Design and Implementation of E-Learning System Based on J2EE

Because of the need of college education, some new methods have been adopted. One of them is e-learning system. So how to design a powerful e-learning system is the main concern of the authors. This paper first discusses the EJB component mechanism and the J2EE multi-tiered model, and then applies them to an e-learning system. The J2EE framework makes the e-learning system easier be developed and be of better performance.

Securing Fog Computing For E-Learning System Using Integration of Two Encryption Algorithms

Currently,the majority of institutions have made use of information technologies to improve and develop their diverse educational methods to attract more learners.Through information technologies,e-learning and learning-on-the go have been adopted by the institutions to provide affordability and flexibility of educational services.Most of the educational institutes are offering online teaching classes using the technologies like cloud computing,networking,etc.Educational institutes have developed their e-learning platforms for the online learning process,through this way they have paved the way for distance learning.But e-learning platform has to face a lot of security challenges in terms of cyberattacks and data hacking through unauthorized access.Fog computing is one of the new technologies that facilitate control over access to big data,as it acts as a mediator between the cloud and the user to bring services closer and reduce their latency.This report presents the use of fog computing for the development of an e-learning platform.and introduced different algorithms to secure the data and information sharing through e-learning platforms.Moreover,this report provides a comparison among RSA,AES,and ECC algorithms for fog-enabled cybersecurity systems.These Algorithms are compared by developing them using python-based language program,in terms of encryption/decryption time,key generations techniques,and other features offered.In addition,we proposed to use a hybrid cryptography system of two types of encryption algorithms such as RSA with AES to fulfill the security,file size,and latency required for the communication between the fog and the e-learning system.we tested our proposed system and highlight the pros and cons of the Integrated Encryption Schemes by performing a testbed for e-learning website scenario using ASP.net and C#.

Implementation of E-Learning System: Findings and Lessons Learned

This paper describes the implementation of the e-learning system at the School of Mathematics and Computer Science, National University of Mongolia. The paper includes in-house development of Edunet 1.0 e-learning system, comparative analysis on LMS, evaluation methodology, selection of e-learning systems, and comparative analysis on implementation of Edunet, Moodle and Canvas systems.

Student Behavior Modeling for an E-Learning System Offering Personalized Learning Experiences

With the advent of computing and communication technologies,it has become possible for a learner to expand his or her knowledge irrespective of the place and time.Web-based learning promotes active and independent learning.Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process.This digital learning improves the quality of teaching and also promotes educational equity.However,the challenges in e-learning platforms include dissimilarities in learner’s ability and needs,lack of student motivation towards learning activities and provision for adaptive learning environment.The quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional strategy.It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course.It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest level.In this research work,a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective attainment.Catering to the demands of e-learner,an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm.An adaptive e-learning system suits every category of learner,improves the learner’s performance and paves way for offering personalized learning experiences.

Development of an Asynchronous Web Based E-Learning System

Advancements in Information Communication Technology (ICT) have led to several opportunities especially the ones provided by the Internet. Several people are now taking advantage of distance learning courses and in the past few years huge research efforts have been dedicated to the development of distance learning systems. So far, many e-learning systems are proposed and used practically. This paper focused on the development of an asynchronous and interactive Web-based e-learning system. Its primary objective is to develop a fast, reliable, effective and efficient web-based e-learning system that will address the problems associated with the traditional learning system. Succinctly, the paper discusses the design of a system that enhances e-learning where course lecturers can set their courses, tests and quizzes at their convenient time and can track the activities and performance of their students and guide them to acquire knowledge without being obliged to be physically present on the institution campus. The system was designed using PHP as the scripting language, Macromedia Dreamweaver for the web page, MySQL as the database and Apache as the web server. The system was implemented using real data and the result was successful. This system is no doubt a solution to the constraints of the classical learning system and can be used successfully in distance learning, training, and various educational institutions.

The Construction of E-learning system Based on Ontology

To solve the irregular, poor efficiency and lowly reusable of resource, the hierarchy model of the ontology-based E-learning system is proposed. Some key techniques in the process of the project are also discussed in this paper, such as the ontology construction, the content ontology for describing the semantics of the learning materials.

Factors Affecting the Adoption of E-Learning Systems in Public Higher Learning Institutions in Tanzania: A Case of Institute of Accountancy Arusha (IAA)

In most developing countries, governments attempt to enforce the movement from analogue to digital for all their sectors, from public to private. These technological advancements have been noted to bring necessary and unavoidable changes to businesses and learning environments. Higher learning institutions have adopted various e-learning systems to support learning, research, and publication activities to stay competitive in global academic systems. However, most public higher learning institutions in Tanzania lag behind in the adoption of these systems. Thus, research shows a failure of these institutions in utilising the full benefit that today’s Information and Communication Technology (ICT) can offer in learning environments. Thus, this study examines factors affecting the adoption of such a system in developing countries like Tanzania, taking the Institute of Accountancy Arusha (IAA) as a case study. The study used a mixed methodology where thematic and descriptive analysis was used to analyse both qualitative and quantitative research data. The study population was 187 teaching staff, a sample size of 126 was obtained, and 157 study participants were involved in the study. The study found that factors affecting the adoption of e-learning systems in public higher learning institutions in Tanzania include lack of ICT infrastructure, lack of technical and managerial support and lack of computers and e-learning knowledge among facilitators. Thus, the study recommended investments in adequate and reliable ICT facilities, high intermate speed and bandwidth, and policies that support e-learning and training programs about e-learning knowledge and use. Also, this study recommends the use of the Multi-Factors Adoption Model (MFAM11) for the successful adoption of an e-learning system in public higher learning institutions in Tanzania.

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.

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。