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1.
未解决当前的远程教育系统存在形式单一和被动教学等问题,该文提出了一个基于学习者个性因素的多Agent学习系统模型。该模型结合智能代理技术,通过分析学习者个性因素,给出了个体Agent能力描述语言,提出了新的个性化分组策略和学习任务分配策略,采用补偿机制鼓励agent合作,结合状态空间搜索理论使M AS系统具有更强的解题能力,并可满足学习者主动学习的要求,还能在一定程度上节约系统的通讯。  相似文献   

2.
多目标的学习者模型研究   总被引:1,自引:0,他引:1  
建立学习者模型几乎是所有ITS系统的普遍任务,当前的软件系统趋向于转入分布式和多代理系统,学习者模型转向零散的、由各种软件代理在特定环境下产生的模型。在“个性化课件生成子系统”中运用了这一思想,建立了基于多目标的、运行于分布式与多代理系统之上的学习者模型,该文将主要介绍这一系统的基本构成与运行结构、系统中目标的定义与组织结构,并对目标的解释、目标的生存周期等问题做了进一步的探讨。  相似文献   

3.
文章将智能代理技术引入远程教学系统,设计了一个智能化的远程教学模型,给出了系统的整体设计,描述了利用多代理技术实现的教学代理的基本结构和相互间的通信机制。该模型建立了个性化和协作式学习环境,实现了远程教学系统的自适应性和智能性。  相似文献   

4.
基于多Agent的网络学习智能推荐模型   总被引:1,自引:0,他引:1  
针对网络学习者面临海量信息选择的困扰,提出了一个基于多Agent的网络学习智能推荐模型.运用界面Agent采实现与学习者的交互,利用基于知识推荐的Agent提供与学习者兴趣相关的推荐,以及基于相似学习者推荐的Agent向特定学习者推荐新的知识,并对模型中推荐的相似度算法进行了阐述.通过多Agent技术的运用,较好的解决了网络学习推荐的智能化,个性化以及灵活性的问题,使网络学习者能在一种交互式的学习环境中得到更人性化的学习推荐服务.  相似文献   

5.
以学生为中心的个性化、交互协作式教学是远程教学的本质特征。在分析远程教育的现状基础上,论文提出了基于多代理技术的个性化远程教育系统。该系统集www技术和Agent技术与一体,通过智能教学Agent跟踪学生学习情况,监视学习交互进程,收集并分析学生在教学各环节的信息,从而为学生提供个性化的学习建议指导。与传统的远程教育系统相比,该系统更能充分体现远程教育个性化教学特点,真正做到因材施教和提高教学质量。  相似文献   

6.
冷德宏  葛亮  顾宁 《计算机工程》2004,30(16):113-115,138
移动设备(如手机)由于屏幕小、带宽有限等因素一直在CSCL领域难有作为。该文利用代理的智能、协作等特性,结合一个已有的CSCL应用平台,设计了一种基于多代理系统的CSCL中间什模型。该模型能够根据用户移动设备的显示性能,个性化处理已有的HTML文档,基本满足学习者对课件的访问,并协助移动用户使用CSCL应用平台,与其它协同学习者交流与合作。  相似文献   

7.
针对目前资源学习系统缺乏个性化导致小学英语学习者的资源选择迷航问题,构建以个性化资源组织为核心的学习系统。通过纪录用户信息和个性化学习行为,建立小学英语学习者信息模型;以知识点标注的方式描述英语学习资源,建立学习资源库;运用学习偏好算法和学习水平算法计算学习者偏好,采用新型智能推荐技术,向用户推荐个性化的学习资源。通过原型系统运行实例,其结果验证了个性化学习和智能推荐的有效性。  相似文献   

8.
一种基于模糊理论的个性化网络学习系统   总被引:2,自引:0,他引:2  
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。  相似文献   

9.
肖建琼  冯庆煜 《计算机应用》2008,28(5):1347-1349
以认知学习理论为依据,运用贝叶斯网络建立学习者模型,提出了一种学习内容自适应呈现算法。学习内容的呈现适合学习者认知发展水平及个性特征,实现了一种智能化、个性化网络学习的自适应系统,为学习者提供一种更优化的学习途径。  相似文献   

10.
该文章介绍了一个多agent的个性化学习路径推荐系统,该系统通过前测来掌握学习者的知识水平,再采用遗传算法来生成最佳学习路径,推荐给学习者。该系统考虑了学习者的水平和推荐课件难度水平的匹配,以及课件之间的相关性以保证学习概念的连续性。对比传统的自由浏览学习模式,这个系统有效地提高了学习者的学习效率。  相似文献   

11.
In order to automatically select learning resources in an Internet environment according to the background knowledge and the learning objectives of the learners,a platform based on integrated modeling is developed.The platform constitutes three levels and four views.The three levels are database level,knowledge analysis level and modeling level.The four views are user modeling,learning resource modeling,knowledge points modeling and learning process modeling.The process model is used to connect the learners and the learning resources.The workflow optimization method is used to optimize the learning resource selection according to the objectives and background knowledge level of the learner,at the same time,a learning plan is given.Finally,a web-based prototype system is developed by java.  相似文献   

12.
Adaptive Educational Hypermedia Systems aim to increase the functionality of hypermedia by making it personalised to individual learners. The adaptive dimension of these systems mainly supports knowledge communication between the system and the learner by adapting the content or the appearance of hypermedia to the knowledge level, goals and other characteristics of each learner. The main objectives are to protect learners from cognitive overload and disorientation by supporting them to find the most relevant content and path in the hyperspace. In the approach presented in this paper, learners' knowledge level and individual traits are used as valuable information to represent learners' current state and personalise the educational system accordingly, in order to facilitate learners to achieve their personal learning goals and objectives. Learners' knowledge level is approached through a qualitative model of the level of performance that learners exhibit with respect to the concepts they study and is used to adapt the lesson contents and the navigation support. Learners' individual traits and especially their learning style represent the way learners perceive and process information, and are exploited to adapt the presentation of the educational material of a lesson. The proposed approach has been implemented through various adaptation technologies and incorporated into a prototype hypermedia system. Finally, a pilot study has been conducted to investigate system's educational effectiveness.  相似文献   

13.
To evaluate how top-down and bottom-up processes contribute to learning from animated displays, we conducted four experiments that varied either in the design of animations or the prior knowledge of the learners. Experiments 1–3 examined whether adding interactivity and signaling to an animation benefits learners in developing a mental model of a mechanical system. Although learners utilized interactive controls and signaling devices, their comprehension of the system was no better than that of learners who saw animations without these design features. Furthermore, the majority of participants developed a mental model of the system that was incorrect and inconsistent with information displayed in the animation. Experiment 4 tested effects of domain knowledge and found, surprisingly, that even some learners with high domain knowledge initially constructed the incorrect mental model. After multiple exposures to the materials, the high knowledge learners revised their mental models to the correct one, while the low-knowledge learners maintained their erroneous models. These results suggest that learning from animations involves a complex interplay between top-down and bottom-up processes and that more emphasis should be placed on how prior knowledge is applied to interpreting animations.  相似文献   

14.
This work proposes an intelligent learning diagnosis system that supports a Web-based thematic learning model, which aims to cultivate learners’ ability of knowledge integration by giving the learners the opportunities to select the learning topics that they are interested, and gain knowledge on the specific topics by surfing on the Internet to search related learning courseware and discussing what they have learned with their colleagues. Based on the log files that record the learners’ past online learning behavior, an intelligent diagnosis system is used to give appropriate learning guidance to assist the learners in improving their study behaviors and grade online class participation for the instructor. The achievement of the learners’ final reports can also be predicted by the diagnosis system accurately. Our experimental results reveal that the proposed learning diagnosis system can efficiently help learners to expand their knowledge while surfing in cyberspace Web-based “theme-based learning” model.  相似文献   

15.
The performance of the learners in E-learning environments is greatly influenced by the nature of the posted E-learning contents. In such a scenario, the performance of the learners can be enhanced by posting the suitable E-learning contents to the learners based on their learning styles. Hence, it is very essential to have a clear knowledge about various learning styles in order to predict the learning styles of different learners in E-learning environments. However, predicting the learning styles needs complete knowledge about the learners past and present characteristics. Since the knowledge available about learners is uncertain, it can be resolved through the use of Fuzzy rules which can handle uncertainty effectively. The core objective of this survey paper is to outline the working of the existing learning style models and the metrics used to evaluate them. Based on the available models, this paper identifies Felder–Silverman learning style model as the suitable model for E-learning and suggests the use of Fuzzy rules to handle uncertainty in learning style prediction so that it can enhance the performance of the E-learning system.  相似文献   

16.
学习者知识模型是智能授导系统(ITS)中教学过程实现和策略实施的基础,然而由于判别学习者知识掌握程度的不确定性和学习者知识掌握水平的实时变化,构建能正确反映学习者知识掌握程度及其变化的知识模型十分困难。基于贝叶斯网络,以知识项为基本节点构建学习者知识模型的结构;引入问题节点,根据学习者的学习测试结果,采用Voting EM算法来对知识模型的参数进行在线学习和更新;同时,通过设置置信因子和更新时间标记来改进在线学习的效果。实验表明,模型能够较好地反映学习者知识掌握状况和快速适应学习者知识掌握水平的变化,有助于ITS更好地评价学习者学习效果。  相似文献   

17.
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.  相似文献   

18.
《Computers & Education》2005,44(2):97-113
The assessment of learners’ metacognitive knowledge level is crucial when developing computer-assisted language learning systems. Currently, many systems assess learners’ metacognitive knowledge level with pre-instructional questionnaires or metacognitive interviews. However, learners with limited language proficiency may be at a disadvantage in responding to verbal-report interview or questionnaire probes. The goal of this study is to present a neural network model that assesses automatically the learner’s metacognitive knowledge level by observing his/her online browsing behavior. The model is implemented through a multi-layer feed forward neural network. An experiment was conducted to examine the suitability of this model in different Web page structures. One hundred and forty-six college students were categorized into three groups according to three Web page structures: networked, hierarchical, and linear. The experiment results verified the suitability of the proposed model, and the MSEs of assessment of the three groups showed no significant differences with respect to the Web page structures.  相似文献   

19.
Electronic learning (e-learning) has become one of the widely used modes of pedagogy in higher education today due to the convenience and flexibility offered in comparison to traditional learning activities. Advancements in Information and Communication Technology have eased learner connectivity online and enabled access to an extensive range of learning materials on the World Wide Web. Post covid-19 pandemic, online learning has become the most essential and inevitable medium of learning in primary, secondary and higher education. In recent times, Massive Open Online Courses (MOOCs) have transformed the current education strategy by offering a technology-rich and flexible form of online learning. A key component to assess the learner’s progress and effectiveness of online teaching is the Multiple Choice Question (MCQ) assessment in most of the MOOC courses. Uncertainty exists on the reliability and validity of the assessment component as it raises a qualm whether the real knowledge acquisition level reflects upon the assessment score. This is due to the possibility of random and smart guesses, learners can attempt, as MCQ assessments are more vulnerable than essay type assessments. This paper presents the architecture, development, evaluation of the I-Quiz system, an intelligent assessment tool, which captures and analyses both the implicit and explicit non-verbal behaviour of learner and provides insights about the learner’s real knowledge acquisition level. The I-Quiz system uses an innovative way to analyse the learner non-verbal behaviour and trains the agent using machine learning techniques. The intelligent agent in the system evaluates and predicts the real knowledge acquisition level of learners. A total of 500 undergraduate engineering students were asked to attend an on-Screen MCQ assessment test using the I-Quiz system comprising 20 multiple choice questions related to advanced C programming. The non-verbal behaviour of the learner is recorded using a front-facing camera during the entire assessment period. The resultant dataset of non-verbal behaviour and question-answer scores is used to train the random forest classifier model to predict the real knowledge acquisition level of the learner. The trained model after hyperparameter tuning and cross validation achieved a normalized prediction accuracy of 85.68%.  相似文献   

20.
Although conventional student assessments are extremely convenient for calculating student scores, they do not conceptualize how students organize their knowledge. Therefore, teachers and students rarely understand how to improve their future learning progress. The limitations of conventional testing methods indicate the importance of accurately assessing and representing student knowledge structures. The personalized diagnosis and remedial learning system (PDRLS) proposed in this study enhances the effectiveness of the Pathfinder network by providing remedial learning paths for individual learners based on their knowledge structure. The sample was 145 students enrolled in introductory JAVA programming language courses at a Central Taiwan technology university. The experimental results demonstrate that learners who received personalized remedial learning guidance via PDRLS achieved improved learning performance, self-efficacy, and PDRLS use intention. The experimental results also indicated that students with lower knowledge level gain more benefits from the PDRLS than those with higher level of knowledge and that field dependence (FD) students obtain a greater benefit from PDRLS than field independence (FI) students do.  相似文献   

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