AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus.The foremost and most prime sector among those affected were schools,colleges,and universities.The education system of entire nations had shifted to online education during this time.Many shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of LMSs.This paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user experience.The AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based chatbots.Session layer enhancements are also required,such as AI-based online proctoring and user authentication using Biometrics.These extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of AI-algorithms.It also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

Towards Automated Assessment of Learning Management Systems in Higher Education Institutions in Zambia

Zambia like any other country in most African regions is still grappling with the dynamics of harnessing technology for the betterment of Higher Education. The onset of the Covid 19 pandemic brought a test for the preparedness of the Zambian Higher Education Institutions (HEIs) in harnessing technology for pedagogical activities. As countries worldwide switched to electronic learning during the pandemic, the same could not be said for Zambian HEIs. Zambian HEIs struggled to conduct pedagogical activities on learning management platforms. This study investigated the factors affecting the implementation and assessment of learning Management systems in Zambia’s HEIs. With its focus on assessing: 1) system features, 2) compliance with regulatory standards, 3) quality of service and 4) technology acceptance as the four key assessment areas of an LMS, this article proposed a model for assessing learning management systems in Zambian HEIs. To test the proposed model, a software tool was also developed.

Rumi: An Intelligent Agent Enhancing Learning Management Systems Using Machine Learning Techniques

The dramatic improvement of information and communication technology (ICT) has made an evolution in learning management systems (LMS). The rapid growth in LMSs has caused users to demand more advanced, automated, and intelligent services. This paper discusses how Artificial Intelligence and Machine Learning techniques are adopted to fulfill users’ needs in a social learning management system named “CourseNetworking”. The paper explains how machine learning contributed to developing an intelligent agent called “Rumi” as a personal assistant in CourseNetworking platform to add personalization, gamification, and more dynamics to the system. This paper aims to introduce machine learning to traditional learning platforms and guide the developers working in LMS field to benefit from advanced technologies in learning platforms by offering customized services.

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.

Parallel Reinforcement Learning-Based Energy Efficiency Improvement for a Cyber-Physical System

As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy.Energy management strategy(EMS)is playing a key role to improve the energy efficiency of this CPS.This paper presents a novel bidirectional long shortterm memory(LSTM)network based parallel reinforcement learning(PRL)approach to construct EMS for a hybrid tracked vehicle(HTV).This method contains two levels.The high-level establishes a parallel system first,which includes a real powertrain system and an artificial system.Then,the synthesized data from this parallel system is trained by a bidirectional LSTM network.The lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning(RL)framework.PRL is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined rules.Finally,real vehicle testing is implemented and relevant experiment data is collected and calibrated.Experimental results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.

The Effectiveness of Blackboard System, Uses and Limitations in Information Management

This paper presents a review of the literature on the effectiveness of Blackboard system, its uses and limitations in information management and highlights the ongoing debate of it. A critical evaluation of Blackboard system literature reveals a good number of academic views, studies, theories, models and experiences regarding the virtual learning environment. The extant literature shows that the world of information management is always in flux as it is being impacted on by the learning technology such as blackboard system. Blackboard system now has a recognised presence in the information management of the education system. Now the question is: how effective is this Blackboard system? The article will explore, in part, how blackboard is designed as suitable learning models in terms of learner cognitive engagement and constructivist perspective, resulting in the effective Blackboard system. It will further cover to review the effectiveness of Blackboard as an aid to pedagogy. Finally, the article will also present a comparison between Blackboard and other LMS such as Moodle to explore the effectiveness and limitations of the Blackboard for better academic information management.

基于LMS AMESim的车用燃料电池-锂离子动力电池混合动力系统能量管理仿真

基于20kW燃料电池电堆及燃料电池测试系统,获得燃料电池极化曲线及氢气消耗量曲线;基于锂离子动力电池充放电系统,获得锂离子动力电池输出电压曲线.将试验所得数据导入到LMS AMESim软件中,分别构建燃料电池及锂离子动力电池模块,同时,构建仿真平台其他所需模块并搭建DC/DC变换器模型,建立燃料电池-锂离子动力电池混合的动力系统仿真平台.依据不同动力源的各自特点,引入能量控制策略,对该混合动力系统进行模拟仿真.在所选定新欧洲驾驶循环(new European driving cycle,NEDC)工况下仿真结果表明,该混合动力系统可以满足车辆在所选定工况下的动力需求.DC/DC变换器可提升并稳定燃料电池输出电压跟随母线电压,并通过对电流的分配进行功率在不同动力源之间的分配;燃料电池输出功率在合理范围之内,并取消燃料电池在低功率下的工况,从而保护燃料电池,延长其使用寿命;锂离子动力电池荷电状态(state of charge,SOC)始终保持在合理范围内,未出现过充或过放情况.研究结果可为搭建混合动力试验平台及整车搭载匹配提供理论依据及参考.

从“工作流”到“学习流”:E-learning系统设计的新视角

目前大多数E-learning支持系统基于内容对象设计,由此造成的学习者控制、网络迷失和认知超载等问题已成为制约E-learning发展的瓶颈。本文借鉴"工作流"的理念,结合学习活动的流程化特性,提出了"学习流"的概念,并建立了学习流的数学模型——ATC模型,在此基础上分析了学习流的构成要素和控制策略设计,试图为E-learning系统的设计和开发提供新的思路和视角。最后简要介绍了本研究开发的基于"学习流"理念的管理系统功能架构,该系统已于2006年成功地应用于一项由教育部主持的面向全国的教师培训项目。研究结果表明,学习者对于"学习流"管理系统支持下的培训有着很高的评价和认同度;采用基于"学习流"的方式组织和管理学习活动,可以大大提高系统的易用性和学习活动的组织效率,避免学习者网络迷航,从而为实施有效教学、促进有意义的学习创造了条件。

E-Learning系统的框架与设计新理念

第一代e-learning系统将全部焦点集中于对培训过程的管理和测量,它们并没有或只赋予学习过程极少的价值,也没有对培训内容的创作过程提供支持,相反却只依赖各种商业课件。此外,学习管理系统经常被认为是实施e-learning唯一需要的因素。这导致e-learning项目演变成纯粹的技术性过程,最终导致在实施中付出昂贵的成本但效率却不高。本文尝试从知识管理的视野入手,以新的理念构建一个完整高效的e-learning系统框架,并深入分析该系统的各个组成要素。

E_Learning在电子信息技术课程中的应用

本文根据E_Learning的特点,分析了在电子信息技术课程中引入E_Learning的必要性和优势,阐述了Blending Learning(混合学习)理论及其主要教学模式以及如何正确选择教学模式,最后介绍了东南大学在电子信息技术课程教学中采用E_Learning技术的经验、成绩和心得。

基于M-learning的教学模式研究——以信息管理与信息系统专业为例

移动学习(M-learning)作为一种崭新的学习形式,能够有效地拓展学生学习知识的空间和时间,随地学习,使学生的应用能力大大提高.深入分析了移动学习的基本要求,针对信息管理与信息系统专业的特征与发展需要,研究与设计了基于M-learning的信息管理与信息系统专业的课程教学模块.

IMS环境下的E-Learning系统设计

结合开源Moodle系统,描述了基于IMS架构的E-Learning系统实现的可能性。为了使学习系统对终端用户(教师、学生、受训人员)有效,在IMS平台中定义了新的成员,包括学习内容部署服务器、讲义管理服务器及讲义管理桌面,通过这些组件实现Moodle系统中的教学管理系统功能。

E-Learning系统中的知识链管理模型

针对目前E-learning系统服务对象较单一,知识的分层处理、多层知识的交互协同供给能力较弱,以及E-learning系统的知识管理和处理方式研究较少的问题,对E-learning系统中的知识链及其知识管理系统架构进行了探究,为增强E-learning系统的知识分层管理能力和提高E-learning系统的知识服务能力的研究做铺垫。

E-Learning系统的框架与设计新理念

第一代e-learning系统将全部焦点集中于对培训过程的管理和测量,它们并没有或只赋予学习过程极少的价值,也没有对培训内容的创作过程提供支持,相反却只依赖各种商业课件。此外,学习管理系统经常被认为是实施e-learning唯一需要的因素。这导致e-learning项目演变成纯粹的技术性过程,最终导致在实施中付出昂贵的成本但效率却不高。本文尝试从知识管理的视野入手,以新的理念构建一个完整高效的e-learning系统框架,并深入分析该系统的各个组成要素。

知识管理视角下的E-learning课程体系设计研究

尽管E-learning已经成为教育机构的一种战略性知识传递模式,得到了广泛应用,但是由于课程体系设计理念局限在吸收模式,不能达成高层面的学习模式,而且学习者易产生"隔绝"感,因此其应用范围和效果受限。本文从知识管理视角重新审视课程体系设计,提出以知识管理的编码化策略和个性化策略为指导,广泛使用知识管理技术构建后台知识体系和前台互动界面实现有效的E-learning个体学习和群体学习。通过知识管理和E-learning的结合构建了一个由具有开放性、动态性、联通性、分布性和社交关注性等特点的知识库和有利学习环境组成的课程体系,改善学习者的学习绩效。

Load Prediction Methods Using Machine Learning for Home Energy Management Systems Based on Human Behavior Patterns Recognition

Load forecasting can enhance the reliability and efficiency of operations in a home energy management system(HEMS).The rise of big data with machine learning in recent years makes it a potential solution.This paper proposes two new energy load forecasting methods,enhancing the traditional sequence to sequence long short-term memory(S2S-LSTM)model.Method 1 integrates S2S-LSTM with human behavior patterns recognition,implemented and compared by 3 types of algorithms:density based spatial clustering of applications with noise(DBSCAN),K-means and Pearson correlation coefficient(PCC).Among them,PCC is proven to be better than the others and suitable for a large number of residential customers.Method 2 further improves Method 1’s performance with a modified multi-layer Neural Network architecture,which is constituted by fully-connected,dropout and stable improved softmax layers.It optimizes the process of supervised learning in LSTM and improves the stability and accuracy of the prediction model.The performances of both proposed methods are evaluated on a dataset of 8-week electricity consumptions from 2337 residential customers.

一种新兴的人员培训方式——浅析E-learning在人力资源管理中的效能

人力资源管理是通过科学的方法对人才结构进行有效的整合和利用,争取在人力资源上取得最大的使用效果。产生最大效益的培训是企业人力资源管理开发工作的重要组成部分,是开发人力资源的基础。如何通过培训出适应现代经济发展的人才,实现科学的人力资源管理,已经成为现代企业谋求发展的主要方向。在线学习(简称E-Learning)是一种新兴的人员培训方式,在人力资源管理中必将发挥重要作用。

Safe reinforcement learning for multi-energy management systems with known constraint functions

Reinforcement learning(RL)is a promising optimal control technique for multi-energy management systems.It does not require a model a priori-reducing the upfront and ongoing project-specific engineering effort and is capable of learning better representations of the underlying system dynamics.However,vanilla RL does not provide constraint satisfaction guarantees—resulting in various potentially unsafe interactions within its environment.In this paper,we present two novel online model-free safe RL methods,namely SafeFallback and GiveSafe,where the safety constraint formulation is decoupled from the RL formulation.These provide hard-constraint satisfaction guarantees both during training and deployment of the(near)optimal policy.This is without the need of solving a mathematical program,resulting in less computational power requirements and more flexible constraint function formulations.In a simulated multi-energy systems case study we have shown that both methods start with a significantly higher utility compared to a vanilla RL benchmark and Optlayer benchmark(94,6%and 82,8%compared to 35,5%and 77,8%)and that the proposed SafeFallback method even can outperform the vanilla RL benchmark(102,9%to 100%).We conclude that both methods are viably safety constraint handling techniques applicable beyond RL,as demonstrated with random policies while still providing hard-constraint guarantees.