Knowledge management perspective on e-learning effectiveness

The synergies, functional effectiveness and integration of KM within an e-learning environment have attracted little interest for serious research, despite the overarching importance of knowledge acquisition by students for fostering their innovation and creativity. Learners often fail to reach their desired learning objects due to the failure of indexing methods to provide them with a ubiquitous learning grid. The aim of this paper is to discuss how knowledge management can be used effectively in e-learning, and how it can provide a learning grid to enable the learner to identify the right learning objects in an environment which is based on the learner’s context and personal preferences.     

The design and implementation of a competency-based intelligent mobile learning system

Mobile learning provides a ubiquitous learning context for the learners to select appropriate learning paths and learning objects. Adaptive learning methods and correct learning path planning can help to achieve the goal of learning anytime and anywhere. Moreover, the display ability of mobile learning devices has become a key factor affecting the interest and acquisition time of learners. Achieving the desired functionality is currently an important topic in the field of mobile learning. This paper uses competency-based learning as the basis to evaluate the knowledge deficiency that the learner must overcome. We then use carrier selection, fuzzy interpolation computation, and ant-genetic algorithm techniques to select the appropriate learning paths and objects. Finally, we use NFC’s point-to-point technology to transfer the learning content in the learning device to a larger screen with NFC capability in the user’s environment to display the same content, thus providing a complete learning system.     

Improving learner quality of experience by content adaptation based on network conditions

Apart from user characteristics, properties of the network over which the content is delivered and device on which the content is displayed affect end-user perceived quality. This paper presents a learner quality of experience (QoE) model that apart from the user-related content adaptation, considers delivery performance-based content personalisation in order to improve user experience when interacting with an online learning system.A comparison-based study on the benefit of using the proposed learner QoE model in adaptive and personalized education was conducted involving the original AHA! and QoEAHA – a version of AHA! enhanced with the learner QoE model. Testing results demonstrate significant benefits in terms of learning achievement, learning performance, learner navigation and user QoE in favour of the learner QoE model-enhanced solution.     

Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system

Advances in wireless networking, mobile broadband Internet access technology as well as the rapid development of ubiquitous computing means e-learning is no longer limited to certain settings. A ubiquitous learning (u-learning) system must however not only provide the learner with learning resources at any time and any place. However, it must also actively provide the learner with the appropriate learning assistance for their context to help him or her complete their e-learning activity. In the traditional e-learning environment, the lack of immediate learning assistance, the limitations of the screen interface or inconvenient operation means the learner is unable to receive learning resources in a timely manner and incorporate them based on the actual context into the learner’s learning activities. The result is impaired learning efficiency. Though developments in technology have overcome the constraints on learning space, an inability to appropriately exploit the technology may make it an obstacle to learning instead. When integrating the relevant information technology to develop a u-learning environment, it is therefore necessary to consider the personalization requirements of the learner to ensure that the technology achieves its intended result. This study therefore sought to apply context aware technology and recommendation algorithms to develop a u-learning system to help lifelong learning learners realize personalized learning goals in a context aware manner and improve the learner’s learning effectiveness.     

Articulation of information requirements for personalised knowledge construction

The quality of information provision influences considerably knowledge construction driven by individual users’ needs. In the design of information systems for e-learning, personal information requirements should be incorporated to determine a selection of suitable learning content, instructive sequencing for learning content, and effective presentation of learning content. This is considered as an important part of instructional design for a personalised information package. The current research reveals that there is a lack of means by which individual users’ information requirements can be effectively incorporated to support personal knowledge construction. This paper presents a method which enables an articulation of users’ requirements based on the rooted learning theories and requirements engineering paradigms. The user’s information requirements can be systematically encapsulated in a user profile (i.e. user requirements space), and further transformed onto instructional design specifications (i.e. information space). These two spaces allow the discovering of information requirements patterns for self-maintaining and self-adapting personalisation that enhance experience in the knowledge construction process.     

面向服务组合的复杂上下文本体模型

为了有效解决泛在网络环境下Web服务质量上下文描述问题,在OWL_S基础上提出了面向服务组合的复杂上下文本体模型-OWL_S_QoS＋,包括用户上下文本体和服务上下文本体。用户上下文本体描述用户的环境状态、拥有的资源信息、用户需求及用户群组等对服务或组合服务运行产生影响的一系列属性;服务上下文本体在原有OWL_S定义的基础上,增加了服务对客户端与服务端的运行环境需求、不确定性QoS描述方法。通过模型比较验证了OWL_S_QoS＋具有较强的描述能力。     

Self-regulated learning strategies and software training

We investigated one aspect of the growing trend towards self-management within organizations by examining the learning processes that individuals use when learning computer software alone in a web-based training environment, and found that individuals learn in different ways. Software training in any context requires individuals to apply self-regulated learning strategies to achieve their learning objectives. Self-regulated learning strategies are the processes that they use to manage their own learning. As self-guided training gains popularity, self-regulated learning strategies become increasingly important, because the learner typically has more control of their training. We found that self-regulated learning strategies led to better outcomes in our study of managers and professionals learning to use an ERP system. We also gained insights into the most popular learning strategies. This work enhanced our understanding of user processes when learning computer software. It should assist organizations in maximizing the return from their investment in training employees and managing knowledge.     

Case-based analysis in user requirements modelling for knowledge construction

ContextLearning can be regarded as knowledge construction in which prior knowledge and experience serve as basis for the learners to expand their knowledge base. Such a process of knowledge construction has to take place continuously in order to enhance the learners’ competence in a competitive working environment. As the information consumers, the individual users demand personalised information provision which meets their own specific purposes, goals, and expectations.ObjectivesThe current methods in requirements engineering are capable of modelling the common user’s behaviour in the domain of knowledge construction. The users’ requirements can be represented as a case in the defined structure which can be reasoned to enable the requirements analysis. Such analysis needs to be enhanced so that personalised information provision can be tackled and modelled. However, there is a lack of suitable modelling methods to achieve this end. This paper presents a new ontological method for capturing individual user’s requirements and transforming the requirements onto personalised information provision specifications. Hence the right information can be provided to the right user for the right purpose.MethodAn experiment was conducted based on the qualitative method. A medium size of group of users participated to validate the method and its techniques, i.e. articulates, maps, configures, and learning content. The results were used as the feedback for the improvement.ResultThe research work has produced an ontology model with a set of techniques which support the functions for profiling user’s requirements, reasoning requirements patterns, generating workflow from norms, and formulating information provision specifications.ConclusionThe current requirements engineering approaches provide the methodical capability for developing solutions. Our research outcome, i.e. the ontology model with the techniques, can further enhance the RE approaches for modelling the individual user’s needs and discovering the user’s requirements.     

A QoS model for collaboration through Distributed Virtual Environments

Today’s virtual environments are expected to be distributed to allow collaboration for common purposes. However, they must ensure a high level of Quality of Service (QoS) to the user, especially in an open context with unknown, a priori, man-in-the-loop event occurrence. This paper presents our Distributed Virtual Environment Collaboration Model (DVECOM) and its implementation which aims to provide an end-user QoS support for distributed virtual reality applications. This model aims to reconcile openness and real-time requirements for collaborative relationships using virtual environments. Real-time requirements ensure the logical synchronization between the displayed scenes of the same virtual world on a set of distributed machines. The DVECOM model is based partially upon COREMO concepts and further work done in the context of the Amusement European Esprit project.DVECOM integrates QoS provision and management. The major idea is to guarantee the consistency of scene rendering and the synchronization of the display from the user point of view. The other idea is to auto-adapt rendering in accordance with the retained strategy, ensuring best effort and least suffering virtual world rendering. Representation degradation is driven by the users’ choices and is assisted by the system. The receiver-side protocol is based upon end-user preferences, physical level capability information, as well as pertinence of notification to each client (contextual end-user information). The model should be able to guarantee logical consistency and synchronization of virtual world distributed displays, with the least rendering degradation possible. When available, such guarantees would make it possible to use DVE out of a closed, oversized, very restricted context for industrial collaborative applications with an expected QoS.     

基于关联规则挖掘与用户情景感知的Web服务QoS预测

作为Web服务的非功能性属性,Qo S在服务选择与服务组合中扮演着重要角色。由于一些Qo S属性值会随着用户情景属性的变化而动态变化,因此在做服务选择或服务组合之前先对Web服务的Qo S做预测是非常必要的。该文提出运用数据挖掘技术挖掘服务组合执行日志,研究服务Qo S和用户情景间的关联关系,根据得到的关联规则对不同用户做个性化服务预测。实验结果表明我们的方法非常有研究价值。     

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。     

Towards a semantic infrastructure for context-aware e-learning

Architectural supports, e.g., user context processing and learning content management are essential for facilitating the development and proliferation of context-aware e-learning services. In this paper, we propose a context-aware e-learning infrastructure called Semantic Learning Space. It leverages the Semantic Web technologies to support semantic knowledge representation, systematic context management, interoperable content integration, expressive knowledge query, and adaptive content recommendation. The functionality encapsulated in the infrastructure handles the common, time-consuming and low-level details in learning context processing and content management. The architectural design and enabling technologies are described in detail. Finally, the prototype implementation and preliminary experimental results are presented.     

一种基于模糊理论的个性化网络学习系统

在信息社会中,学习已经成为人们日常生活中很重要的组成部分。网络学习是一种集计算机网络技术、卫星通信技术和多媒体技术于一体的学习方式,它对人们的终身学习起到非常重要的作用。提出了一种基于模糊集理论的个性化网络学习系统,利用模糊集理论知识构建和描述学习资源数据库模型和学习者数据库模型。这种系统既能形成描述网络课程知识的模糊结构图,又能针对不同的学习者形成学习者的模糊结构子图,并能根据学习者的学习进度和能力水平,提供不同的学习内容和导航策略,从而满足个性化网络学习的需求。     

Automatic detection of learning styles for an e-learning system

A desirable characteristic for an e-learning system is to provide the learner the most appropriate information based on his requirements and preferences. This can be achieved by capturing and utilizing the learner model. Learner models can be extracted based on personality factors like learning styles, behavioral factors like user’s browsing history and knowledge factors like user’s prior knowledge. In this paper, we address the problem of extracting the learner model based on Felder–Silverman learning style model. The target learners in this problem are the ones studying basic science. Using NBTree classification algorithm in conjunction with Binary Relevance classifier, the learners are classified based on their interests. Then, learners’ learning styles are detected using these classification results. Experimental results are also conducted to evaluate the performance of the proposed automated learner modeling approach. The results show that the match ratio between the obtained learner’s learning style using the proposed learner model and those obtained by the questionnaires traditionally used for learning style assessment is consistent for most of the dimensions of Felder–Silverman learning style.     

Context-aware QoS prediction for web service recommendation and selection

QoS prediction is one of the key problems in Web service recommendation and selection. The context information is a dominant factor affecting QoS, but is ignored by most of existing works. In this paper, we employ the context information, from both the user side and service side, to achieve superior QoS prediction accuracy. We propose two novel prediction models, which are capable of using the context information of users and services respectively. In the user side, we use the geographical information as the user context, and identify similar neighbors for each user based on the similarity of their context. We study the mapping relationship between the similarity value and the geographical distance. In the service side, we use the affiliation information as the service context, including the company affiliation and country affiliation. In the two models, the prediction value is learned by the QoS records of a user (or a service) and the neighbors. Also, we propose an ensemble model to combine the results of the two models. We conduct comprehensive experiments in two real-world datasets, and the experimental results demonstrate the effectiveness of our models.     

An expert system manipulating knowledge to help human learners into virtual environment

This research is situated within the context of the creation of human learning environments using virtual reality. We propose the integration of a generic and adaptable intelligent tutoring system (Pegase). The aim is to instruct the learner, and to assist the instructor. The multi-agent system emits a set of knowledge (actions carried out by the learner, knowledge of the field, etc.) used by an artificial intelligence to make pedagogical decisions. Our study focuses on the representation of knowledge about the environment, and on the adaptable pedagogical agent providing instructive assistance.     

QoS驱动的服务动态替换方法

在基于SOA(Service-Oriented Architecture)的软件中,为了适应用户对软件QoS(Quality of Service)值要求的改变或是组成软件的Web服务本身QoS值的不稳定,需要对软件进行重配置,使其QoS值达到用户要求。提出一个QoS驱动的动态替换算法,通过对软件的QoS的监测结果或用户所提出的QoS需求,确定动态替换目标。通过目标的替换,使配置后的软件满足用户需求。理论分析和实验结果都说明了算法的可行性和有效性。     

Mobile PointMe-based spatial haptic interaction with annotated media for learning purposes

In this paper, we propose a framework to facilitate handheld device-based PointMe interaction with annotated media content, where the user points his/her handheld device to the media presentation screen in order to access services/information from the annotated media. Each annotation is encoded into customized learning object metadata (LOM) format that provides access point for available information about a specific media on the screen. By incorporating a fusion of infra-red (IR) motion points and accelerometer data of the handheld device, the system determines the learner’s PointMe location on the media screen. By using the pointed location spatial geometrical query is performed over the displayed annotated media in order to deliver learner’s level-specific interactive learning content to the user’s handheld device. The level customization is achieved by incorporating learner’s age, progress, etc., parameters in the media content delivery process. We design intuitive learning scenarios by leveraging the PointMe interaction scheme, where learners are challenged to answer quiz questions in learning-based games. This real-world interaction technique with the annotated media and seamless virtual learning information acquisition process makes the system transparent to young learners and encourage them to engage and concentrate on their learning activities. We develop a prototype and perform experiments in a technology-augmented learning space to show the suitability of the proposed approach.