资源中交互之开发思路,其具有4大特点。最后,以著名的Rapid E-Learning工具Engage为例,说明交互式移动微课资源案例的开发和实现。

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。

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。

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。

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。

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。

模糊情感分析及其在E-learning系统中的应用

基于模糊数学,本文提出了用模糊数学的评价集方法对学习者进行综合评价,是模糊模式识别的一种新的扩展和应用,为拟人化和情感识别提供了一种新的研究思路。同时本文设计了一个具有情绪反应的智能Agent助理。根据以上提出的模糊情感识别,我们将模糊情感识别的结果应用于E-learning系统,从而实现了教学系统的人性化和智能化。

学习科学视域下的e-Learning深度学习研究

深度学习是学习科学的主旨,也是现阶段所要破解的e-Learning难题,因此,对学习科学视域下的e-Learning深度学习进行研究,追溯起源,把握热点对推进e-Learning学习意义深远。在梳理e-Learning的现状及主要问题的基础之上,剖析了学习科学领域深度学习的重要性,以深度学习的内涵与特征、有意义学习是深度学习的主旨、高水平思维是深度学习的核心等理念为依据构建了e-Learning环境下深度学习分析模型,继而又以此为据,对e-Learning深度学习的研究现状进行述评。研究发现,为了进一步推动学习科学的发展,深度学习的研究必须在学习过程的评估与分析、情感体验等方面进一步加强,在研究方法与研究视角方面进行多角度创新,研究应该更精细深入,多进行相关的量化研究,以充分利用学习评估技术对深度学习的现状进行把脉。

基于情感计算的e-Learning系统建模

e-Learning也叫数字化学习,是通过因特网或其他数字化媒体进行学习与教学的活动。情感计算是指关于情感、情感产生以及影响情感的计算,试图创建一种能感知、识别和理解人的情感,并能针对人的情感做出智能、灵敏、友好反应的计算机系统。本文将 e-Learning系统和情感计算结合在一起,提出了一个基于情感计算的 e-Learning系统模型,旨在有效地解决 e-Learning系统中情感交流匮乏的问题。