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1.
本文以个性化学习理论为指导,探索出能够适应学习者个性化学习的网络学习系统的基本模块及其功能,并通过综合运用数据库技术、数据挖掘技术以及人工智能技术来完善系统的功能。该系统将网络学习的特点与学习者的认知水平、认知风格和兴趣爱好等个性化因素紧密结合起来,为学习者营造一个个性化的网络学习环境。  相似文献   

2.
本文以个性化学习理论为指导,探索出能够适应学习者个性化学习的网络学习系统的基本模块及其功能,并通过综合运用数据库技术、数据挖掘技术以及人工智能技术来完善系统的功能。该系统将网络学习的特点与学习者的认知水平、认知风格和兴趣爱好等个性化因素紧密结合起来,为学习者营造一个个性化的网络学习环境。  相似文献   

3.
具有情感交互功能的智能E-learning系统   总被引:2,自引:0,他引:2  
分析了网络教学中普遍存在的情感缺失问题.将模糊情感技术应用于网络教学,构建了以Agent为核心的智能E-learning系统,实现了个性化教学和学习者情感识别;系统以模糊教学为基础构建在线学习评价系统,利用智能Agent及时捕捉学生在线学习时的情感信息和学习状态,并根据学习者的不同学习状态和学习评价结果及时做出情绪反应.  相似文献   

4.
本文以数据挖掘和机器学习技术为基础,提出了一种基于网络的智能学习系统的基本结构,该系统以学习者为中心,根据学习者的学习兴趣,为其提供智能的个性化学习平台;通过对系统关键技术的讨论,介绍和分析了学习者个性化学习兴趣模型建立和个性化学习的基本过程.  相似文献   

5.
针对学习资源使用者的特点和当前网络学习模型的不足,提出运用贝叶斯网络建立一种个性化学习者模型。基于用户决策方案指导资源库的建设,提出了一种新的学习资源推荐算法,使学习资源的呈现符合学习者认知发展水平和个性特征,改善资源库的组织结构,实现智能化、个性化的学习资源库推荐系统。实践证明,对于本系统所推荐的学习资源,学习者非常满意。  相似文献   

6.
《软件工程师》2018,(3):47-50
电子学习系统的快速发展为学习者在线学习提供了巨大的机会。然而,在线学习系统中太多的学习活动使个体学习者很难找到合适自己的学习活动,所以在线学习系统必须有能够提供个性化产品的推荐系统。本研究首先提出了一种模糊树状结构学习活动模型,然后结合基于知识和协同过滤推荐算法的优点提出了基于混合学习活动推荐方法的模糊树匹配方法。  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.
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   

9.
目前基于web的学习系统已经不能满足不同背景的学习者在不同时间、以不同目的、不同方式的学习需求。本文在利用以知识点为导向的数据挖掘技术,运用多种数据挖掘算法,构建一种智能化的个性化网络学习系统用以满足对学习者有针对性的教育需求,提高教学质量。  相似文献   

10.
基于本体的智能学习资源分配模型构建   总被引:1,自引:0,他引:1  
丁荣涛 《计算机科学》2008,35(11):293-294
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  相似文献   

11.
基于网络的个性化学习系统中学习者个性特征的提取算法   总被引:3,自引:0,他引:3  
杨卉  王陆 《计算机工程与应用》2003,39(25):179-181,201
研究了基于网络的个性化学习系统如何根据学习者的个性特征主动推送个性化学习信息服务,并就如何提高基于网络的个性化学习系统的运行效率,提出了一种有效提取学习者个性特征的算法。该算法主要分为三步:(1)捕获学习者个性特征;(2)统计分析学习者所在学习群体的群体特征和趋势;(3)根据学习者与他所在学习群体有显著差异的特征项和群体特征,推送个性化学习信息。  相似文献   

12.
This paper presents the details of a student model that enables an open learning environment to provide tailored feedback on a learner's exploration. Open learning environments have been shown to be beneficial for learners with appropriate learning styles and characteristics, but problematic for those who are not able to explore effectively. To address this problem, we have built a student model capable of detecting when the learner is having difficulty exploring and of providing the types of assessments that the environment needs to guide and improve the learner's exploration of the available material. The model, which uses Bayesian Networks, was built using an iterative design and evaluation process. We describe the details of this process, as it was used to both define the structure of the model and to provide its initial validation.  相似文献   

13.
Content Provisioning for Ubiquitous Learning   总被引:1,自引:0,他引:1  
In this article, the authors present an approach for context-aware and QoS-enabled learning content provisioning, one of the essential elements in ubiquitous learning. The essence of the system is recommending the right content, in the right form, to the right learner, based on a wide range of user context information and QoS requirements. To facilitate knowledge interoperability and sharing, they modeled the learner context, content knowledge, and domain knowledge using ontologies. They first propose a knowledge-based semantic recommendation method to acquire the content the user really wants and needs to learn. Then, a fuzzy logic-based decision-making strategy and an adaptive QoS mapping mechanism determine the appropriate presentation according to user's QoS requirements and device/network capability.  相似文献   

14.
This work aims to present and evaluate a Fuzzy-Case Based Reasoning Diagnosis system of Historical Text Comprehension. The synergism of fuzzy logic and case based reasoning techniques handles the uncertainty in the acquisition of human expert's knowledge regarding learner's observable behaviour and integrates the right balance between expert's knowledge described in the form of fuzzy sets and previous experiences documented in the form of cases. The formative evaluation focused on the comparison of the system's performance to the performance of human experts concerning the diagnosis accuracy. The system was also evaluated for its behaviour when using two different historical texts. Empirical evaluation conducted with human experts and real students indicated the need for revision of the diagnosis model. The evaluation results are encouraging for the system's educational impact on learners and for future work concerning an intelligent educational system for individualized learning.  相似文献   

15.
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.  相似文献   

16.
《Knowledge》2007,20(2):177-185
There is an extensive body of work on Intelligent Tutoring Systems: computer environments for education, teaching and training that adapt to the needs of the individual learner. Work on personalisation and adaptivity has included research into allowing the student user to enhance the system’s adaptivity by improving the accuracy of the underlying learner model. Open Learner Modelling, where the system’s model of the user’s knowledge is revealed to the user, has been proposed to support student reflection on their learning. Increased accuracy of the learner model can be obtained by the student and system jointly negotiating the learner model. We present the initial investigations into a system to allow people to negotiate the model of their understanding of a topic in natural language. This paper discusses the development and capabilities of both conversational agents (or chatbots) and Intelligent Tutoring Systems, in particular Open Learner Modelling. We describe a Wizard-of-Oz experiment to investigate the feasibility of using a chatbot to support negotiation, and conclude that a fusion of the two fields can lead to developing negotiation techniques for chatbots and the enhancement of the Open Learner Model. This technology, if successful, could have widespread application in schools, universities and other training scenarios.  相似文献   

17.
We examine the performance of a fuzzy genetics-based machine learning method for multidimensional pattern classification problems with continuous attributes. In our method, each fuzzy if-then rule is handled as an individual, and a fitness value is assigned to each rule. Thus, our method can be viewed as a classifier system. In this paper, we first describe fuzzy if-then rules and fuzzy reasoning for pattern classification problems. Then we explain a genetics-based machine learning method that automatically generates fuzzy if-then rules for pattern classification problems from numerical data. Because our method uses linguistic values with fixed membership functions as antecedent fuzzy sets, a linguistic interpretation of each fuzzy if-then rule is easily obtained. The fixed membership functions also lead to a simple implementation of our method as a computer program. The simplicity of implementation and the linguistic interpretation of the generated fuzzy if-then rules are the main characteristic features of our method. The performance of our method is evaluated by computer simulations on some well-known test problems. While our method involves no tuning mechanism of membership functions, it works very well in comparison with other classification methods such as nonfuzzy machine learning techniques and neural networks.  相似文献   

18.
In this paper, a Feature-Extraction Neuron-Fuzzy Classification Model (FENFCM) is proposed that enables the extraction of feature variables and provides the classification results. The proposed classification model synergistically integrates a standard fuzzy inference system and a neural network with supervised learning. The FENFCM automatically generates the fuzzy rules from the numerical data and triangular functions that are used as membership functions both in the feature extraction unit and in the inference unit. To adapt the proposed FENFCM, two modificatory algorithms are applied. First, we utilize Evolutionary Programming (EP) to determine the distribution of fuzzy sets for each feature variable of the feature extraction unit. Second, the Weight Revised Algorithm (WRA) is used to regulate the weight grade of the principal output node of the inference unit. Finally, the proposed FENFCM is validated using two benchmark data sets: the Wine database and the Iris database. Computer simulation results demonstrate that the proposed classification model can provide a sufficiently high classification rate in comparison with that of other models proposed in the literature.  相似文献   

19.
针对当前e-learning系统中存在着资源利用效率低和学习内容单一、个性化不足等问题,设计一个基于本体的web挖掘的个性化e-learning系统,通过应用web挖掘和本体技术,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。实验表明本体技术能明显改善挖掘效果,提高学习资源库的管理效率,有效促进学生的网络学习,满足学生个性化学习的需求,为系统的决策分析提供了智能的辅助手段。  相似文献   

20.
Abstract: Machine learning can extract desired knowledge from training examples and ease the development bottleneck in building expert systems. Most learning approaches derive rules from complete and incomplete data sets. If attribute values are known as possibility distributions on the domain of the attributes, the system is called an incomplete fuzzy information system. Learning from incomplete fuzzy data sets is usually more difficult than learning from complete data sets and incomplete data sets. In this paper, we deal with the problem of producing a set of certain and possible rules from incomplete fuzzy data sets based on rough sets. The notions of lower and upper generalized fuzzy rough approximations are introduced. By using the fuzzy rough upper approximation operator, we transform each fuzzy subset of the domain of every attribute in an incomplete fuzzy information system into a fuzzy subset of the universe, from which fuzzy similarity neighbourhoods of objects in the system are derived. The fuzzy lower and upper approximations for any subset of the universe are then calculated and the knowledge hidden in the information system is unravelled and expressed in the form of decision rules.  相似文献   

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