基于知识点本体的个性化课程组织方法

基于本体技术和知识管理理论,提出了组织知识的5层结构和使用知识的4层结构.设计开发了知识点本体、学习者本体和学习策略本体,并基于这3个本体查找、选择合适的知识点,生成个性化课程推荐给学习者.实验结果表明:学习者使用该方法自动生成的个性化课程,能够提高自身的学习积极性,用户满意度达到84%以上.     

基于粒子群优化算法的学习资源推荐方法

针对学习者的能力、学习目标、学习时间的个别差异,提出以粒子群优化（PSO）算法为基础的学习资源推荐方法,提供每位学习者个性化的数字课程。综合概念图和知识结构相关理论构建知识点网络结构图,运用项目反应理论（IRT）分析不同学习者的学习目标和能力程度,再应用PSO算法从多样性的学习资源中挑选学习内容,形成个性化的课程推荐给学生。初始化粒子时考虑学习者的学习时间上下限,过滤掉一些不必要的粒子来提高算法效率,在确定最优解位置时,使用Sigmoid函数修正粒子更新速度,保证其在有效范围内。实验结果表明,随着迭代次数增加,所推荐的内容与学习者预定目标差异为0,挑选出的课程与学习者能力差异为0.6,整体差异为0.25,说明所使用的方法具有较好的收敛性,推荐的学习资源能够满足学习者要求。     

泛在学习理论下高等数学网络学习资源优化探讨

基于泛在学习的理论和特点,提出以学习元为基础的网络学习资源建设,避免内容重复构建,提升知识点间的动态关联,构建层次化结构,拓展知识的外延性和内涵性,设定多个模块引导学习.该方法的运用能促进高等数学课程网路学习资源优化,满足学习者无时无刻无处不在的无缝学习,有效提高教学活动的开展,解决现行教学活动中遇到的实际问题.     

基于本体的智能学习资源分配模型构建

网络学习系统的核心是学习资源的分配和管理。学习资源的分配原则是按照教学策略依据学习者特征和学习资源特征进行匹配,从存储学习资源的信息库中调出所需的学习资源内容进行学习。引入领域本体进行建模,对学习资源进行语义描述,引入本体知识,利用本体描述学习者信息和学习资源信息,建立相关本体模型。主要针对资源的组成部分的显示形式和操作进行描述,支持个性化学习产     

基于细粒度学习情感本体的学习效果评估方法——以算法设计与分析课程为例

教育目标包括认知领域目标、动作技能领域目标和情感领域目标。情感领域目标教育已受到越来越多教育者和众多领域学者的关注和研究。学习者的情感在传统教育和网络教育中都起着十分重要的作用,影响着学习者的学习主动性、积极性、创造性以及学习效果。基于多年承担本科生和硕士生的算法相关课程的教学实践,构建了细粒度学习情感本体,提出了基于细粒度学习情感本体的学习效果评估方法。细粒度学习情感本体的特点是引入了课程知识点之间的多种语义关系,构建了基于知识点的教师情感反馈行为分类。学习效果评估方法的特点是构建了基于细粒度学习情感本体中知识点关系路径的学习情感演化模型,并应用该模型来评估学习效果。     

基于网络课程本体的语义查询推理系统

针对网络课程知识点相对孤立、共享性与重用性差、学习资源查找困难、不能提供个性化服务等问题,提出使用基于本体查询推理的解决方法,设计了检索、查询和推理的功能模块和工作流程,并综合运用了Jena和SPARQL实现了相应的原型系统。通过具体的课程本体进行测试,验证了该解决方法的有效性和可行性。该系统能够将网络课程知识内容有机组织到一起,达到共享、重用的效果,可以提供个性化的学习服务,为进一步开发智能化的网络课程平台提供了条件。     

基于本体的e-Learning环境个性化服务处理方法*

为了向e-Learning环境中的学习者提供符合其个性化需求的学习服务,结合本体论具有概念和关系定义明确的特性,提出了e-Learning环境中学习者的个性化情形本体模型和相应的学习者个性化服务处理方法,该方法综合考虑了学习者的认知状态和学习偏好,进行个性化的答疑和进一步学习的内容推荐。采用该方法实现的原型系统实验表明,可使学习者的学习更有针对性,可更及时有效地消解疑惑,从而提高了学习者的学习效果和效率。     

基于动态学习者模型的个性化资源推送策略研究

随着互联网的普及,移动学习已经成为当今获取知识的主要方式,学习不受时间和地点的限制是它的最大特点。在高校注重以实践为主的编程课程中,引入移动学习非常有必要,碎片化知识点的学习可以通过个性化推送获得。基于此,笔者在充分了解个性化资源推送中常用的建模类型的基础上,通过问卷调查分析学习者的行为特征,提出了基于动态学习者模型的个性化推送方案。该方案注重以人为本,尽可能全方面考虑学习者的感受,大大提高了学习资源的个性化推送效率。     

面向学习对象课程群模型的构建

为了开发<信息安全>课程群智能辅助教学系统和满足网络环境下的多元化教学要求,提高学习效率,在研究学习对象及其相关标准的基础上,设计了一种基于集教学内容、教学设计和教学活动于一体的学习对象的课程群构建模型.首先根据岗位能力需要把课程群递归定义成课程群、岗位能力和知识点三个层次的学习对象,知识点为最小学习对象;然后结合学习对象元数据、学习设计和内容包装规范对各层次学习对象的定义内容包装;最后采用领域本体技术对课程群建模,定义领域内的概念及其相互关系与实际的课程群资源间的映射,并基于XML技术绑定.该模型中每层学习对象内部均含有描述学习活动的教学设计,这使学习对象增加了教师特性,并能同时为多个学习者分别创建学习情境、提供个别指导.     

基于知识点本体的学习资源框架设计

针对目前语义缺失,缺少可重用性、互操作性、共享性的学习资源现状,提出将知识点本体引入学习资源库功能模型的构建和学习知识的表达,模型中采用KDD技术对领域学科知识点的内在联系及层次关系进行分析和研究．并构建领域知识点本体模型,为学习资源适应个性化学习提供语义基础,实现资源的共享和重用。     

《数据结构》课程知识本体构建研究

介绍当代移动学习的特点和语义网本体技术,提出利用本体技术来适应移动学习的片段式学习方式要求,并以《数据结构》课程为例阐述课程知识本体库的具体构建过程．为其在移动学习系统中的应用打下研究基础。     

面向文本的本体学习研究概述

对本体（ontology）的研究在计算机领域变得越来越广泛，但手工构造本体是一项繁琐而辛苦的任务，还会导致知识获取瓶颈。本体学习技术是利用本体工程技术和机器学习技术等众多学科技术来实现本体的（半）自动构建。本体的学习可以面向文本、知识库、结构化数据、半结构化数据和无结构数据。本文主要介绍了面向文本的本体学习，并对其中的学习内容、学习方法、学习工具、学习过程和系统评价等关键技术进行了说明，特别介绍了学习方法中的基于统计的方法、词汇句法模式法和形式概念分析法并对其优缺点做了简单的分析。     

Ontology learning: state of the art and open issues

Ontology is one of the fundamental cornerstones of the semantic Web. The pervasive use of ontologies in information sharing and knowledge management calls for efficient and effective approaches to ontology development. Ontology learning, which seeks to discover ontological knowledge from various forms of data automatically or semi-automatically, can overcome the bottleneck of ontology acquisition in ontology development. Despite the significant progress in ontology learning research over the past decade, there remain a number of open problems in this field. This paper provides a comprehensive review and discussion of major issues, challenges, and opportunities in ontology learning. We propose a new learning-oriented model for ontology development and a framework for ontology learning. Moreover, we identify and discuss important dimensions for classifying ontology learning approaches and techniques. In light of the impact of domain on choosing ontology learning approaches, we summarize domain characteristics that can facilitate future ontology learning effort. The paper offers a road map and a variety of insights about this fast-growing field.     

A language learning support system using course-centered ontology and its evaluation

This paper presents a course-centered ontology for assisting learning support systems to embody the relations among knowledge points and also among the learning materials for those knowledge points. An “individual-class-individual” ontology design (first an individual-class design, then an innovative design about relations among bottom individuals), was applied to the construction of a course-centered ontology for an existing Japanese grammar course. Furthermore, a customizable language learning support system was built to manipulate the course-centered ontology to provide an interface for the learning objects arrangement which displays the visual representation of knowledge points and their relations. The intention underlying the development of the system is to encourage instructors to orient their teaching materials to specific knowledge points and even directly to relations between knowledge points. With these orientations, the learning support system is able to provide an environment in which learners can readily distinguish between related knowledge points. Finally, based on the result of a preliminary evaluation, a study to explore the impact of learning styles and learning habits on learning performance was conducted to further evaluate our ontology-based learning support system. The results of the study suggest three main points: (a) the experimental students who learned with our system achieved significantly better learning achievement than those who just did self-study with textbooks after studying the same target contents for 60 mins; (b) the learning achievement of experimental group was not related to either their learning style in Sequential/Global dimension or their habit of “learning from comparison”; (c) in terms of the learning perception of experimental group, compared to “Sequential learners”, most “Global learners” had a stronger feeling that the comparison function is useful in improving their learning performance, and the learners who don't habitually “learning from comparison” were more likely to suffer from lack of the attention and feel more pressure than those who do habitually “learning from comparison”.     

Web 3.0 – Based personalisation of learning objects in virtual learning environments

The paper aims at research on Web 3.0 – based personalisation of learning objects (LOs) while learning in virtual learning environments. Learning personalisation is analysed in terms of suitability of LOs and VLEs to particular learning styles. The novel sets portrait analysing interconnections between students’ learning styles, their preferred learning activities, relevant teaching/learning methods, and LOs types is presented in more detail. The well-known standardised vocabularies of teaching/learning methods and LOs types were used to establish these interconnections. The sets portrait of these interconnections is followed by the appropriate ontology. The ontology is considered as an appropriate tool to create learners’ personalised learning environments consisting of LOs, suitable teaching/learning methods and activities according to their preferred learning styles. The ontology should help the learner to find suitable LOs according to preferred learning methods/activities, and vice versa, and thus to personalise learning. The presented Web 3.0 – based approaches are the typical cases of information and communication technologies (ICT’s) application for development of human capital.     

A methodology for constructing of philosophy ontology based on philosophical texts

An ontology is a crucial factor for the success of the Semantic Web and other knowledge-based systems in terms of share and reuse of domain knowledge. However, there are a few concrete ontologies within actual knowledge domains including learning domains. In this paper, we develop an ontology which is an explicit formal specification of concepts and semantic relations among them in philosophy. We call it a philosophy ontology. Our philosophy is a formal specification of philosophical knowledge including knowledge of contents of classical texts of philosophy. We propose a methodology, which consists of detailed guidelines and templates, for constructing text-based ontology. Our methodology consists of 3 major steps and 14 minor steps. To implement the philosophy ontology, we develop an ontology management system based on Topic Maps. Our system includes a semi-automatic translator for creating Topic Map documents from the output of conceptualization steps and other tools to construct, store, retrieve ontologies based on Topic Maps. Our methodology and tools can be applied to other learning domain ontologies, such as history, literature, arts, and music.     

Semantic Ontology Mapping for Interoperability of Learning Resource Systems using a rule-based reasoning approach

There is an increasing demand for sharing learning resources between existing learning resource systems to support reusability, exchangeability, and adaptability. The learning resources need to be annotated with ontologies into learning objects that use different metadata standards. These ontologies have introduced the problems of semantic and structural heterogeneity. This research proposes a Semantic Ontology Mapping for Interoperability of Learning Resource Systems. To enable semantic ontology mapping, this research proposes conflict detection and resolution techniques for both semantic and structural conflicts. The Semantic Bridge Ontology has been proposed as a core component for generating mapping rules to reconcile terms defined in local ontologies into terms defined in the target common ontology. This work defines the reasoning rules to classify related learning objects to enhance the powerful deductive reasoning capabilities of the system. As a consequence, ontology-based learning object metadata are generated and used by the semantic query engine to facilitate user queries of learning objects across heterogeneous learning resource systems.     

Ontology-based curriculum content sequencing system with semantic rules

Curriculum content sequencing involves managing a learning route to help users achieve learning goals. A conventional learning route consists of ordered content and is primarily based on a single course material. In an e-learning system, amount of similar course contents are available. These contents are expected to mutually substitute for one another in creating flexible learning routes. Owing to inconsistency in materials editing and cataloging, composing contents based on multiple sources leads to sequencing complexity. Most significantly, most e-learning systems lack a sequencing mechanism for dominating content composition. This study utilizes a knowledge-intensive approach to create a general sequencing knowledge base. This approach includes two components: (1) ontology is used to represent abstract views of content sequencing and course materials and (2) added semantic rules are used to represent relationships between individuals. Following knowledge base creation, both practical curriculum sequences and course materials can be inserted as factual knowledge. A reliable knowledge base can be established using inference power. An example involving mathematics course in elementary school education is designed using Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL). Experimental lessons demonstrate that semantic rules in conjunction with ontologies not only solve sequencing problems but also achieve a durable knowledge base and a reliable system.     

基于语义模型的信息检索机制研究

提出了一种基于本体语义模型的信息检索方法。该方法充分利用领域本体提供的概念之间的语义相关性，从语义模型扩展、概念相似度、相关度计算，并以用户反馈等角度探讨了基于语义模型的自动推理方法在信息检索中的应用，文章介绍了系统实现框架。该系统将应用在学习资源的智能检索中。     

Content annotation for the semantic web: an automatic web-based approach

Semantic Annotation is required to add machine-readable content to natural language text. A global initiative such as the Semantic Web directly depends on the annotation of massive amounts of textual Web resources. However, considering the amount of those resources, a manual semantic annotation of their contents is neither feasible nor scalable. In this paper we introduce a methodology to partially annotate textual content of Web resources in an automatic and unsupervised way. It uses several well-established learning techniques and heuristics to discover relevant entities in text and to associate them to classes of an input ontology by means of linguistic patterns. It also relies on the Web information distribution to assess the degree of semantic co-relation between entities and classes of the input domain ontology. Special efforts have been put in minimizing the amount of Web accesses required to evaluate entities in order to ensure the scalability of the approach. A manual evaluation has been carried out to test the methodology for several domains showing promising results.