基于Web2.0和本体检索的个性化E-learning系统研究与开发

针对当前E-learning系统中存在着堆砌教学资料和学习内容单一、个性化不足等问题,设计一个基于Web2.0和本体检索技术的个性化E-learning系统,通过应用Ajax和RSS聚合技术以及Ontology本体技术,使得该系统能根据学习者的知识结构、学习目标、学习风格、偏好等特征信息提供适应学习者的教学方法和学习资源,营造个性化的网络学习环境。实验结果表明该系统能有效促进学生网络学习的效率,满足学生个性化学习的需求。     

基于Web挖掘的个性化网络教学系统的设计与实现

设计了一种基于Web挖掘的个性化网络教学系统，该系统结合Web使用挖掘、web内容挖掘和Web结构挖掘的挖掘结果为学生提供个性化的推荐服务，即使在使用数据比较少，或教学内容变化比较频繁的情况下，也能为学生提供高质量的个性化推荐服务。     

基于本体和Web服务的适应性e-Learning系统

本文将本体和Web服务技术引入到e-Learning应用中,构建了一个开放的适应性e-Learning系统结构, 利用本体来描述学习资源的语义,通过web服务支持个性化学习和系统间的资源共享。该方法把建立良好个性 化功能的适应性教育系统和分布的学习资源网络环境进行了有效的结合。     

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     

面向个性化学习的慕课资源推荐系统开发

近年来,在线学习得到了大规模普及,互联网上已发布了海量的慕课学习资源.针对广大学习者进行在线慕课学习时面临的"信息迷航"和"信息过载"等问题,以混合推荐算法为基础,通过多角度挖掘用户的个性化信息,设计并实现了一个面向个性化学习的慕课资源推荐系统,以此为广大学习者提供自主学习的辅助支持.该系统能充分挖掘学习者的显式和隐式偏好,为其推荐满意的慕课资源,具有良好的应用价值.     

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。     

基于Web挖掘的个性化网络学习系统设计

设计了一种基于Web挖掘的个性化网络学习系统,该系统给出了Web内容挖掘、Web使用挖掘和Web结构挖掘的结果,并结合其推荐结果为学习者提供个性化的服务。并给出个性化推荐算法。     

基于领域本体的学习资源语义检索模型*

为解决e-learning领域内学习资源的有效检索,提出了一个基于领域本体的e-learning学习资源语义检索模型。用Protégé构建了教育技术领域本体,通过对检索方法的研究分析和算法实现,开发了基于本体语义检索原型系统;并由对比全文检索和基于本体的语义检索两种方法的查准率,证明了基于本体的语义检索方式,在一定程度上能解决目前传统检索中存在的不足。     

数据挖掘在个性化网络学习中的应用

本文分析了传统网络学习系统的现状及其弊端,针对这些问题提出：使用Web数据挖掘技术,对学习者网络学习行为进行分析,在此基础上对学习者网络学习行为进行预测,从而为学习者提供个性化的学习建议,提高学习者的学习效率;同时根据对学习者网络学习行为的数据挖掘结果,改进网站设计。     

基于网络学习行为的学习者本体构建

随着多媒体技术的发展,教育技术进入到大数据时代,数据挖掘等学习技术对教育领域的各种原理研究十分重要,然而怎样把数据挖掘这种技术有效的应用到教育教学中是问题的关键所在。其中,本体构建就是一个重要的方法,该方法将会对研究行为规律、分析学生的行为产生重大的影响。本文在已有本体构建方法基础上,通过总结本体构建过程,设计网络学习空间中学习者本体构建方法并成功实施,最后对学习者本体进行一致性分析。这将为学习者提供精准的教学支持服务,实现个性化学习。同时,学习者本体的成功构建也将为智慧教育提供重要帮助。     

Data mining for providing a personalized learning path in creativity: An application of decision trees

Customizing a learning environment to optimize personal learning has recently become a popular trend in e-learning. Because creativity has become an essential skill in the current e-learning epoch, this study aims to develop a personalized creativity learning system (PCLS) that is based on the data mining technique of decision trees to provide personalized learning paths for optimizing the performance of creativity. The PCLS includes a series of creativity tasks as well as a questionnaire regarding several key variables. Ninety-two college students were included in this study to examine the effectiveness of the PCLS. The experimental results show that, when the learning path suggested by a hybrid decision tree is employed, the learners have a 90% probability of obtaining an above-average creativity score, which suggests that the employed data mining technique can be a good vehicle for providing adaptive learning that is related to creativity. Moreover, the findings in this study shed light on what components should be accounted for when designing a personalized creativity learning system as well as how to integrate personalized learning and game-based learning into a creative learning program to maximize learner motivation and learning effects.     

基于多维度推荐的智慧在线学习系统

针对目前在线学习系统中存在的不足,探讨如何有效地运用数据挖掘技术建立智慧的在线学习系统.从大量的用户数据中挖掘出关联关系,用以提供全面个性化、定制化的学习过程序列.利用数据挖掘着重发现用户与课程之间、课程与课程之间、用户与用户间的关联,形成一个多维度的网络.利用多维度推荐为用户推荐有价值的课程.实验表明多维度推荐具有良好的准确性和良好的用户体验.     

基于移动Agent的个性化数字学习模型研究

在开放、动态的学习资源网络环境中,个性化服务支持对在线学习者尤其重要.讲述了在基于web服务技术的分布式学习环境中如何构造一个分布式的个性化数宁学习环境.在建立服务代理的功能模型的基础上,构造了一种基于多移动Agent的个性化数字学习(E-Learning)框架模型,阐述了其工作流程并进行了结构分析;引入Petri网模型,设计了基于移动Agent的联邦组建与动态服务合成算法;说明了系统实现的技术手段与方法.从对系统模型雏形的应用与性能监测来看,系统模型实现切实可行且运行性能良好.     

A model for a web-based learning system

The dissemination of knowledge is currently being improved by e-learning, which consists of a combination of teaching methodologies and computer-based tools. Recently e-learning environments have started to exploit web technology to provide a simple, flexible, distributed and open platform. In this paper we propose a model for an e-learning system, aiming at sharing both course contents and teaching materials, in order to provide students with a single and uniform set of concepts to be learned, and promoting active learning by allowing the construction of courses which are personalized in terms of both contents and teaching materials, selected according to each student’s needs and capabilities. A first, open-source prototype based on the proposed model has been implemented to validate the model.     

Data mining in course management systems: Moodle case study and tutorial

Educational data mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. This work is a survey of the specific application of data mining in learning management systems and a case study tutorial with the Moodle system. Our objective is to introduce it both theoretically and practically to all users interested in this new research area, and in particular to online instructors and e-learning administrators. We describe the full process for mining e-learning data step by step as well as how to apply the main data mining techniques used, such as statistics, visualization, classification, clustering and association rule mining of Moodle data. We have used free data mining tools so that any user can immediately begin to apply data mining without having to purchase a commercial tool or program a specific personalized tool.     

Personalized web-based tutoring system based on fuzzy item response theory

With the rapid growth of computer and Internet technologies, e-learning has become a major trend in the computer assisted teaching and learning field. Previously, many researchers put effort into e-learning systems with personalized learning mechanism to aid on-line learning. However, most systems focus on using learner’s behaviors, interests, and habits to provide personalized e-learning services. These systems commonly neglect to consider if learner ability and the difficulty level of the recommended courseware are matched to each other. Frequently, unsuitable courseware causes learner’s cognitive overload or disorientation during learning. To promote learning effectiveness, our previous study proposed a personalized e-learning system based on Item response theory (PEL-IRT), which can consider both course material difficulty and learner ability evaluated by learner’s crisp feedback responses (i.e. completely understanding or not understanding answer) to provide personalized learning paths for individual learners. The PEL-IRT cannot estimate learner ability for personalized learning services according to learner’s non-crisp responses (i.e. uncertain/fuzzy responses). The main problem is that learner’s response is not usually belonging to completely understanding or not understanding case for the content of learned courseware. Therefore, this study developed a personalized intelligent tutoring system based on the proposed fuzzy item response theory (FIRT), which could be capable of recommending courseware with suitable difficulty levels for learners according to learner’s uncertain/fuzzy feedback responses. The proposed FIRT can correctly estimate learner ability via the fuzzy inference mechanism and revise estimating function of learner ability while the learner responds to the difficulty level and comprehension percentage for the learned courseware. Moreover, a courseware modeling process developed in this study is based on a statistical technique to establish the difficulty parameters of courseware for the proposed personalized intelligent tutoring system. Experiment results indicate that applying the proposed FIRT to web-based learning can provide better learning services for individual learners than our previous study, thus helping learners to learn more effectively.     

基于Web挖掘的个性化远程教育系统研究

针对现在网络远程教育存在的系统教育模式单一问题,通过介绍Web挖掘在远程教育系统中的应用,指出了Web挖掘的基本过程和关键技术,提出了一种基于Web挖掘的个性化远程教育服务系统模型,重点论述了应用Web挖掘进行个性化远程教育服务系统的体系结构及其个性化引擎实现.实践证明基于Web挖掘技术在远程学习系统中的应用提高了学习系统的个性化服务水平,为系统的决策分析提供了智能的辅助手段.     

E-Learning personalization based on hybrid recommendation strategy and learning style identification

Personalized learning occurs when e-learning systems make deliberate efforts to design educational experiences that fit the needs, goals, talents, and interests of their learners. Researchers had recently begun to investigate various techniques to help teachers improve e-learning systems. In this paper, we describe a recommendation module of a programming tutoring system - Protus, which can automatically adapt to the interests and knowledge levels of learners. This system recognizes different patterns of learning style and learners’ habits through testing the learning styles of learners and mining their server logs. Firstly, it processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the learners through mining the frequent sequences by the AprioriAll algorithm. Finally, this system completes personalized recommendation of the learning content according to the ratings of these frequent sequences, provided by the Protus system. Some experiments were carried out with two real groups of learners: the experimental and the control group. Learners of the control group learned in a normal way and did not receive any recommendation or guidance through the course, while the students of the experimental group were required to use the Protus system. The results show suitability of using this recommendation model, in order to suggest online learning activities to learners based on their learning style, knowledge and preferences.     

PC<Superscript>2</Superscript>PSO: personalized e-course composition based on Particle Swarm Optimization

This paper proposes a Personalized e-Course Composition approach based on Particle Swarm Optimization (PSO) algorithm, called PC²PSO, to compose appropriate e-learning materials into personalized e-courses for individual learners. The PC²PSO composes a personalized e-course according to (1) whether or not the covered learning concepts of the personalized e-course meets the expected learning target of a learner, (2) whether or not the difficulty of the e-learning material matches a learner’s ability, (3) the limitation of learning time for individual learners, and (4) the balance of the weight of learning concepts that are covered in a personalized e-course. PC²PSO can provide a truly personalized learning environment when used in conjunction with an Intelligent Tutoring System (ITS). When an e-course authoring tool is based on the proposed approach, the PC²PSO can facilitate instructors in selecting appropriate e-learning materials from a mass of candidate e-learning materials, and then saves time and effort in the e-course editing process.